UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 107 SECTION 4 Heat transfer, ENERGY GENERATION & CONVERSION
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 107
SECTION 4
Heat transfer,
ENERGY GENERATION
& CONVERSION
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 108
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 109
Proceedings of the Fourteenth Annual Early Career Technical Conference The University of Alabama, Birmingham ECTC 2015
November 7, 2015 - Birmingham, Alabama USA
DESIGN AND STUDY OF SOLAR CONCENTRATOR WITH FLAT HEXAGONAL PETALS
Ketan Solanki, Mohamed Nizamuddin Shaik, Vijaya Krishna Teja Bangi Department of Mechanical Engineering
Lamar University Beaumont, TX, USA
Dr. Ramesh K. Guduru
Department of Mechanical Engineering Lamar University
Beaumont, TX, USA
Dr. Kendrick T. Aung
Department of Mechanical Engineering Lamar University
Beaumont, TX, USA
ABSTRACT
The rapidly increasing demand for energy along with
depletion of global fossil fuel reserves makes renewable energy
a very attractive long term solution. Among various renewable
technologies, the harnessing of solar energy for generation of
electricity and heating applications is certainly one of the
foremost and most easily viable solutions. There are different
approaches in practice for collection as well as conversion of
solar energy into many useful applications. Solar flat collectors,
solar troughs, and tube collectors are conventionally used for
several engineering and household applications. Along these
lines, we have conducted a new feasibility study on use of
Aluminum (Al) kitchen foil and hexagonal petals for a solar
concentrator with an aim to reduce the costs and complexity of
solar dish fabrication. Here, we present a design and
experimental study of a solar dish concentrator built using
Aluminum (Al) foil wrapped around flat hexagonal cardboard
petals as a medium of reflection. The solar concentrator was
designed based on dish geometry for high collection and
concentration efficiency in addition to cost effectiveness.
However the arrangement of flat petals into a dish geometry
was dictated by the angle () between the petals as well as the
petal size (a) while limiting the amount of area of reflection on
the dish. We made several 3D designs using CATIA V5 with
variations in ‘’ values and petal sizes in order to determine a
compatible arrangement of the flat petals. Among various petal
sizes and ‘’ angles, we chose to build a prototype with petal
size of 25mm for an angle () of 30
in our experimental
investigation, using the CATIA designs in order to have more
area for reflection. This experimental setup yielded a maximum
temperature rise of 1200C under an average solar radiation of
1000 W/m2. This is the first work ever to report a temperature
rise of 1200C with use of aluminum kitchen foil and hexagonal
petals on a solar concentrator.
INTRODUCTION The most abundant energy source for the earth is the Sun.
It provides around 3.8 x 1020
MW of energy, which is
equivalent to a power of 63 MW/m2. However, due to reflection
and absorption by the atmosphere, only 174 Peta Watt is
reached [1]. If this energy is totally captured for an hour period,
it can easily supply the energy needed for the whole world for
more than a year.
Solar energy can be utilized in many ways, and currently it
is being used for water heating and generation of electricity in
the households. The worldwide demand for electricity has been
increasing by 5% every year [2], and therefore relying on
nonrenewable (e.g. fossil fuel) sources will eventually hamper
the human life with depletion of resources in the long term.
However, use of renewable energy sources with cost efficient
technologies will not only boost the effectiveness of solar
energy consumption but will also reduce greenhouse emissions
and help in sustainable growth.
Solar concentrators are certainly one of the most attractive
technologies in the household applications, with a good room
for improvement in efficiency of operation while increasing
their affordability, in contrast to the solar cells. Among the
different types of solar concentrators [3], the parabolic dish is
the most efficient system for solar energy concentration
because of its high collection and concentration effectiveness.
However, the area of a solar concentrator’s field is dictated by
its size and the amount of energy collected. Usually, dish type
solar concentrators are made using curved mirrors [4] for solar
cookers and to generate electricity [5]; curvy hexagonal petals
are used for reflector dishes in telecommunications [6]. But,
these are not affordable to the common man due to expensive
manufacturing costs associated with complex fabrication
approaches. Thus, there is certainly a huge requirement for the
development of inexpensive and efficient technologies that can
capture the solar energy for common applications with greater
affordability.
Keeping those above discussed goals in mind, here we
develop a solar concentrator based on dish geometry using flat
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 110
hexagonal petals. Initially, we chose to make CATIA models
with hexagonal petals of different sizes (a) and different angles
() between the petals as shown in Fig. 1. After analyzing the
petal arrangement and the area of reflection for a combination
of different petal sizes and the angles between them, a
prototype was developed and subsequently implemented for
experimental investigation. These studies are unique in terms of
using inexpensive materials, such as Al kitchen foil and flat
cardboard petals, to develop a solar dish concentrator. The
experimental observations yielded a high temperature rise of
120 0C.
Figure 1. Schematic for a solar concentrator dish showing dish diameter, petal size (a) and the angle between the
petals ()
METHODS The quantity of solar energy reaching the Earth's surface
averages about 1,000 W/m2 under clear skies, depending upon
weather conditions, location and orientation. Solar collectors
are typically used in residential and commercial buildings for
space/water heating.
In our approach, the following parameters were considered
for the design of the parabolic dish
1. Area of the dish.
2. Reflectivity of the material.
3. Hexagonal petal dimension (a).
4. Angle between the petals ().
5. Tolerance/gap between the petals.
The area of a dish is primarily controlled by the
dimensions of the petals, the angle between adjacent petals, and
the tolerance maintained between the petals. In order to obtain
optimum reflection of the solar radiation, the surface of the dish
is supposed to be as smooth as possible, and this can be
achieved using petals of smaller dimensions. Also, as the angle
() between the petals grows small, the dish area increases in
size, and vice versa.
The arrangement of petals in a dish is dictated by the
tolerance or gap maintained between them, and it will
eventually make the petals run in to each other, causing a force
fit or termination of the dish symmetry, depending on the petal
size. However, the gap between the petals can be varied
accordingly to achieve the best possible fit. A smaller gap leads
to early termination, while the larger gaps compromise the
effective surface area of reflection, depending on the
dimensions of the hexagonal petals. Thus a tolerance
maintained between the petals will play a key role in the total
reflective area of the dish.
3D Modeling We used CATIA V5 to design our solar dish models with
flat hexagonal petals. The strategy used in our designs was to
achieve a maximum reflective surface area. The flat hexagonal
petals were placed starting from the center of the dish, and 1
mm tolerance was chosen between the petals. As the petals
were arranged from the centre of the dish, the gap between
them gradually reduced. Finally, the stacking of petals failed
when there was no gap between them.
Experimental approach In a solar concentrator all the sunrays are reflected on to a
defined focal point to concentrate the solar energy. In our
experimental studies, the dish was fabricated with incoming
light rays parallel to the dish's axis to reflect onto an absorber
that was placed at the focal point. The dish was aligned with its
axis pointing toward the sun, allowing almost all of the
incoming radiation to be reflected onto the focal point.
Radiation losses in such collectors could occur due to scattering
of the light in to a wide range of angles, if all the petals are not
focused onto a single spot. Also, the type of reflective materials
used, such as aluminum foil, mirrors or any reflective painting,
will control the extent of light reflection; Al kitchen foil has
reflectivity around 85% [7].
RESULTS AND DISCUSSION
3D Design In our design process, two distinct design approaches were
followed. In the first approach, the angle between the petals ()
was maintained constant while their size (a) was varied; this
approach enabled us to fix petals of different sizes in a dish
with fixed dimension i.e., fixed diameter and angle ‘’. In the
second approach, the petal size (a) was fixed, and the angle ‘’
between the petals was varied while keeping the diameter of the
dish constant. This helped us to determine the maximum
possible reflective area of the dish for different ‘’ values.
Following the first approach, we created models with
different petal sizes (a) for a given angle () of 100. Table 1
shows the variation in reflective surface area on the dish as a
function of petal size.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 111
Table 1. Variation of petal size and reflective surface area
for a fixed angle () of 100 between the adjacent petals
Sr. No
Size of Petal (mm)
Angle (Degree)
()
Number of stacking layers
Reflective Surface Area
(mm2)
1 30 10 1 16366
2 25 10 2 30856
3 20 10 2 19741
4 15 10 2 11115
5 10 10 4 9620
Figure 2 shows the stacking of petals with a size of 30 mm
for an angle () of 100 between the petals, which failed at the
second layer of stacking, causing them to run into each other, as
shown by the arrow
Figure 2. Arrangement of hexagonal petals in to a dish
with a petal size (a) of 30 mm and an angle () of 100
between adjacent petals
In a similar way, the petals arranged in a dish for a petal
size of 5 mm are shown in Fig. 3. However, this configuration
failed at the fifth layer of stacking. The gap of 1 mm between
the petals in the initial iteration enabled for the accommodation
of improper symmetry as the stacking continued. However, it
failed after the fifth layer of stacking, and thus proved the
important role of the gap between the petals in achieving a
maximum surface area, while facilitating increased numbers of
layers of petal stacking before they crash.
For every petal size, the total reflective area on the dish
varied, depending on the number of layers of stacking. Figure 4
shows the variation of total reflective area on the dish with
changing petal size. Based on these designs, and from the Fig.
4, it is clear that the highest reflective area was achieved for a
petal size of 25 mm.
In the second approach, we kept the size (a) of the
hexagonal petal constant, and varied the angle between adjacent
petals (θ) so that different sizes of concentrators with varying
diameters could be modeled. However, choosing an optimum
angle was very tricky. If the angle was larger, the focal point
would be close to the dish and thereby the reflected area would
decrease. The problem with this configuration was that the
absorber placed in the focal point could block a considerable
amount of radiation and would compromise the efficiency of
the design. On the other hand, a smaller angle would result in a
focal point that was farther from the dish, which also would
have an effect on the solar concentration of the system. This
will be discussed momentarily in regards to the blowing winds
as well as scattering of light into a wider angle range.
Figure 3. Arrangement of hexagonal petals in to a dish
with a petal size (a) of 5 mm and an angle () of 100
between the adjacent petals
Figure 4. Variation of reflective area on dish with different
petal sizes for a fixed angle () between the petals
Figures 5 and 6 show the configuration of 10 mm petals for
angles () of 200 and 3
0, respectively. In Fig. 5 the arrangement
of petals crashed in the second layer of stacking because of a
very high angle of 200 between the petals. Using the smaller
angle of 30 resulted in a maximum reflective surface area of
86060 mm2. However, this design could present challenges for
fabrication, as the petals must be manually fixed into the dish
geometry to focus on a single point, and their smaller size
presents problems with scattering light into a wider angle
range.
16366
30856
19741 15998
11115
9620 3965
0
10000
20000
30000
40000
0 10 20 30 40 Su
rfa
ce
Are
a (
mm
2)
Size of Petal (mm)
Size of Petal (cm) Vs Surface Area (mm2)
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 112
Table 2. Variation of the angle between the adjacent
petals () for a fixed petal size
Sr. No
Size of Petal (mm)
(a)
Angle (Degree)
()
Number of stacking layers
Reflective surface area
(mm2)
1 10 20 2 4940
2 10 18 2 4940
3 10 15 3 9620
4 10 12 3 9620
5 10 10 4 15860
6 10 5 7 33020
7 10 3 10 86060
Figure 5. Arrangement of hexagonal petals in to a dish
with a petal size (a) of 10 mm and an angle () of 200
between the adjacent petals
Figure 6. Arrangement of hexagonal petals in to a dish
with a petal size (a) of 10 mm and an angle () of 30
between the adjacent petals
Figure 7 shows the variation of total reflective surface area
for a 10 mm petal size on the dish for different angles between
the petals, which certainly proves that a smaller angle would
result in a higher reflective area.
Figure 7. Variation of dish area with different angles () between the petals for a fixed petal size (a= 10 mm).
While investigating for the optimum dimensions of the
hexagonal petals and the angles between them, it was observed
that smaller petal dimension with smaller angles resulted in
much smoother concentrator surface with more layers of petal
stacking. The design data from the Tables 1 and 2 prove this
notion. In practical terms, it is difficult to fabricate smaller
hexagonal petals. Also, a smaller angle between the petals
implies a larger dish area, and the focal point would be farther
away from the dish. In such cases, the solar energy
concentrated will be comparatively less because of scattering of
the reflected radiation and also because of the convective heat
losses from the absorber surface to the surrounding
environment. Therefore, we chose to further investigate our
designs with different petal sizes for an angle of 3º to achieve a
maximum reflecting area.
Table 3 shows the highest reflective area for a petal size of
25 mm with an angle of 30
between the petals. Based on this
design we conducted our experimental investigation.
Table 3. Variation of petal size for a fixed angle 30
between the adjacent petals ()
Sr. No
Size of Petal (mm)
Angle (Degree)
Number of
iterations achieved
Reflective Surface
Area (mm2)
1 30 3 3 86506
2 25 3 5 147784
3 20 3 5 94549
4 15 3 6 74295
5 10 3 10 86060
FABRICATION OF CARDBOARD DISH
Materials used- Cardboard of 2 mm thick, Al kitchen foil
and glue/adhesive tape.
Design criterion-From the CATIA designs, a maximum
reflective area was achieved when the petal size was 25 mm for
an angle of 30 between adjacent petals. Hence, we chose to
fabricate the dish of the same dimensions/geometry.
4940 4940
9620 9620
15860
33020
86060
0
20000
40000
60000
80000
100000
0 10 20 30 Su
rfa
ce
Are
a (
mm
2)
Angle (degree)
Angle (degree) Vs Surface Area (mm2)
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 113
Fabrication of dish- Hexagonal petals of 25 mm were cut,
and Al kitchen foil was glued on to these petals to prevent
wrinkles. The petal was then allowed to set for 2 hours before
being fixed on to the dish. The frame work for the dish was
made using cardboard sheets as shown in figure 8. In this
design, the focal point was set at 250 mm with a diameter of
200 mm.
Figure 8. Supporting base structure for the dish.
The base for the hexagonal petals was developed by
sticking duct tape to the supporting structure shown in Figure 8.
Figures 9 and 10 show the 3D design and construction of a
prototype, respectively, for a petal size of 25 mm and 30 angle.
Figure 9. Arrangement of hexagonal petals in to a dish
with a petal size (a) of 25 mm and an angle () of 30
between the adjacent petals
Figure 10. Arrangement of hexagonal petals on a dish
with a petal size (a) of 10 mm and an angle () of 30
between the adjacent petals
TESTING OF SOLAR CONCENTRATOR The prototype concentrator was exposed to incoming sun
radiation on a sunny day with solar intensity around 1000
W/m2, and a maximum temperature of 120
0C was obtained.
See Figure 11. The temperature readings at the focal point were
taken at regular intervals of two minutes, and the experiment
was continued for half an hour. These experimental
investigations verify our modeled design in terms of stacking of
petals, and thereby support the design approach. The
experimental data yielded a reasonable temperature rise while
utilizing flat petals in to a dish geometry.
Figure 11. Variation of solar radiation and temperature at
focal point with respect to time (a=25 mm and =30).
EXPERIMENTAL OBSERVATIONS The goal of this project was to study the feasibility of
developing a flat hexagonal petal based dish for solar
concentrator applications, using inexpensive resources
available to us. Our study was done using Al ktichen foil as a
reflective medium. Preliminary experimental observations with
a prototype showed very promising results with possibility to
achieve temperatures up to 120 0C using mere Aluminum foil
reflectors (85%) [7], in contrast to the expensive and highly
reflective curved mirrors (95%) [8].
While testing our prototype, various un-controllable
problems were faced, such as blowing cold air and frequent
blockage of sunlight by clouds from time to time causing
fluctuations in the radiation collection and thereby resultant
temperatures. A mechanism for accurately tracking the position
of the sun has yet to be incorporated in to the system.
To avoid such uncontrollable problems, further research in
this direction can be done by use of glass dome over the solar
concentrator to avoid convective heat losses due to wind. A
mechanism for tracking the Sun can be developed and
incorporated into the system to ensure a constant input of solar
radiation with respect to the time.
CONCLUSIONS We conclude that the methodology and design approach
followed in the present work can be implemented to develop
inexpensive solar concentrators using regular Al foil and
50
70
90
110
130
1000
1010
1020
1030
1040
1050
0 10 20 30 Te
mp
rea
ture
(0C
)
Inte
nsity (
Wa
tt/m
2)
Time (min)
Intensity Of Radiation Tempreature
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 114
cardboard petals. These studies shed light on enhancing
renewable energy technologies and design opportunities for
large scale applications. However, there are some limitations of
this technology with direct exposure of the radiation collector
to the winds and also the pliability of the framework and petals,
materials, designs and fabrication methods. The results prove to
be promising to obtain high temperatures with very low cost of
manufacturing, making this proof of concept design successful.
REFERENCES [1] Kreith F, Kreider JF., 1978, “Principles of solar
engineering,” New York: McGraw-Hill.
[2] World watch Institute, 2009, State of the World – Into a
Warming World.
[3] Soteris A. Kalogirou, 2004, “Solar thermal collectors and
applications,” Progress in Energy and Combustion Science 30,
pp. 231–295.
[3] A. R. El Ouederni1, A.W. Dahmani2, F. Askri3, M. Ben
Salah3 and S. Ben Nasrallah4, 2009, “Experimental study of a
parabolic solar concentrator,” Revue des Energies
Renouvelables Vol. 12 No. 3, pp. 395 – 404.
[4] C. Lertsatitthanakorn1, J. Jamradloedluk1 and M.
Rungsiyopas2, 2014, “Electricity generation from a solar
parabolic concentrator coupled to a thermoelectric module,”
2013 International Conference on Alternative Energy in
Developing Countries and Emerging Economies, Energy
Procedia 52 , pp. 150 – 158.
[5] Lifang Li1, Steven Dubowsky, 2011, “A New Design
Approach for Solar Concentrating Parabolic Dish Based on
Optimized Flexible Petals,” Mechanism and Machine Theory
46, pp. 1536–1548.
[6] Hanlon, J., 1992, “1st ed. Handbook of Package
Engineering”, ISBN 0-87762-924-2. Chapter 3 Films and Foils.
http://scienceworld.scholastic.com/Physics-
News/2012/12/optics.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 115
Proceedings of the Fifteenth Annual Early Career Technical Conference The University of Alabama, Birmingham ECTC 2015
November 7, 2015 - Birmingham, Alabama USA
APPLICATION OF A SEQUENCE OF DESIGN METHODOLOGIES TO THE PROBLEM OF TRANSPORTING WARM PARTICLES IN PARTICLE HEATING RECEIVER SOLAR ENERGY
SYSTEMS
Kenzo Repole, Sheldon Jeter
Georgia Institute of Technology Atlanta, GA, USA
ABSTRACT The central receiver power tower (CRPT) with a particle
heating receiver (PHR) is a form of concentrating solar power
(CSP) system which has the ability to achieve high efficiency at
low cost and to readily incorporate thermal energy storage
(TES).
A critical component in such a PHR system is the particle
lift system, which must transport the particulate from the lower
temperature TES bin back to the PHR. This particle lift system
is a critical, innovative solution drawing from many industries,
such as the mining industry, yet it is being qualified through a
special sequence of design methodologies in order to develop a
viable design neutral solution.
INTRODUCTION As countries around the world move towards increased use
of renewable energy and to find ways of climate change
mitigation, governments have started to institute stricter
regulations to reduce the use of fossil fuel and to encourage
renewable energy sources. One such set of regulations has been
issued by the Environmental Protection Agency in the United
States [1].
These types of policies mean that renewable energy will
play a greater role in delivering electricity via the power grids.
However, to ensure dependable base loads either a fossil fuel
plant must be used to generate electricity or some form of
storage must be used to access energy from renewable sources
when they are unavailable.
These forms of energy storage can range from chemical
batteries, flywheels, compressed air, capacitors, or thermal
energy storage [2].
One form of solar energy power is the concentrating solar
power (CSP) system. A particular form of CSP uses particles to
capture the solar energy then convert it to electricity. This is
known as a particle heating receiver (PHR) system. This system
has the ability to incorporate economical thermal energy
storage (TES).
In a PHR system, solar energy is concentrated from a field
of heliostats onto a falling curtain or layer of particles at the
receiver aperture of a power tower. [3]
The particles can then be directed towards a heat exchanger
in a power generation system or to storage for later use when
the utility load demand is greater or as a means to compensate
for the solar variation experienced during the day.
The advantage that PHR systems have over other forms of
CSP is the ability use low cost materials to store sensible
energy over a longer period of time. Storage allows off-sun and
night time generation, and extended use of the energy plant
reduces the levelized cost of energy (LCOE) [4].
As the need for greater capacity CSP, especially PHR
systems, grows so will the need to find an effective delivery
system to recycle the working particles to the PHR at faster
rates and in larger amounts.
Larger capacity PHR systems with TES means the power
tower will increase in width and especially in height in order to
store the high temperature particles used during power
generation or as storage for demand based power generation.
Currently, there are many different candidate methods of
carrying fine particles up to elevations of the PHR. However, to
increase the capacity of the power plant and its efficiency, the
particles entering the PHR need to be at higher temperatures
ranging from 300°C (572°F) up to 600°C (1112°F). At such
temperatures some conventional methods of delivering large
amounts of working particles are not viable, since the operating
environment is outside the operating range for some delivery
mechanisms. [5]
This paper outlines the processes used to develop candidate
designs, and the further integration of the favored conceptual
design is discussed. The process is the application of a different
set of design methodologies used in a special sequence in order
to elicit a feasible design solution from vague requirements. It
is felt that this special sequence could be used in similar
situations.
This design is especially demanding, since it is outside
what is currently used in commercial or industrial applications.
The final solution would need high reliability in elevated
temperatures, where a lift of tons of fine particles over
hundreds of meters is needed.
The final solution is not refining an existing design but
finding a new solution for an application never used before.
Just as importantly, the final solution is not meant to be
mass produced and but to be efficient and cost effective where
many iterations and extended research are not feasible.
DESIGN METHODOLOGIES There exist many design methodologies for concept
generation. However, they can be mostly grouped into three
types. These are the conventional methods, intuitive methods
and the discursive or systematic methods [6].
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 116
Conventional methods encompass such things as patent
and literature reviews, analysis of existing systems, analyzing
natural systems or using analogies to formulate a design [7].
These processes are best suited for product improvement and
mass production products.
Intuitive methods include processes like brainstorming [8],
mind mapping [9], or the 635 Method [10]. These processes are
better suited for innovative or inventive products.
The systematic methods are design-neutral methods, which
follow a step by step sequence in order to come to a best
solution. Such processes are like the Morphological chart
method [11] or Axiomatic design [12]. These processes are
meant to have a design neutral analysis giving allowance for
intuitive design or conventional methods.
THE PUGH METHOD When designing solutions for novel needs or unique use, it
is helpful to use a best practice design methodology. Such a
methodology helps to account for important criteria, which
could be overlooked, and encourages a design neutral process
to develop a fully comprehensive design.
One of the best practices in design methodology is the so-
called Pugh concept selection [13]. After the customer needs
have been compiled and the initial round of function
requirements and selection criteria developed, different
concepts are generated based on this information. In the Pugh
concept selection process, one concept generated is considered
to be the reference design, and all other designs are compared
using the equally-weighted selection criteria. The scores are
then used to rank each design. The top ranked designs can be
compared a second time using unevenly weighted criteria to
focus on the most suited design solution. Once the highest
ranked conceptual design solution is selected, it can then be
developed into a more detailed design.
AXIOMATIC METHOD A best practice design methodology to evaluate concepts is
the axiomatic design method [27]. In this process, functional
requirements (FR) in the physical domain are developed from
customer needs and then mapped to design parameters (DP) in
the design domain. The DP is the feature required to meet the
FR.
Once this mapping is done, the design parameters become
the basis for development of more detailed FR, and this next
level of FR becomes the basis for the next set of more detailed
DP. This process is repeated until the functional requirements
and design parameters have reached a sufficiently detailed
level.
After this definition is achieved, the Independence Axiom
and Information Axiom [27] can be applied to the FR and DP in
order to determine if the design is a suitable design in the sense
of being robust with respect to design interactions. For the
independence axiom, this is normally illustrated in a matrix
form as {FR} = [ A ]{DP} where the design matrix is [ A ]. The
ideal design in this sense would result in a design matrix that is
diagonal. This means that each FR is independent of the other
FRs. Such a design is called an uncoupled design. The design
can easily be optimized since each FR can be modified
independently of the other FR.
If the design matrix closely resembles a triangular matrix
this design is considered a decoupled design. Most designs are
decoupled, since many of the different parts of the design
architecture depend on each other at interfaces.
The least desirable design is a coupled design. This would
result in a full (or nearly full) design matrix. This would
indicate that every FR is linked to all other FR. This design
would be very difficult to optimize, and it would be difficult to
change any FR without having to modify all other FRs.
DESIGN FOR MANUFACTURING Design for manufacturing is a process whereby a product
design is adjusted in order to increase the ease with which it
can be manufactured and assembled. Such aspects of the design
would be to use reduced cost materials, standardized parts, and
to use dimensions compatible with available transportation
methods or to set the shape of components for ease of
manufacturability or assembly. Also, a modular design would
be adopted with the reduction in the number of parts in order to
lower cost and increase the efficiency of assembly [14].
CUSTOMER NEEDS As part of the US Department of Energy’s the Sun Shot
Program's project for the "Development of a High Temperature
Falling Particle Receiver" a means for transporting particles to
the top of the CSP tower was needed. However, the exact
specification were not given; only that the lift must maintain
particles being transported close to 300°C (572°F) and must
deliver the particles in a manner to ensure a constant mass flow
rate with respect to thermal energy requirements. As the design
process evolved more functional requirements and design
specifications were developed.
CONCEPT GENERATION AND SCREENING Due to time and budgetary constraints, it was decided to
use a sequence of different design methodologies in order to
arrive at a viable design solution. Since the exact customer
needs were not specified, axiomatic design elicitation was used
to develop the first level of functional requirements.
This first level of functional requirements was then used as
the initial selection criterion during the conventional method of
patent and literature review. This review was conducted to
understand the current solutions for transporting high
temperature particles on an industrial scale within the area of
the first level of functional requirements.
After this the qualified options where screened and
compared to a reference solution which was used in a previous
prototype. The most viable option was then put through the
intuitive design process of mind mapping in order to generate
any other requirements which were not readily apparent.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 117
FUNCTIONAL REQUIREMENTS For our initial design, a particle transport solution was
needed to deliver particulates up to a height of 138 m of a 60
MWth solar power tower, after which we applied our findings
to a proposed commercial scale 100 MWth solar power tower.
The height of the lift is determined by the size of the high
temperature bin used to store the particulate for use during the
day and the assumed 9 hour off-sun period. Based on this
information and other constraints, the first level of FR was
developed and is shown in Table 1.
Table 1 First Level Functional Requirements
FR# FR Description
FR01 Be able to transport vertically large amount of small
particulates
FR02 Be able to operate in a shaft temperature environment
between 150 to 200°C
FR03 Be able to operate with minimal heat loss
FR04 Be able to operate with minimum particulate spillage.
FR05 Be able to resist wear regardless of size or hardness of
particles
FR06 Be able to have an overall energy efficiency of 75%
FR07 Be able to have dimensions that allow transportation
on rail and shipping containers
FR08 Be able to meet safety factor minimum for industry
related standard.
It was then decided to use these FR as selection criteria for
the initial round of concept generation and selection using the
Pugh concept selection.
PARTICLE TRANSPORTATION OPTIONS Some of the options that are available to meet this
challenge are bucket elevators, Olds Elevators, conveyer belts,
and particle skips similar to those used in the mining industry.
Suitable bucket elevators may have the ability operate at
high temperatures, greater than 200°C (392°F) [15]. However,
this technology would typically require the shaft for particle
transport to be kept at the same temperature as the particles for
a reasonable heat loss. Moreover, this system would likely
experience a high spillage rate during operation.
Olds elevators (OLDS) [18] have the ability to deliver the
working particles in a continuous flow and high temperature.
However, as the height of the tower increases the cost of the
OLDS increases significantly due to the nature of its design.
Nevertheless, the OLDS was used as the reference solution
since it is used in the known PHR prototypes [19].
Conveyer belts have little spillage but are difficult to
integrate into a tower and are not suited to convey high
temperature particles without huge heat loss. Application of the
Pugh method as seen in Table 2 shows that the most suitable
design option that can address the current and future needs of
larger capacity PHR that maintain high thermal efficiency and
low exergy degradation is the particle lift.
SKIP LIFT ALTERNATIVES
Particle lifts similar to those used in the mining industry
come in different forms. The main forms currently used in the
mining industry are Bottom Dump skips, Front Dump skips,
Kimberly Skips (KS), and Arc Gate skips.
The main tradeoffs between the different skips types are
(1) the extra height required during operation, (2) ease of
operation at high temperature, (3) spillage during use, (4)
maintenance requirements, and (5) simplicity of effective
thermal insulation.
Bottom Dump skips are charged from the top and
discharged by a trap door forming part of the bottom of the
skip. It does not require a large amount of extra height for its
operation in comparison to the other types of skips. This skip
design is light weight and rugged; but due to the fine size, 250
nm, of the particles used in the PHR, spillage may be large
during the transport and discharge of the particles.
Front Dump skips, are charged from the top and discharged
through a gate forming part of the lower end of the front side of
the skip. Such skips are reportedly able to carry large volumes
of particles and to put the least amount of stress on the head
frame [20]. However, the spillage rate may still be high in
comparison to other types of skips.
Arc Gate skips are considered to be safe and rugged [20].
They are charged from the side and discharged through an arc
gate on the side. As with the bottom dump skip, the Arc Gate
skip may experience large amounts of spillage during charge
and discharge. In addition to this skip has many moving parts
implying an increased risk of failure under high temperature
and extreme environments that would be experienced in
transporting the working particles in the PHR.
Kimberly skips, are charged and discharged from the top of
the skip. The particles are loaded into the skip from the top. The
skip then travels in this configuration until it reaches its
discharge location. As it reaches the discharge location, a set of
scroll wheels on the skip engages scroll guides on the shaft
walls. These wheels guide the skip through the dump zone and
allow the skip to rotate to about 120° from its vertical position.
This action discharges the particles from the skip. The KS is
expected to have the lowest initial cost, the lowest maintenance
cost and highest service life in comparison to the other skip
types [20]. KS have the lowest amount of spillage occurrence
during use. However, KS requires larger headroom and width
clearance than any other skip design. They also exert the largest
amount of stress on the head frame [21], [22].
In our development, the Front dump and Kimberly skips
appeared to be most promising, so scale models were
developed for qualitative comparison. Operation of the scale
models indicated that the KS would be easiest to effectively
insulate and operate at the expected high temperatures.
Another important subsystem in the particle lift is the hoist.
There are two main types of hoist [22]. The drum type has a
single rope or multiple ropes wound on a drum and controlled
by an AC drive motor. The other main type is the Koepe
friction hoist. In this design, two skips or one skip and a
counter balance are operated by ropes passing over a drum, and
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 118
the friction between the ropes and the drum controls the motion
of the skip.
The Koepe hoist is reported to be the most common hoist
system used in the mining industry today. It is based on using
the friction between the drum and the wire rope to enable the
drum to drive the skip operation. Despite its attractive low cost
and wide use, it was not initially selected in this project due to
concerns about reliable friction-dependent high temperature
operation, the reason being there is a possibility that the heat
generated from the friction in addition the high temperature of
the shaft could greatly reduce the life of the wire rope in use.
Therefore for this design a Blair [22] drum hoist was
selected. One of the advantages of the Blair drum is its ability
to run the skips independently of each other in cases of
emergency, thus giving the system a contingent means to
continue operating if one skip becomes nonfunctional.
REFINING PARTICLE LIFT SELECTION After selecting the general particle lift concept by the Pugh
method and generating a more detailed level of DP by mind
mapping as seen in Figure 1, the design problem was further
analyzed using the Independence Axiom. For this analysis the
more detailed or so-called “drill down” FR are shown in
Table 3, and the corresponding drill down DP are shown in
Table 4.
Table 2 Pugh Matrix for the Concept Selection
FR
Options
Bucket
Elevator
Olds Elevator
(Reference)
Conveyer
Belt Lift Hoist
System
FR01 0 0 0 0
FR02 0 0 - -
FR03 - 0 - +
FR04 - 0 - +
FR05 - 0 0 +
FR06 0 0 0 0
FR07 0 0 0 0
FR08 0 0 0 0
Sum + 0 0 0 3
Sum 0 5 8 5 4
Sum - 3 0 3 1
Net Score
-3 0 -3 2
Rank 3 2 3 1
Figure 1 Mind Map for Particle Lift Solution
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 119
Table 3 Detailed Functional Requirements
FR# Description
FR01-01 Be able to transport 250 nm size particulates
FR01-02 Be able to transport particulates up to 140 m
FR01-03 Be able to transport 20000 kg of particulates within 60 seconds
FR02-01 Material must be able to maintain desired strength
between 150 to 200°C environmental temperature
FR02-02 Fluids used including lubricants must be able to maintain
desired properties between 150 to 200°C
FR03-01 Skip must be insulated
FR04-01 Overall system must have less than 1% spillage or
system for recovery
FR05-01 Impact surfaces need to resist wear from loading and unloading particulates
FR05-02 All moving parts and ropes need to resist abrasion wear
either by clearance or debris removal mechanisms
FR06-01 Be able to have an overall energy efficiency of 75%
FR07-01 Skip maximum dimension must not exceed
LxWxH (2 m x 2m x 12m)
FR08-01 Single Rope Factor of Safety 5 [11]
FR08-02 Rope nominal diameter cannot exceed 76 mm
FR08-03 Rope contains independent core rope that can withstand shaft temperatures
FR08-04 Overall system should have factor of 5 [16], [17]
Initially, several lift options were considered. Analysis
identified the counterbalanced Blair drum hoist as the most
promising based on efficiency, cost, and reliability. Two
generic skip types were considered most promising: (1) the
Front Dump Skip and (2) the Kimberly Skip. The Front Dump
Skip is evidently favored in traditional mining, since its layout
is compatible with a relatively small cross section and longer
length. The smaller cross section is highly desirable in mining
where the vertical shaft can be hundreds to thousands of meters
deep. In contrast, the simplicity of the KS promotes a low
initial cost, low maintenance cost and high service life. All
these features are important in CSP applications; therefore the
KS was selected for this application.
The qualitative analysis (including construction and
operation of two scale models) outlined above identified the KS
with Blair drum hoist as the promising design. Furthermore
application of Axiomatic analysis shows that this combination
is a highly suitable design but not an ideal design. The resulting
design matrix, as seen in Figure 4 , is close to a triangular
matrix, meaning the design is largely decoupled. There was
clustering around FR for rope selection and temperature, as
expected. This indicates that these two criteria are critical to
success of this design.
Table 4 Detailed Design Parameters
DP# Description
DP01-01 Kimberly Skip Design
DP01-02 Blair Drum Type
DP01-03 AC Electric Drive Motors
DP02-01 Metal for Skip is of SS 316-H material 3 mm thick
DP02-02 Lubricant with NLGI No 2 and flash point over 200°C
DP03-01 Fire Brick with Thermal Conductivity of 0.32 W/m-K @
800°C and Density 800 kg/m3
DP04-01 Olds Elevator in Sump to remove spillage
DP05-01 Loading and Unloading angles greater than 20 degrees
DP05-02 Bearing and joint material with Hardness greater than
ID-50K
DP06-01 Lift efficiency greater than 75% & Recovery Efficiency
greater than 75%
DP07-01 Skip dimensions LxWxH (1m x 2m x 4m)
DP08-01 Rope Safety Factor of 5
DP08-02 Rope Diameter between 31.7 mm and 50.8 mm
DP08-03 Rope Core is SS316 material
DP08-04 Rope Safety Factor greater than 5 & Skip Safety Factor
greater than 5
All such spillage will be accumulated in a sump built into
the lift shaft, which can be emptied as necessary; therefore,
there will essentially zero net loss of particulate from the
system. Minimal heat leak is also an objective, and simplicity
of the proposed skip design makes it easy and inexpensive to
install adequate internal insulation to keep the heat leak from
the skip well under 0.1% of the rated capacity of the system.
Our design also envisions a lift shaft allowed to stay at
200°C (392°F), which further minimizes incidental heat leaks.
Altogether the proposed design ensures a minimal heat leak that
will have negligible effect on the overall system efficiency.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 120
No. Name
1 Lift Machine Room
2 Lift Discharge Chute
3 Particle receiver
4 High Temperature TES Bin
5 PWF Heat Exchanger
6 Low Temperature TES Bin
7 Lift Charge Chute
8 Lift Shaft
9 Top hopper
Figure 2 Lift integrated into TES Tower. [5]
Figure 3 Conceptual Insulated Kimberly skip charging, travel position and discharging [5]
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 121
=
Figure 4 Independence Axiom Matrix Analysis for Kimberly Skip with Blair Drum
COMMERCIAL SCALED PARTICLE LIFT The analysis and concept selection described above
resulted in the selection of KS with Blair hoist. Our next task
was to develop this concept into design for a commercial
particle lift that would be part of the 460 MW-th CSP tower as
seen in Figure 2. The design and engineering of a commercial
particle lift system was then completed. Conceptual design
drawings and energy efficiency and heat loss modeling have
also been completed. Detailed efficiency modeling based on
reliable published component efficiencies resulted in an energy
efficiency of 80%, which exceeds the 75% energy efficiency
metric. With this efficiency, the parasitic power should be less
than 1% of the rated output. The selected skip design is that of
the KS type, seen in Figure 3 with its charging, travel and
discharge configurations.
This skip is both filled and discharged from the top and
does not have a complicated and leak-prone bottom hatch. This
arrangement facilitates a design that is very simple structurally
and mechanically. The single top hatch, which is opened and
closed by motion of the skip, thereby eliminating any
mechanical or hydraulic actuators, is critical to this simplicity.
Importantly, this design appears to be almost leak proof and
should easily achieve much less than 0.1% temporary spillage
of particulate during filling and discharge.
The first set of drawings and specifications has been
completed, and we have already consulted with one company
familiar with steel fabrication and industrial lift manufacture.
This company has commented that our design will be easy to
fabricate. After incorporating some minor modifications based
on this review, we also consulted informally with a skip-hoist
component supplier. With helpful input from these initial
reviews from smaller companies, we contacted two of the
major manufacturers.
One of these manufacturers has commented that our
design should be generally feasible to fabricate and install;
however, they have responded that the Koepe hoist and a
bottom discharge skip should also be considered. The Koepe
hoist would probably be simpler and less expensive, and it
should also have lower drum inertia, which would reduce
dynamic loads and deceleration losses. Going forward, we will
definitely be considering these alternatives.
The simple design (basically a bucket with a hinged lid and
a lifting bail) allows effective thermal insulation with mere
layers of continuous suitable rigid insulation such as firebrick
inside the skip and the lid with no complicated bottom hatch to
insulate and no mechanism components (such as links and
latches) to act as thermal short circuits. The bottom hatch is
also likely to leak during lifting, which is not an issue when
handling typical raw materials but which is important when
hoisting the TES medium. Our experience with the two small-
scale models was convincing with regard to these issues.
As shown Figure 2 the Kimberly skip is easy to integrate
into the CSP system. Note that the lift shaft will be kept at
elevated temperature around 200°C (392°F) to minimize heat
losses, but the electrical and mechanical equipment (other than
the lift drum) will be kept at near ambient temperature for
efficiency and economy.
Typically, stainless steel such as SS316 wire rope is
selected for durability, corrosion resistance, and excellent high
temperature strength. Cost estimates based on a highly regarded
source of generic unit and subsystem costs are summarized in
the following Table 6.
MODIFICATION OF DESIGN FOR MANFACTURABILITY The design was then modified to make it more suitable for
manufacturability. For example the LxW dimensions were
made equivalent in order to fit inside shipping containers and
on trains. This also allowed the volume to change, thus
reducing the height needed.
Also, the safety factor was relaxed to 3 since the design
was considered as an industrial application where personnel
would be not be present in the vicinity during operation. This
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 122
modification has allowed the reduction of wire rope diameter to
below or equal to 76 mm (3 inches). In general, 76 mm is the
upper limit for easily available SS316 wire rope. The modified
design specification is shown in Table 5.
Table 5 Design Specifications for Modified Particle Lift
Design Specification Value
Power Capacity of Tower 460 MWth
System Mass Flow 979 kg/s
Skip installed 2 skips
Estimated Skip Dimensions (LxWxH) 2m x 2m x 6m
Ropes in use per skip 1 rope per skip
Rope Type SS 316 6x37 IWRC
Rope Diameter 76 mm (3 in.)
Drum to Rope Diameter Ratio 60
Rope Layers on Drum 3
Rope wraps per layer 5
Electric Motor AC Induction Motor
Gear Reduction Ratio 46
Overall Safety Factor 3
Table 6 Current Cost Analysis for Particle Lift
Estimated Cost of 2 Skips without Hoist System $198,533
Estimated Cost of Hoist System $294,600
OLDS Elevator Particulate Recovery system $30,000
Total Estimated Cost per System $523,133
Total Estimated Particle Lift cost per MWth $8,719
The cost analysis was conducted based on design of the
skip and using typical values found in cost databases for other
necessary parts of the system including the drive, control and
guidance systems[23].
The total estimated cost per system was determined to be
$523,133; this was then compared to the costing analysis
methods developed by Sayadi [24] which was $538,567 and
typical values found in the mining industry [22], which was
$1,171,900, all in US Dollars (2014).
The cost analysis and the estimate using the method
developed by Sayadi were close in value; however, the value
normally found in industry [22] was almost double this value. It
is felt that the difference is due to the overhead charged by
hoist vendors included in the hoist installations including
installation consulting fees, maintenance service agreements
and multi-year license and support agreements for propriety
software used in lift control systems. The estimates did not
include the construction of the lift shaft.
Accordingly, the lift system is expected to cost around
$8,719 per MWth, which agrees with previous independent cost
estimates using technology-specific cost engineering research
results, which was around $9,614 per MWth [25].
This analysis was conducted assuming a module of 60
MWth. A system size up to 460 MWth would be
accommodated by some combination of larger skips and
multiple pairs of skips.
Typically, costs per unit are improved at larger size, and
overall efficiency is only negligibly changed with larger system
size and longer lift.
The rope size of 0.076 m (3 inch) based on the above
calculated stress and on vendor tensile strength of SS316 using
the factor of safety of 5 as required by OSHA[26] for wire rope
and many other critical applications. A multi-rope system may
need to be considered in future analysis, since smaller rope
diameters are easier to source and are more flexible.
The energy efficiency modeling, which is still being
refined, is based on lift and recovery efficiencies and the ratio
of overall tare to payload in Table 7. The tare fraction is
important, since the potential energy of the skip and rope
cannot be 100% recovered. Also, the lift efficiency of 85% is
from several published standards and models and has been
confirmed by component modeling shown in Table 8. The
expected energy efficiency is 80%, which is higher than the
target of 75%.
Table 7 Estimates of overall efficiency for particle lift design.
Data Efficiency
Lift Efficiency 0.85
Recovery Efficiency 0.93
Ratio: Tare/PL 0.2549
Overall Efficiency 0.8064
Fraction Parasitic 0.0086
Table 8 Estimates of lift component efficiency for particle lift design.
Component Efficiency
VF Drive 0.96
Electric Motor 0.95
Gearing, 2 Stage 0.98 x 0.99 = 0.97
Rope/Drum Efficiency 0.98
Overall Product 0.86 to 0.87
Some remaining more detailed aspects that will be
investigated in future include tribological analysis and design
and material selection for all bearings, joints, and ropes. In
particular, situations where similar materials are in contact can
result in types of adhesive wear, such as galling or scuffing.
Furthermore the metals are typically softer than the particulate;
therefore, the possibility of third body abrasive wear [28] is
significant. The final design must ensure that such tribological
effects do not reduce the life of the skip or hoist system.
FURTHER WORK Currently, plans are under way to build a scaled working
model of the particle lift and to refine the cost model to reflect
more accurately the particle lift components and to account for
vendor markups. More importantly, research is planned to
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 123
verify outstanding issues with elevated temperature effects on
lubricated parts and material endurance on such components as
the wire rope.
CONCLUSIONS In conclusion, as the need for better performing and larger
capacity CSPs increases, the need for lower cost and higher
capacity TES will follow suit. This will be especially true for
solid particle based TES.
It has been shown that out of the many different systems
that can be used to transport the particles up to the PHR, a
suitable commercial solution would be a KS based particle lift.
This design would have low cost per unit of CSP power
capacity, high overall efficiency, long service life, and low
maintenance cost with proper tribological design. More
importantly, the sequence of design processes used developed a
viable solution which could easily be implemented in a
commercial project scope and other similar research projects
could use this exact sequence to bring forth a solution within
the scope of the project with time and budget constraints.
ACKNOWLEDGMENT Part of this work was supported by the US Department of
Energy through the Sun Shot Program's project for the
"Development of a High Temperature Falling Particle
Receiver" (Project ID: DE-AC04-94L85000). The Prime
Contractor is Sandia National Laboratories, and the Sandia’s PI
is Dr. Clifford K Ho. The financial and programmatic support is
recognized and greatly appreciated
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UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 124
Proceedings of the Fifteenth Annual Early Career Technical Conference The University of Alabama, Birmingham ECTC 2015
November 7, 2015 - Birmingham, Alabama USA
ANALYSIS OF AN EXPERIMENTAL BUILDING HVAC SYSTEM TO IMPROVE EFFICIENCY
Christopher Fernandez
Georgia Institute of Technology Atlanta, GA, USA
Sheldon Jeter
Georgia Institute of Technology Atlanta, GA, USA
ABSTRACT
The goal of this research project is to develop an easily
adaptable simulation model that represented a generic multi-
purpose building in higher education and research to determine
what heating, cooling, and ventilation systems are the most
energy efficient. To have full control of the building, a
computer based simulation software is used to adjust
parameters within a realistic budget. As such, some
assumptions are made, such as that the weekday people and
lighting loads are equal, to make scheduling loads easier and
consistent. While electrical usage is monitored, other
parameters are also factored into the model, such as: chilled
water thermal energy, hot water thermal energy, room air
changes per hour, occupant comfort, and contamination in the
form of high humidity and carbon-dioxide. It is found that
situational cost analysis based on electrical cost compared
favorably to access to supplied cooling and heating sources.
INTRODUCTION It is well known that the majority of electrical power
consumed in the United States by buildings goes into climate
control. With the rise of energy prices coupled with an
emphasis on renewable and efficient energy, developing a new
way to insure personal climate comfort and low energy usage is
a growing concern.
The objective of this work is to run simulations for a
building that represents a large majority of buildings on the
Georgia Tech campus. This required that the model building
have no unique architectural features or complications. The
experimental building is designed to be a typical low-rise
building with three floors and 3 zones per floor; the zones can
be changed to suit any design but are left as offices for
simplicity. Being an education-focused building, the lighting
and plug loads are set higher than a typical office building, but
can easily be changed to reflect conditions in any building.
Most buildings also have a perimeter zone and offices in
interior zones, but most buildings on the Georgia Tech campus
have exterior zones and internal passageways to allow labs and
classrooms to have windows. The flexibility coupled with ease
of schedule and zone changes allows a quick adaptation of this
model to most any building in higher education. While day to
day people, plug, and lighting loads realistically change, for the
sake of convenient schedules and to avoid different loads on
different simulations, the weekend and weekday schedules are
kept constant throughout the simulation year. In addition,
natural inefficiencies i.e. open windows, leaking ducts, etc. are
not included to insure all energy changes are the result of the
HVAC system and not a reduction in losses.
This paper discusses the details of the model, including
why certain design considerations are made. Software played a
major role in the research and development; the benefits and
problems that are overcome are detailed as well for those
unfamiliar with the programs used. In addition, some basic
explanations on how the different softwares go about
thermodynamic, contaminant balance, and heat transfer are also
provided. A desire to keep only a small number of variables in
the simulation is another priority in development, so a
procedure is established to give consistent results for every
simulation. The details of this are presented before results are
given so that one can understand the process before discussing
results of said designs.
MODEL DETAILS The model is broken up into three floors with three zones
per floor, as in Figure 1: the longer Main zone with Side A and
Side B having the same design as each other. The design is
based on most academic buildings on the Georgia Tech campus,
where corridors are small and in the interior, while offices and
classrooms are placed along the perimeter. The model building
included minimal fenestration along the West facing
fenestration; the majority of the fenestration is located on the
South and North faces, ensuring that all rooms have an ample
view. There is no fenestration on the East wall. While excluding
an interior zone may seem like a flaw in design, the people load
in a hallway is typically low and is an artifact of people
entering and leaving the individual zones. As such, a constant
supply of air could meet the thermal load of a hallway in
conjunction with substantial thermal capacity. In addition, one
could establish the building to have return ducts in the hallway
so that it would be conditioned by the air leaving the zones.
Having separate, smaller zones allows for greater zone control
over parameters and loads. Just as in a real building, all the
zones are not used equally throughout the day, structuring the
building to allow for fluctuations in usage, which are
independent.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 125
Figure 1. Top down view of zone layout
Figure 2. Georgia Tech Whitehead Building
While energy conservation is the main objective, having
occupants feel comfortable with large windows to the exterior
is incorporated into the design in Figure 3. Some modern
design features are incorporated in Figure 4, such as minimal
fenestration on the West and South, longer along the East/West
direction, high North facing fenestration as seen in the
Whitehead Building in Figure 2. [1].
Figure 3. SketchUp view of South and East walls
While the physical shape of the building is not changed
through the modeling iterations, the methods of meeting the
thermal loads and outdoor air specifications are adjusted and
compared to each other. With these criteria established, the
objective of finding the most energy efficient building is
pursued.
Figure 4. SketchUp view of South and East walls
SOFTWARE
EnergyPlus is a program provided by the US Department
of Energy; it is designed for complete building simulation of
energy, people, utility usage, lighting, energy flows, and many
other features. The program also allows users to establish how
rigorous the simulation should be. The program has an
abundance of beneficial abilities, such as automatic conversion
between English and Metric units, converting material
properties into constructions for boundaries, auto calculating
sizing and heat loads, and the ability to use real world weather
data.
EnergyPlus is an ideal simulation tool for buildings
because it is a combination of a thermodynamic, mass transfer,
and concentration model. The benefit is that these three
parameters can be adjusted independently of each other yet can
still function together. In addition, there are a number of
simulation parameters that can bring EnergyPlus to an all-
inclusive level of simulation and customization. There are
options for different convection and conduction algorithms that
apply to different methods of building modeling and heat
transfer levels.
Practically any simulation and method for heat or particle
transfer can be represented through EnergyPlus. At its most
advanced, it is even capable of monitoring moisture diffusion
and evaporation through layers of materials depending on
material properties and temperature gradients, isotherms, and
conductivity.
EnergyPlus is also compatible with other software such as
Google SketchUp and OpenStudio, which are used to create the
building geometry. Other programs are used for geometric
construction because EnergyPlus relies on a coordinate system
input for walls, floors, roofs, and fenestrations while Google
SketchUp uses a graphical interphase.
In addition to external software compatibility, EnergyPlus
allows for external data entry directly into the simulation. This
includes, but is not limited to, actual electrical loads, which can
be reduced into specific loads per zone, occupancy loads for
individual zones, and real world weather. While not necessarily
required for a simulation, understanding how a building
realistically is used and the weather conditions it will be subject
to can greatly influence how to properly setup a building to
reflect actual usage. While EnergyPlus includes TMY2 weather
data, weather data that reflects an average over the entire year,
it is possible to gather actual weather information and simulate
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 126
the building that way. While it is not recommended to design a
building based on only one year of data, this allows one to see
how a stretch of extreme weather could overload a HVAC
system. On the Georgia Tech campus, a local weather station is
setup to record wet and dry bulb temperatures, humidity, solar
irradiance over the horizontal and direct, as well as rain, wind
speed and direction.
PROCEDURE The building structure and layout in Figure 5 was created
in Google SketchUp and has a design of a building that
represents a majority of the buildings on the Georgia Tech
Campus. No hallways are added, so that the entire building
would be used as occupied space; this is done for simplicity.
While this may lead to an inefficient design where the envelope
of the building directly affects the thermal comfort of the
occupied zone, it also makes changes to the model more
noticeable. In addition, most hallways are in the interior core of
buildings to allow windows in offices and classrooms.
Throughout all simulations some properties are kept
constant, such as the ASHRAE standard 15 cfm/person of
outdoor air as well as person, lighting, electrical schedules and
power consumption.
Figure 5. HVAC Standard diagram
A zone is susceptible to different loads of varying intensity
and type. In the simulation there are four sensible heat loads:
shell load from the atmosphere, plug load from electrical
devices used inside the room, lighting load is recorded
differently, as lighting can be independent of the number of
people occupying a load. The simulation includes two latent
loads: the atmospheric humidity and humidity from occupants.
The first simulations are done to establish a good base
construction. This includes materials and insulation layering
within walls and roofs. The default constructions are an
inefficient and “heavy wall” design. While the heating load is
lower in winter, the cooling load the rest of the year is greater.
Atlanta is a hot, humid climate, so cooling with
dehumidification is much more significant on energy usage
than heating. The materials are changed to reflect the
construction of the Carbon Neutral Energy Simulation building
on campus that is LEED certified. It was discovered that while
the construction was “lighter” and required more heating in
winter, the cooling load was lowered overall.
Once materials and construction design and schedules were
set, only the methods for heating and cooling the zones were
changed. Like most buildings on campus, the temperature
allowed out of the cooling coil was no higher than 55F, to keep
humidity in the zones acceptable. While this leads to a
comfortable zone, the energy usage is high, due to the amount
of reheating of cold air into the zone. Other systems are
simulated including different versions of heat recovery, radiant
heating, radiant cooling, dedicated outdoor air units,
displacement air, and many others. With the parameters
established, different heating and cooling designs are developed
and tested.
DESIGNS
There are two distinct methods of delivering air into zones:
the variable air volume (VAV) system, as in Figure 6 and
dedicated outdoor air system. Both of these systems were
simulated with heat recovery in the air system or radiant
heating/cooling inside the zones.
Figure 6. Standard outdoor air treatment leading to
variable air volume unit
The Standard outdoor air treatment, as in Figure 7, should
be the most inefficient system because it has to cool all air to
55F before sending it to the supply splitter, where it is then
heated by the VAV (not shown) to insure the room does not
drop below the heating set point.
Figure 7. Standard outdoor air treatment with preconditioning heat and enthalpy exchanger
The standard system with a total heat and enthalpy
exchanger requires balanced flow and affects the outside air
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 127
inlet. The total energy heat exchanger includes latent and
sensible heat transfer. Interestingly, it was found to be more
efficient to have a high latent and sensible heat transfer (90%
efficiency) and a low (20% efficiency sensible, 0% efficiency
latent). This is due to the outlet from the refrigeration coils
being set to 55F and there being return air recirculated in the
system. Hotter inlet air requires more cooling energy, so it is
advisable to get the inlet air as close to 55F as possible in order
to accomplish dehumidification.
The dedicated outdoor (DOA) air system is in Figure 8. It
is the simplest, as the intake is balanced with the exhaust.
Figure 8. Dedicated outdoor system
The benefit to the dedicated outdoor air system is that only
the outdoor air needs to be supplied and conditioned for the
space and humidity levels. In addition, the system is zone
independent, which allows different loads and temperature set
points to easily be adjusted for. However, a DOA with heat
recovery, as in Figure 9, is much more efficient, as the air is
precooled and then reheated through dehumidification after the
initial heating and cooling.
Figure 9. DOA with two heat exchangers
The first wheel is sensible heat transfer only while the
wheel on the right is latent transfer only. This has a surprising
benefit because the return air is cooled by giving up its latent
load, which makes the precooling on the left even more drastic.
Conversely, the outside air gets cooled more before being
subject to the cooling coil, but is reheated slightly and
dehumidified more through the desiccant wheel. There is
another benefit, because the inlet air is dehumidified entering
the room, a higher cooling set point can be set because the
dehumidification is not all performed at a single source.
SIMULATIONS The temperature set point schedule, occupancy, lighting,
and plug schedules are all kept constant, as indicated in Figure
10.
time occ
up
ancy
frac
tio
n
ligh
tin
g
load
frac
tio
n
plu
g lo
ad
frac
tio
n
coo
lin
g
setp
oin
t (F
)
he
atin
g
setp
oin
t (F
)
0:00-6:00 0 0.05 0.4 80 65
6:00-8:00 0.5 0.3 0.9 75 70
8:00-12:00 0.8 0.3 0.9 75 70
12:00-13:00 0.5 0.9 0.9 75 70
13:00-17:00 0.95 0.9 0.9 75 70
17:00-18:00 0.8 0.5 0.9 75 70
18:00-24:00 0 0.05 0.4 80 65
weekends 0 5 0.4 80 65 Figure 10. Schedules
Throughout all the simulations, changes were made only to
the system responsible for air flow, heating, and cooling. The
people, plug, and lighting loads were all kept within a typical
load for an academic building to insure reasonable reporting of
results with moderate accuracy. While many simulations were
performed, the most reasonable systems, which covered a broad
range of similar systems that would meet a building or
ASHRAE code, were reported.
A possible source of error is the lack of internally specified
equipment within EnergyPlus. For instance, the fan and pumps
had no default or auto size for power usage; it may be possible
to generate false results with improper fan specification. To
minimize risk in equipment specification, data from high-
performance buildings on the Georgia Tech campus were used
in this simulation, notably Clough Undergraduate Learning
Commons, and Carbon Neutral Energy Solutions. However,
some data, such as those on radiant heating and cooling systems
were underperforming or unavailable. In the event that
specifications were missing or proprietary, a minimal impact
setup was created so as to not set an artificially high load for a
minimal impact system.
Another area of difficulty is the lack of intelligent
simulation adaptation. EnergyPlus requires an extremely
specific inputs to run as intended. This becomes problematic
when sometimes a simulation will run without error, but behave
in a manner that is not representative of the intended model; as
a result, manual checking of the simulation is required to insure
intended and accurate results.
In all buildings, a minimum outdoor airflow is required,
but a minimum of 15 cfm/person was selected in an attempt to
equalize results. However, a traditional VAV does not have
occupancy control and only meets a thermal load and as such,
used the most cooling and heating load.
RESULTS
As Table 2 shows, by reducing the outside air flow in an
identical system, the efficiency of heating and cooling water
systems is slightly offset by the use of electricity. This result
shows that a single answer system cannot be determined. This
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 128
becomes increasingly true if there are different methods for
gathering electricity (grid or renewable) or if the chilled and
heated water is produced on site. If an institute can receive
electricity more cheaply than it can supply water throughout a
building, then a high-efficiency VAV system would probably
have cost benefits better than a more efficient chilled beam
system. Comparing raw numbers between buildings is not an
effective method for analysis. To normalize results the use of an
Energy Use Intensity (EUI), both as a sum of total energy (as
kBTU) over gross area and the method of adjusting site/source
usage from the US Environmental Protection Agency [2] was
used in the results. While the EUI of other buildings on Georgia
Tech’s campus is not reported, the expected EUI excluding
source multipliers has been published by the EPA (Autodesk
Sustainability Workshop, 2011) and is in Figure 12.
Figure 11. EPA source energy use conversion
Figure 12. EPA generated EUI table
The setup and usage of passive systems is discovered to
have a drastic change in the efficiency of the building. It is
discovered that having a conventional VAV with chilled beams
saved negligible amount of cooling energy but cost
significantly more electricity. When the radiant temperature
control system is used as the primary method of control with an
occupant controlled outdoor air supply, the electrical, cooling,
and heating load all decreased significantly. It should be noted
that for the DOA and radiant system, an almost constant mass
flow rate of water into the chiller is discovered. While the
details of this anomaly could not be corrected, it is theorized
that by ignoring the chiller power, the DOA with radiant system
is by far the most efficient system. It is believed that a design
criterion within the EnergyPlus software creates a non-zero
mass flow rate through the radiant system.
Table 2. Results
CONCLUSION While the energy usage seems to show large usage with
radiant supplement it is believed this may be due to
inconsistencies within the EnergyPlus model. The dedicated
outdoor air model required a constant mass flow rate of chilled
water regardless of the air flow through the unit, which
dramatically increased chiller power with no gain. Additionally,
in EnergyPlus, heated floors and chilled ceilings cannot exist
within the same floor construction if the zones are stacked.
type
Building
total
elecrical
(MWh)
HVAC
Electrical
(MWh)
Chiller
Electrical
(MWh)
Cooling
thermal
energy
(BTU 10^6)
Heating
thermal
energy
(BTU
10^6)
EUI
(kBTU/ft^2)
EPA EUI
(kBTU/ft^2)
VAV Standard 852 547 285 2630 304 144.3 330.8
VAV Standard w/ Heat Recovery 837 540 305 2467 307 139.0 321.4
VAV People controlled OA 790 485 297 2077 152 121.6 291.9
VAV Radiant heat&cool 1059 754 462 2603 272 160.2 387.3
DOA & Radiant 894 589 305 1202 63 106.6 291.2
DOA & Radiant people controlled 1135 829 694 1068 45 123.1 354.2
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 129
Realistically zones on the same side of the building with similar
usage would be calling for heat and cooling at the same time,
meaning a chilled ceiling and heated floor would add to heat
transfer through free convection and further reduce electrical
and hot/cold water requirements. It is also important to consider
the costs of implementing systems when considering where and
how a building is used. In temperate climates where windows
are used, an exclusive radiant system could be acceptable while
in a humid climate dehumidification is important, so radiant
systems can only account for sensible load. What the simulation
shows is how a generic building could perform in any location
with minimal adjustments. A change in the weather file and a
couple of parameters would effectively completely change the
output and the determination of what system is most cost
effective.
REFERENCES [1] "Georgia Tech Stamps Health Services," Georgia Institute
of Technology, 2014. [Online]. Available:
https://health.gatech.edu/Pages/default.aspx.
[2] Trustees of the University of Illinois, Ernest Orlando
Lawrence Berkeley National Laboratory, "EnergyPlus," 2015.
[Online].Available:
https://energyplus.net/sites/all/modules/custom/nrel_custom/pdf
s/pdfs_v8.3.0/EngineeringReference.pdf.
[3] Autodesk Sustainability Workshop, "Autodesk," Autodesk,
2011.[Online].Available:
http://sustainabilityworkshop.autodesk.com/buildings/measurin
g-building-energy-use.
[4] Energystar, "EnergyStar," EnergyStar, [Online]. Available:
http://www.energystar.gov/buildings/facility-owners-and-
managers/existing-buildings/use-portfolio-manager/understand-
metrics/what-energy. [Accessed 25 08 2015].
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 130
Proceedings of the Fifteenth Annual Early Career Technical Conference The University of Alabama, Birmingham ECTC 2015
November 7, 2015 - Birmingham, Alabama USA
UNCERTAINTY ANALYSIS METHODOLOGY FOR PARTICLE HEATING RECEIVER TESTING
Clayton Nguyen Georgia Institute of Technology Atlanta, Georgia, United States
Matthew Golob Georgia Institute of Technology Atlanta, Georgia, United States
Sheldon Jeter Georgia Institute of Technology Atlanta, Georgia, United States
Said Abdel-Khalik Georgia Institute of Technology Atlanta, Georgia, United States
ABSTRACT The use of particulates as a thermal medium for solar
receiver technology has opened new possibilities in pushing temperature limits and power cycle efficiencies for solar power plants. To explore particulate use, a new type of solar receiver is being developed at Georgia Tech. To show the viability of this technology a high thermal efficiency in combination with highly accurate results must be shown.
In this paper the uncertainty analysis used to evaluate solar particle heating receivers will be covered. The paper focuses on the uncertainty of single point measurements and on reducing the direct measurements to obtain an overall lower indirect measurement uncertainty. The end result is that a receiver efficiency with an uncertainty lower than 5% can be found.
INTRODUCTION This paper will describe and demonstrate the methodology
and data sources used to analyze and estimate the combined uncertainty of the experimentally measured solar energy collection efficiency of a small scale particle heating receiver. While such a value can be calculated through basic thermodynamic and statistical techniques, the greater objective of this work is determine the receiver efficiency with uncertainty that is less than 5%. Uncertainty results will come from a combination of two types of error, random fluctuations (UA) and systematic bias (UB). A thorough accounting of the methods used to obtain an uncertainty of less than 5% will be shown here. These methods are recorded to show the validity of the results of the experiment and to provide a guideline and reference for future related work. The experiment uses Georgia Tech’s High Flux Solar Simulator to heat a particle heating receiver (PHR). To test this apparatus three main measurement methods are used. A calorimeter is used to measure the expected heat flux into the particle heating receiver, a scale base is used to measure the outlet mass flow rate and thermocouples are used to measure the heating of the particulate through the PHR.
LITERATURE Presently, we are collecting single point efficiency data and
not developing a statistical model for the efficiency. The basic theory for the uncertainty of single point measurements is based on the work of Kline and McKlintock [1], describing how to compute the uncertainty for single sample experiments. The basic form for calculating an indirectly measured value is shown in equation 1 below.
22
22
2
11
nn
RB wv
Rw
v
Rw
v
RwU
(1) The paper describes how to combine the uncertainty of the
various variables into an overall uncertainty for the final computed measurement. For completely independent sources of error the sum of the squares of the uncertainties is equal to the squared overall uncertainty, as in equation 2.
222BAC UUU (2)
Standards set forth by Kline and McKlintock are used to help determine the American Society of Mechanical Engineering’s standards for uncertainty analysis [1]. The measurement of solar collection efficiency is particularly complex in comparison with similar mechanical engineering applications, so special attention is required to attend to all the details.
EXPIREMENTAL BACKGROUND The purpose of the experiment is to find the receiver
efficiency of a PHR. To do this HFSS is used to heat the particulates that flow through the PHR. In the current experiment particulates flow through the receiver by process of a batch system. An upper hopper is opened to allow the particulate to fall through the system, and the particulate is then caught in the lower hopper.
The receiver efficiency is evaluated using equation 3:
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 131
rcalorimetercalorimete
inout
rCalorimete
absorbedPHR
)(
Q
dTcm
Q
hhm
Q
Q
out
in
T
T
p
(3) To evaluate the receiver the efficiency, the upstream
temperature, downstream temperature, specific heat of the particulate, irradiated heat flux in and mass flow rate must all be measured. To minimize the overall uncertainty each of these measurements is carefully calibrated: Mass and mass flow
The lower hopper is placed on top of a load cell; the response can be used to transiently measure the mass flow rate of the system. The calibration of the load cell is the first of the key measurements necessary for evaluating the PHR. The change in the signal output of the load cell over time will be used to measure the mass flow rate.
Incident power
The second key measurement is the irradiative power going into the PHR, which is measured by a separate device, the calorimeter. The calorimeter uses the same receiver aperture as the PHR, in the same location to ensure similar operating conditions.
Temperatures
Finally to measure the particulate temperatures, a series of thermocouples is placed within the system. Each of these thermocouples must be submerged by the particulate flow to read an accurate temperature. They must then be calibrated to a higher degree of accuracy due to the small expected temperature differential. Specific heat
The specific heat of water is a well-known value. Water purity is measured to assure that there are no significant deviations from the commonly accepted values. These values are used when calculating the heat input into the calorimeter.
In comparison, the specific heat of the particulates is much more difficult to measure but serves an important role since these tests are to be conducted anywhere between 25°C to 700°C. Over this range of temperatures the specific heat changes significantly. To accommodate for this, the specific heat values will be found using experimental and chemical composition data.
STATISTICAL UNCERTAINTY While the bulk of this paper focuses on the minimizing the
systematic bias in the results, each set of tests and calibrations will also take into account the statistical uncertainty. The statistical uncertainty is obtained by calculating the sample standard deviation of a set of data, multiplying that by the corresponding coverage determined from the small sample t-statistics factor and dividing the square root of the number of data points. UA is the expanded uncertainty with 95%
coverage, while the UA is the standard uncertainty, as in equation 4.
n
SSDkukU cAcA
(4) The coverage factor, kc can be found by taking the two
tailed inverse of the Student’s t-test at a 95% probability. For estimating purposes 2 can be typically used as the coverage factor for large data sets. At the end of the experiments the UA is found over a steady state result (explain s-s) and is then combined with the UB to find the combined uncertainty, UC.
TRANSIENT MASS FLOW MEASUREMENT The measurement of the mass flow rate of the systems is
absolutely essential to transiently measuring the receiver efficiency of the system. A scale base [2], load cell with a platform to prevent oscillation issues, is used to measure the mass of the system. Post analysis of the signal allows for the mass to be measured with time to find the mass flow rate.
The scale base is calibrated using two different methods. The first method uses a series of weights measured by a high accuracy lab scale (±0.1 g). This provides a calibration range for up to 50 kg. This calibration is used for measuring the mass of a gradually filling bucket of water in order to calibrate a flow meter. The scale base provides an additional frame, which is used to mitigate the slight oscillations that occur in isolated load cells. This in turn reduces the noise in the signal output from the load cell.
Mass calibration of the scale base used to measure the mass flux rate was done incrementally using an existing hopper filled with ID50-K particulate and an empty hopper set on a scale base mounted in the planned PHR test configuration. A beaker full of ID50-K was then taken out of the full hopper placed on an OHAUS GT8000 (±0.1g) scale [3], where it was weighed and recorded with the beaker’s mass tared out; it was then poured into the empty hopper. This process was repeated until the empty hopper on the scale base was filled. A few extra weights were added to increase the calibration range in case of future modifications to the hopper. This yielded a calibration for the scale base accurate to within ±2.5g and ±0.21g/s transiently over the needed test range.
The GTHFSS setup uses the manually calibrated scale base load cell in order to measure the mass in the outlet hopper. Post processing was used in order to transiently measure the mass flow and flux rates. The rate of change in the load cell signal was calculated using a five point stencil as in equation 5 below,
avg12218182
tiiii
dtid
(5) where tavg represents the average time differential between each measurement, ε is the signal voltage and i is the index for each measurement. A calibration constant Km was then used to convert the data into a mass flow rate as in equation 6.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 132
CalKdt
dm
(6)
Table 1: Expanded Uncertainty for the Particulate Mass Flow Rate
Measurement xiU
Influence Coefficient,
ix
m
22
ixii x
hUU
(g/s)2
2
2
B
i
U
U
(%)
Source
ROC of Scale Voltage, ε
1.026 x10-8 V/s 71002.2
)/(
mK
dtd
m
9.8×10-2 100 (1)
Scale Calibration Constant, K
1640 g/V
6105.6
dt
dmK
m
1.1×10-4 0 (2)
sum of Ui2 = 9.8×102 Expanded
Uncertainty UB= 3.1×10-4 kg/s
1) 5 point numerical stencil, Uxi calculated in Appendix 2 using the 34970 user manual (1)
2) Manual calibration using a series of known weights
Figure 1: Mass Calibration
By taking a graph of the accumulated mass against the time one can see that the average mass flow rate is 0.13 kg/s with an R2 value well above 0.90.indicating a steady mass flow rate.
CALORIMETER EXPERIMENT The calorimeter is the simplest of the devices and
quantifies the heat input into the system through the HFSS. The calorimeter is made up of a copper tube coiled into two sections, a cylinder and a cone. The coils of the copper tube are then compressed using a stainless steel frame, and a cap is added to end of the cone in order to form a receiver. The cylindrical diameter of the calorimeter is 102 mm, which is larger than the PHR aperture. The larger diameter of the cylinder is to ensure that the entirety of the incident radiation is captured within the cavity.
Figure 2: Calorimeter Overview (1) Insulation, (2) Water
cooled coil, (3) Cavity, (4) Stainless Steel frame Water flows within the copper tubes to absorb the energy
absorbed by the cavity. Water temperatures are measured at the inlet and outlet of the calorimeter using resistance temperature detectors (RTD). The calculated enthalpies are then used to find the amount of heat input expected in the PHR experiment. Each of these RTDs is calibrated using a highly accurate standard from Burns Engineering. The platinum resistance thermometer (PRT) has an accuracy of ±0.0025 K and can be calibrated anywhere between -190°C to 420°C. The PRTs are calibrated using a water bath at several different set points, and this is done during heating and cooling to prevent uneven heating and cooling biases.
Additionally a digital flow meter measures the mass flow rate of the water. The device provides a voltage response based on the flow of the water. The mass flow rate is obtained as an indirect measurement. This is accomplished by calibrating the flow meter while filling a container placed on the previously calibrated scale base. To provide an added sanity check the final mass and time are compared to the mass flow rate for that time period.
Measurements are taken at three different flow rates. A five gallon bucket is used to hold the water; as such the maximum amount of statistical data that can be collected for each run is dependent on the time necessary to fill the container. Each of the runs is conducted using 100 powerline cycles, and a measurement is taken every 10 seconds. At the highest flow rate 40 measurements can be taken prior to overflow.
These measurements are taken using a digital instrument and have the associated resolutions for each device. The calibration of each of these devices determines the least significant digit. The uncertainty of the calibration is taken to be the systematic bias in each single measurement experiment.
y = 0.13x + 27R² = 1
0
20
40
60
80
0 100 200 300
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Time (s)
1
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2
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ver can be estabThe heat flux thal calorimeter fequation 7.
mQ
14
et to act as a steady state bn of the shield hed planar l
ge data and flummediately bef
n lamp perforbefore any PHRystem were in
Lambertian tarmp 7 at focal p
of testing the manage this, thy gauging and nd Lambertian
d a consistent aements when
SUREMENT e lamps’ outpuer coils with he water inlet aole body is hepper non-cavitlines. A displam of the calorer passing throu
a heat flux oblished. hrough the irisformula divide
waterp,watercm
1
shield. This isefore calorimwas confirme
lasers. For qux images werfore each calormance. The sR runs to verifysame conditio
rget at focal pplane Flux Ga
calorimeter, lohe series of pla
setting the orin target to thand quickly retesting, with a
ut, water was rcalibrated RT
and outlet of theavily insulatety surfaces as
acement water rimeter to meugh the calorimoutput of the
s plate is calcued by the area
2
4inoutr
DTT
Page 13
used to ensurmeter and PHR
d between runquality controre also taken aorimeter test tosame data any that the lamp
on as during th
plane (Right) auge Sensor
ocation was thanar lasers waientation of the lamps’ foca
epeatable set oan accuracy o
run through thTD temperaturhe coiled cavityed to minimiz well as wateflow meter waasure the masmeter. With th
lamp into th
ulated using thof the aperture
2 (7
1
3
e R ns ol at o d s e
e as e
al of of
e e y e
er as s e e
e e,
7)
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 134
Table 2: Uncertainty Table for 4 Lamps Calorimeter Test (~123.5 W)
Measurement xiU
Influence Coefficient
ix
m
22
ixii x
hUU
(W/cm2)2 2
2
B
i
U
U
(%)
Mass Flow Rate1 ṁ
0.22 g/s
7.22
inoutwaterp,4
)(
D
TTcwaterm
Q
0.35 21
Inlet Temperature2
Km
0.0251 °C
6.32
waterp,water4in
D
cm
T
Q
0.0081 0.50
Outlet Temperature2
Tout
0.0251 °C
6.32
waterp,water4out
D
cm
T
Q
0.0081 0.50
Diameter3
D 0.038 cm
293
inoutwaterp,water8
)(
D
TTcm
D
fluxQ
1.3 78
sum of Ui2 = 1.6 Expanded
Uncertainty UB = 1.3 (W/cm2)
1) Mass flow rate is obtained from flow meter calibration, which is performed using a scale base that has been previously calibrated using known weights.
2) Temperature readings are calibrated using a Standard Platinum Resistance Thermometer
3) The diameter is found using multiple measurements taken using a dial caliper
Table 3: Heat Flux for Various Lamp Combinations
Lamps Overall Heat Rate
(W) Heat Flux (W/cm2)
1, 4 2760 ± 15 55 ± 0.58 1,4,7 4470 ± 23 89 ± 0.93 1, 2, 4, 7 6030 ± 31 120 ± 1.3
In order to determine the effects of heat loss in the calorimeter, the calorimeter was run at ambient temperatures. The increase in the water temperatures and the flow rate was used to calculate the UAL (overall heat loss conductance) as shown in equation 8 below.
2outletinlet
rCalorimete
)rCalorimeteamb
(L
inoutwaterp,waterL
TTT
TTUA
TTcmQ
(8)
This UAL was then used to calculate the heat loss from the calorimeter for tests at non ambient temperatures. The temperature of the calorimeter was approximated by taking the average of the inlet and outlet temperatures of the water.
Even with the highest heat input used, 6200W, the overall heat loss was calculated to be only be 9.1 Watts. Relative to the input this is negligible at lower temperatures. Fig. 5, below shows the heat flux once the lamps have reached steady state operation.
Figure 5: Lamp Solar Flux Outputs
It is important to note that the number of lamps used on the
calorimeter is only approximately proportional to the solar flux through the aperture. This is due to complexities in the system causing each lamp to provide a different amount of power. In addition, the amount of irradiation that is focused through the aperture differs from lamp to lamp due to slight variations in the alignment of the lamps and interference from the iris edge.
RECEIVER TESTING The particle heating receiver is tested using a batch process
system. Particles are contained within an upper hopper that provides enough material for approximately 5 minutes of run time. The scale base is used to transiently measure the mass flow rate of the particulates through the particle receiver. To measure the temperature of the particulate, sets of thermocouples were placed at the inlet and outlet of the particle heating receiver.
The outlet particle thermocouples measure the particulate temperature after the particles travel through a mixer section. Due to the low thermal conductivity of the particles and the uneven heating in the PHR at the small scale, this section is required in order to measure any sort of average particle outlet temperature.
The receiver efficiency is calculated by comparing the difference of the inlet and outlet enthalpies to the heat input measured by the calorimeter, as in equation 9. These enthalpies are highly dependent on the specific heat of the particles, which
0
50
100
150
0 20 40 60 80So
lar Flux (W
/cm^2
)Scan
Lamps 1,2,4,7 Lamps 2,4,7 Lamps 1,4
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 135
is temperature dependent. To measure this specific heat, a digital scanning calorimeter (DSC) can be used in combination with a theoretical model using the Kopp-Neumann law. According to the manufacturer, a high end DSC, assuming no operation errors, can provide an accuracy of 3.5%. In comparison, studies have shown that the Kopp-Neumann law is accurate to about 3% near ambient and 4-6% at high temperatures.
rcalorimetercalorimete
inout
rCalorimete
absorbedPHR
)(
Q
dTcm
Q
hhm
Q
Q
out
in
T
T
p
(9)
To evaluate uncertainties related to the Kopp-Neumann law, a conservative uncertainty will be assumed. For DSC measurements, 3.5% uncertainty is used as the UB while the UA is calculated during the regression of an experimental model for the data.
The uncertainty table used for calculating the receiver efficiency shown below is an abbreviated table. Since there are multiple thermocouples, and since the numbers change from setup to setup, the table easily changes depending on the setup of the instrumentation for the PHR. In addition, depending on the type of numerical regression used, the model used for the formula changes drastically and can greatly extend the size of the partial derivative. As such this table serves only as a basic template.
Table 4: Uncertainty Table for Receiver Efficiency Test
Measurement xiU
Influence Coefficient, `
ix
m
2
2
B
i
U
U
(%)
Mass Flow Rate, ṁ
0.0031 kg/s
476.9PHR
m
45 (1)
Inlet Temperature, Tin, 1
0.025 °C 02.0PHR
inT
0.01 (2)
Outlet Temperature, Tout, 1
0.025 °C 01.0PHR
outT
0.01 (2)
Calorimeter Heat Input, Qcalorimeter
0.001 kW 14.
rcalorimete
PHR
Q
0.5 (3)
Specific Heat Cp
3.5% 92.0PHR
pc
54 (4)
1) Obtained from flow meter calibration, which is performed using a scale base that has been previously calibrated using known weights.
2) Calibrated using a Standard Platinum Resistance Thermometer
3) Measured using a digital scanning calorimeter and the Kopp-Neumann Law.
4) Experimental measurement
DISCUSSION The receiver efficiency for an ambient test run was found
to be 91.58% with a UA of 0.61% and a UB of 4.36%.The major sources of error can be attributed to uncertainty in the specific heat and the mass flow rate. The uncertainty in the specific heat was not unexpected due to the inherently difficult task of measuring the specific heat of particulates in comparison to measuring that of a block of material. These difficulties can be attributed to the interstitial spaces between the particles and to difficulties due to contact resistance.
CONCLUSION This methodology has used several different instruments to
measure data in order to evaluate the receiver efficiency for a particle heating receiver. Each instrument has been calibrated as accurately as possible to minimize uncertainty in the results. As such the main sources of error come from the uncertainty of the specific heat, which cannot realistically be reduced further than 3.5%, and the mass flow rate. With an absolute uncertainty of about 4.5% this methodology for measuring the efficiency of particle heating receivers provides a means by which future experiments can be expected to provide high quality results.
REFERENCES 1. Describing Uncertainties in Single-Sample Experiments. Kline, S. J. and McClintock, F. A. 1, 1953, Vol. 75. 2. Ohaus B250S. Champ Scale Bases. [Online] dmx.ohaus.com/WorkArea/downloadasset.aspx?id=6570. 3. GT8000. Ohaus Discontinued Product Instruction Manuals. [Online] [Cited: ] 4. Keysight Technologies. 34970A Data Acquisition / Data Logger Switch Unit. Keysight Technologies. [Online] May 2012. http://www.me.umn.edu/courses/me4331/FILES/AgilentManual_34.pdf.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 136
Proceedings of the Fifteenth Annual Early Career Technical Conference The University of Alabama, Birmingham ECTC 2015
November 7, 2015 - Birmingham, Alabama USA
A DISTRIBUTION LEVEL RENEWABLE INTEGRATION OPTIMIZATION TECHNIQUE WITH VARIABLE PRICING, DEMAND RESPONSE AND THERMAL ENERGY
STORAGE FOR RESIDENTIAL APPLICATIONS
Justin M. Hill Hessam Taherian Sandeep S. Chahal
University of Alabama at Birmingham Birmingham, AL, USA
ABSTRACT
As renewable energy generation continues to proliferate
throughout the world, advanced strategies for integrating
intermittent generation into the existing grid infrastructure must
be developed. This paper proposes a technique where an
iterative method of energy supply and demand optimization in a
distribution system can be implemented by utilizing
advancements in two-way communications, variable rate
structures, energy storage and home energy management
(HEM) systems. This approach coordinates traditional
resources with renewable generation and energy demand to
maximize the production from renewable resources and
minimize the curtailment due to excess supply. It begins with
the electric utility developing a sub-hourly day-ahead energy
cost based on forecasted renewable output and energy demand,
which is then sent directly to a participating customer’s HEM.
This pricing structure contains multiple energy price points
each hour, typically in five to fifteen minute intervals, which
are based on the forecasted energy availability for the
upcoming day. The customer’s HEM takes this cost schedule
and develops a schedule for how the home will operate based
on programmed preferences and forecasted distributed
generation production locally available, all with the goal of
minimizing the total energy cost for the homeowner while
maintaining their comfort settings. Each home then sends their
load shape to the utility who aggregates the energy
consumption data and recalculates an updated sub-hourly
pricing scheme. This new pricing schedule is republished to
further encourage customers to move what energy consumption
they can to lower cost times where energy generation is in
excess of demand. This process will iterate until the supply and
demand of energy are reasonably aligned, utilizing additional
necessary parameters required to ensure the optimal strategy is
seen by all participants. Additionally, customer owned energy
storage systems – thermal, battery or other – receive a second,
more real-time pricing signal throughout the actual day to
compensate for forecasting errors in the day-ahead scheme.
The goal of this strategy is to develop a non-invasive method of
coordinating energy supply and demand while allowing the
customer to maintain control over their energy usage and
comfort. The strategy is currently being modeled with
approximately ten homes utilizing DOE building simulation
platforms and tools which can show the effect of the control
algorithms when aggregated together in a community.
INTRODUCTION Since energy generation is beginning to shift away from
largely base load sources with fossil fuel and hydro based assets
being utilized to compensate for the variability in energy
demand towards higher amounts of renewable generation,
which introduces uncertainty in both supply and demand of
energy, energy utilization optimization research will be forced
to follow. This implies a shift in research from component
level efficiencies to controlling when loads occur on the grid
and making them responsive to grid signals. More simply
stated, it will become increasingly important at what time of the
day energy is consumed rather than how much energy each
component consumes over the course of the year – see [1].
This type of responsiveness in end-devices to grid signals is
made possible by the advent of sophisticated communication
protocols and/or advanced, second generation smart meters
from electric utilities which can be used to exchange pricing
signals and other types of information and also, more
importantly, the advances in wireless communications and the
increase in their speed and bandwidth make fast exchanges of
large packets of data possible.
Due to the previous factors, the project described in this
report focuses on a control methodology to iteratively optimize
the balance between supply and demand for energy on a day-
ahead basis utilizing variable rate structures. The project also
implements a secondary rate structure to utilize different types
of energy storage on a shorter time interval. This dual
timeframe structure was chosen, as it is seen as a void in
current research and can be utilized by the utility to allocate
generation resources more effectively and increase the
efficiency of the grid while also compensating for errors in the
renewable energy output and demand side forecasts. This
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 137
approach is also seen as a way to minimize inconvenience or
impacts that occur from real-time control of devices or other
type of direct load control through traditional forms of demand
response.
There have been several research projects found related to
this topic and serve as a foundation for this project; however
the available research focuses on different objectives. These
include factors like solely minimizing the cost to the customer
and do not allow the utility to utilize the information to
optimize the grid and benefit all consumers served [2], [3], [4],
[5], or they focus on only one technology in the home [6], or
plan to operate in a continuous real-time manner [7], [8], [9],
[10] requiring unnecessarily high levels of computing power,
communication bandwidth, with a high potential to cause
customer inconvenience.
Previous work in this field has been performed, most
notably the work done by Bakker et al. in [7] and [11] which
presents a three step optimization methodology which includes
building a daily load shape for the house and iterating with the
utility for an optimal solution but makes operating decision for
all appliances in real time using a centralized controller. This
approach stops short of utilizing any inherent thermal energy
storage in the home and its appliances. Another noteworthy
project is presented by Li et al. in [2] which presents a method
to determine the daily energy usage of a home and optimize its
performance based on a learned thermal model of the home
from thermostat data. The research of this paper builds upon the
separate research performed by these two entities but also
includes major differences such as a) time horizons for
scheduling of different appliances, b) overall simulation goals
of incorporating renewable generation sources using demand
side resources and energy storage to supplement the grid rather
than having an additional local fuel based generation source c)
optimizing the grid rather than solely minimizing the energy
costs to the customer and d) utilizing the same pricing signal to
all customers at all times rather than steering customers
individually with customized cost signals. These four major
differences are seen as gaps in the present research in the area
and can be used to improve the work done previously while
also making it more relevant to current US energy market.
The first change (a) allows for sub-hourly planning of
individual appliances through the use of pricing signals,
increasing the granularity of accuracy while also adding rules to
the algorithm to prevent the systems from short cycling by
implementing a minimum run time. This also includes a more
real-time approach to send a second pricing scheme to energy
storage devices which can be used to compensate for errors not
seen the day before. The second change (b) increases the
complexity of the algorithm, since there is no longer a local
fuel source to compensate for errors; however the addition of
energy storage, both thermal and electrical, can be used. Also
by removing the local generation source, it aligns more closely
to the energy market in the US where large, central generation
plants are dominant and can allow for this type of system to be
provided as a service to the customer and the utility without
additional generation. The third difference (c) simply shifts the
overall focus of the research from a self-serving algorithm to
minimize the cost for one individual to a system which allows
the players involved to minimize their energy costs while also
working together to optimize the grid and decrease the amount
of fossil fuel based generation required to meet their needs.
Finally the fourth change (d) is done to maintain fairness to all
customers involved. This change increases the complexity of
the algorithms required but is seen as the only feasible option
for field implementation due to the amount of government
regulation in the utility industry and maintaining an unbiased
control algorithm.
The paper is organized as follows. First we introduce the
overall control strategy and discuss the merits and limitations of
it. Secondly, the modeling approach is discussed and broken
into work that has been completed and modeling that is still
planned. Finally we discuss future research plans for the
project.
CONTROL STRATEGY The proposed control algorithm is built using six major
steps.
1) The utility builds a day-ahead, sub-hourly pricing
schedule based on expected demand and energy
generation. This is very similar to how the grid
operates today and just assumes an increased level of
renewables.
2) It sends this pricing schedule to the customer’s HEM.
3) The HEM develops a schedule of appliance operation
based on historical customer usage, customer
preferences and cost of energy.
4) The customer’s HEM sends their sub-hourly load
shape back to the utility.
5) The utility aggregates this load shape information,
combines it with utility owned energy storage and
republishes updated sub-hourly energy costs.
6) Steps 1-5 are repeated iteratively until an acceptable
alignment of supply and demand are found.
These six steps give the utility and the customer a baseline
load shape, and the customer’s system decides when appliances
will operate based on historical data. However there are three
main concerns that come from this setup, the first is having
customer’s shifting all their energy usage to one time frame
where the lowest cost period is seen, thus making optimization
impossible. This can be avoided by adding a cap to the amount
of energy available at the lowest cost by introducing an
inclining block rate as described in [12]. This encourages
consumers – and their HEM – to spread out their energy
consumption to more evenly match the supply of generation to
the system. The second concern is compensating for errors in
both supply and demand forecasting. To compensate for this,
the control strategy will provide a separate, near real-time price
that is provided to energy storage devices, both thermal and
electrical e.g. water heaters, electric vehicles and battery energy
storage systems. This type of dual-pricing policy was
demonstrated in [13], which sets pricing based on different
electrical tasks – interruptible or non-interruptible and is
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 138
utilized to flatten a customer’s load shape. This differs from
our proposed control strategy, as the proposed strategy only
targets energy storage devices rather than all appliances in a
home and also has the goal of compensating for forecasting
error rather than simply flattening a customer’s load shape,
making it a more flexible platform. The third issue is a scenario
where an HEM would attempt to manipulate the system by
sending inaccurate data to the utility and force the energy price
down during hours of the day that only benefit them rather than
the system as a whole. This is addressed with the inclining
block rate to a certain extent, since a large consumption in a
period of time would cause planned usage over that amount and
increase the cost of energy for that customer. However to
discourage a system from projecting energy usage substantially
higher than is actually going to be used, a penalty for under
usage would then need to be enforced – this would require a
tolerance level be developed as to not punish well-meaning
customers. This will become a moot point after the system
reaches a large enough scale, because each customer is not
privy to other customer’s load shapes or information, so a large
over-bid from a single home will not impact the price and
therefore not be in the best interest of the customer to attempt
to mislead the system.
Overall, this method of control is chosen because it is
applicable to all types of systems on a neighborhood level
scale. That is, it can be applied to a traditional neighborhood
where there is no local renewable energy or storage or in a
location that has centralized or distributed and/or community
scale renewables and energy storage. The system is also
designed to optimize the grid with very little interaction from
customers themselves and each system learns over time to
match the customer’s preference in scheduling and balancing
their needs of cost with comfort and convenience. It should be
noted that the intent of this control strategy is not to be the only
solution for integrating renewable energy and is not designed to
handle fast response items such as frequency regulation. The
purpose of this strategy is to develop a low cost method to
optimize how the grid operates with increased renewable
generation and to provide customers the ability to take
advantage of this to lower their energy costs while also
benefiting the grid.
MODELING THE SYSTEM The control strategy described above must be modeled to
determine gaps and its effectiveness as a resource for
integrating renewable energy into the grid more effectively.
This section includes modeling that has been performed as well
as modeling that will be taking place over the next several
months. The future modeling section is based on a plan from
the knowledge that has been developed to this point, however
results from earlier modeling sections can influence and change
decisions as necessary.
The first step in modeling the system is to develop a
building energy model of a home and verify that it is thermally
accurate and representative of how a home would respond with
the control algorithms in place. To do so, multiple weeks of
circuit level sub-metered data and thermostat setpoints were
gathered from a home in the Birmingham, AL area where the
physical characteristics and appliance types are known. This
information was then used to develop schedules for appliances
such as the refrigerator, washer, dryer, dishwasher, etc. and also
used as a baseline to determine how well the model is
calibrated to the thermal characteristics of the home and the
simulated HVAC usage was compared to the metered data. The
home was initially modeled using BEopt 2.3 [14], a front-end
to the DOE’s EnergyPlus building energy simulation tool [15]
which was developed to assist engineers with introducing
energy efficiency upgrades to homes. However, since sub-
hourly schedules and other information was available, the
EnergyPlus text editor was also used to input more detailed
information and schedules to mimic the energy consumption
patterns seen in the metered data. A 3D sketch from BEopt 2.3
of the home is shown in Figure 1.
Figure 1. 3D Sketch of Model Home.
In addition to the home characteristics, to produce a
realistic model of the house, a weather file containing actual
measured data for the time period of the model is required. To
create this, weather data was gathered from the Iowa
Environmental Mesonet [16] website which includes historical
weather data for locations across the country. A second source,
SolarAnywhere [17], was also used to include solar radiation
data which was not present in the original weather dataset.
After the envelope, energy consumption of appliances and
weather data was developed, simulations were performed to
match the HVAC energy consumption and the whole home
consumption between the metered data and the simulation over
the time period available – August 5, 2015 – August 21, 2015
by changing unknowns in the makeup of the home such as
infiltration rates. After some minor envelope changes the
simulated and the metered load shapes began to line up. This
data is shown in Figure 2 and contains the simulated and actual
energy usage data measured for both the HVAC (top) and the
whole home energy consumption (bottom). As can be seen, the
lines do not match exactly as there are several small
introductions of error including the meters used for sub-
metering, minor physical characteristic differences of the home
and the model and local weather variations from the monitored
site.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 139
Figure 2. HVAC (Top) and Whole Home (Bottom) Usage, Measured vs. Simulated
Although the data does not align exactly from the model to
the metered data, it is seen to be very representative of the
home’s thermal characteristics. Data shown in Table 1
summarizes the differences in the total energy usage and peak
demand of both the HVAC system and the whole home. This
data shows a minimal difference between the simulated and the
metered data, demonstrating accuracy in the thermal
characteristics of the model.
Table 1. Comparison of Metered and Simulated Usage
Usage
(kWh)
Peak Hourly
Demand (kW) Percent Diff.
Metered HVAC 305 3.5 4.2%, 2.9%
Simulated HVAC 318 3.4
Metered Home 470 6.8 1.7%, 14.2%
Simulated Home 478 5.9
Once the model has been validated to be thermally
representative of the home chosen, the next step to model the
project focuses on developing a starting point for a real-time
pricing (RTP) rate. This information will ultimately be utilized
as the day-ahead rate structure initially sent to the included
homes in the model. To compile this information, historical
hourly capacity energy price data from the ERCOT market [18]
was gathered to provide a proxy to correlate the energy demand
on the grid for each hour of the year.
The most recent year available for the ERCOT capacity
pricing data was 2014 and this data will serve as the foundation
for which RTP rates are chosen, however another important
factor in their determination is the renewable energy output. To
include this variability in the pricing model, five minute
interval data of simulated energy generation for both solar PV
and wind generation was gathered from the National
Renewable Energy Lab’s (NREL) study on the eastern
transmission renewable integration study [19] and their Wind
Prospector tool [20]. Two sites for wind (16 MW capacity
each) and two sites for solar PV (39 MW capacity each)
generation were chosen in the Birmingham, AL area. The most
recent data available for wind energy was from 2012 and that
for solar PV was from 2006. While it should be noted that none
of these three date ranges align, it was determined to be
insignificant since the modeling is based on typical weather
data and a typical year of performance rather than validating
against a known baseline.
A typical daily load shape for solar is relatively constant,
with the output rising as the sun comes up, peaking at around
noon and then decreasing output as the sun goes down. Wind
output is less predictable but tends to follow a consistent output
when averaged over longer periods of time. However, the
variations throughout the day are more important for this
research and can cause the energy price to vary from day-to-day
and potentially even minute-by-minute. This variability can be
seen in Figure 3 and shows the average percent output
compared to its maximum generating capacity over a fifteen
minute period.
Figure 3. Fifteen Minute Energy Output (%) [19], [20]
While understanding this variability exists with renewable
generation sources, for this project we can combine the four (or
however many sites were to actually exist) into one load shape
since we focus on how a distribution level system can respond
to the grid fluctuations rather than matching the output of an
individual renewable generation source. Figure 4 shows the
same day combined weighted output from the four generation
sources from Figure 3.
Figure 4. Combined Renewable Energy Output –Weighted [19], [20]
This data shows a smaller peak output of just over 50%
compared to almost 70% before but shows some smoothing of
the load shape, making predicting more feasible but still not a
highly accurate process. This introduction of error makes
electrical energy and thermal energy storage even more
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0:00 3:36 7:12 10:48 14:24 18:00 21:36
Per
cent
Outp
ut
(%)
Wind 1 Wind 2 Solar 1 Solar 2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0:00 3:36 7:12 10:48 14:24 18:00 21:36
Per
cent
Outp
ut
(%)
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 140
valuable and is the purpose of the proposed dual-pricing policy
to have these devices compensate for the inevitable error in
predicting output.
This renewable generation information must now be
incorporated into the pricing scheme described previously. To
do this, a correlation must be made between the percent output
and the cost increaseor decrease it causes. This was done by
creating a multiplier at each time step to be applied to the
baseline energy cost for the renewable energy. Since the
correlation will not be linear from zero to 100%, four evenly
distributed segments were chosen with different slopes and a
cross-over point at 50% output. This correlation with the
respective formulas can be seen in Figure 5.
Figure 5. Renewable Energy and Cost Curve
Correlation To combine the two sources of cost it now becomes a
simple algebraic equation where the ERCOT data is used for
the traditional generation load (67%), and the cost data found
from Figure 5 is used to represent the renewable generation
output (33%). These percentages were chosen to align with
California ‘s renewable portfolio standards [21] and are a
simplification of the breakdown of cost allocations. Other
variables necessary for this calculation are the cost to generate
renewable energy over the long term which was found to be
around $0,025/kWh [22] which is based on long term purchase
power agreements (PPA) for wind farms in place today through
the year 2040. Secondly, the ERCOT data is based on
wholesale energy costs and not delivered costs to the customer.
Data from the US EIA was found to show the average retail rate
for residential applications for the state of Texas to be
$0.1188/kWh [23] so a multiplier was developed to bring the
average wholesale energy cost for the year equal to the EIA
data. This information is summarized in Table 2.
Table 2. Variables Needed for Rate Setup
Variable Type Amount
Renewable Energy Base Cost [22] $0.025 /kWh
Renewable Gen. Percent of capacity [21] 33%
Multiplier; wholesale to retail [18], [23] 3.12
With this information in place, the fifteen minute rate
structure for the year can be cal calculated to include both
traditional and renewable energy costs as well as incorporating
a proxy for the amount of demand on the grid through the
ERCOT wholesale energy cost data. This information is shown
in Figure 6 where the red line represents the monthly load
shape of the renewable energy output as a percentage in March
(left axis) and the blue line is the corresponding monthly
average energy costs for each period of the day (right axis).
This information shows a strong correlation between renewable
energy generation and energy costs. However, it is not absolute
as there are locations on the graph where large amounts of
energy is being generated, but due to the ERCOT data, it is
known that the demand for energy is also high, therefore the
cost does not follow.
Figure 6. Profile of Energy Cost and Renewable
Generation Load Shape for March
The next source of data necessary to model the integration
system is a baseline load shape pattern for a home which will
be used to determine feasible times throughout the day where
appliances can operate without imposing an inconvenience to
the homeowner and to also account for human behavior in
energy consumption. The major source for this data comes
from a large load research project undertaken in the Pacific
Northwest region of the US, starting in April 2012 and running
through July 2014 by Ecotope, Inc. and funded by the
Northwest Energy Efficiency Alliance (NEEA) and Bonneville
Power Administration (BPA) [24]. This study captured fifteen
minute energy usage data for all types of electrical appliances
within 101 homes and was made publically available through
the original project.
For the purpose of this project a subset of homes were
randomly selected to be utilized. It should be noted that while
the information is valuable, the climate for the Pacific
Northwest is very different than the one in the Southeast US,
therefore only non-weather dependent end uses were
considered. For example, clothes washer and dryer, oven and
dishwashers were included but HVAC and water heating were
excluded and will be included in the model with the use of
Renewable
Energy Costs
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 141
thermostat changes rather than a fixed load shape. Additionally
other appliances are seen as non-interruptible such as a TV, PC
or lighting and are not controlled by the home controller either.
The interval end use load data and the interval cost data
discussed in this section are combined through the use of an
algorithm which takes the daily profiles and searches for the
combination of the lowest cost and the highest probability that
the customer would operate each appliance during that time
span.
FUTURE RESEARCH The research work presented in this paper is a foundation
that will be expanded upon under the same project. The next
step in modeling will include developing and automating the
process and algorithm by which the optimal time to schedule an
appliance to operate is found for the corresponding day of the
year. A separate rate structure will be developed based off the
one presented previously that will incorporate errors in the
forecasting methodology which will then be presented to
energy storage devices within the home to compensate. The
response (charge and discharge) will need to be developed to
optimize the storage potential and will be done by changing
temperature setpoints, charge rates, etc.
Finally all the controls and pricing schemes will be
modeled together with approximately ten homes at once. This
system model will be developed utilizing the Building Controls
Virtual Test Bed (BCVTB), a software environment which
allows for co-simulation between different software packages
[25]. The BCVTB tool will be utilized to link, in real-time,
EnergyPlus and Matlab and allow them to exchange relevant
information to incorporate the necessary short-term
optimizations while also providing an actual building
simulation to model the day-ahead schedules and determine
how changes within the home impact energy consumption and
comfort as a whole and how this can be used to optimize the
grid while reducing the energy costs for the consumer.
This simulation model will also allow for an iterative
approach where the energy consumption and load shape results
from a simulation will be fed into the beginning of the next
iteration to simulate how energy consumption changes in
response to updated energy costs.
SUMMARY The research project presented focuses on the day-ahead
time frame optimization of the grid based on traditional
forecasting techniques of energy demand along with methods of
how energy will be generated. In addition, a shorter time
horizon response to utilize inherent thermal energy storage
available on the grid as well as electrical energy storage to
compensate for longer term errors that arise will be utilized.
This dual time scale and technology focus is meant to address
gaps in current research and to be utilized by a utility provider
to allocate generation resources more effectively and increase
the overall efficiency of the grid while also being able to
compensate for errors in the longer term renewable energy
output and demand side forecasts. This approach is not meant
to address all issues related to integrating renewable energy
such as frequency response which must be performed in the
sub-second time frame but is meant to serve as an optimization
technique and improve the link between energy usage and
renewable energy generation, bridging a gap in today’s grid to
allow for higher levels of renewable energy to supply the
electricity needs of the country while also minimizing or even
eliminating the amount of renewable energy generation that
must be curtailed due to lack of demand side optimization.
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UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 143
Proceedings of the Fifteenth Annual Early Career Technical Conference The University of Alabama, Birmingham ECTC 2015
November 7, 2015 - Birmingham, Alabama USA
SOLAR SIMULATOR MIXED TEMPERATURE TEST OF WIRE MESH PARTICLE HEATING RECEIVER TO MEASURE RECEIVER EFFICIENCY
Matthew Golob, Clayton Nguyen, Sheldon Jeter, Said Abdel-Khalik
Georgia Institute of Technology Atlanta, Georgia, USA
ABSTRACT
Solar receiver technology is exploring new possibilities,
particularly in pushing higher temperature ceilings to improve
power cycle efficiency. This move has shifted solar receivers
from troughs using heat transfer oils to central receivers
utilizing molten salts that can capitalize on the higher
concentration flux and resulting higher output temperature.
While molten salts have allowed for high receiver temperatures,
they are limited by cost and composition. The higher
temperatures and potentially resulting higher efficiency can be
reached by utilizing particulates instead. Particulates such as
commercially available aluminia based products offer two key
benefits. (1) Unlike typical molten salts, which are restricted
by phase changes to operating ranges between 240°C to 565°C
[1], these ceramic particles are stable to over 1000°C [2] and
ID50-K melt at 2200°C [3]. (2) While not a fluid, they can still
be flowed through a structure allowing for much better heat
exchange regimes.
To explore this alternative potential, an experimental trial
at Georgia Tech has been further developed [4] to assess the
efficiency and effectiveness of a heating receiver employing
particles. The basic approach of the receiver is to drop particles
vertically through the irradiated space in order to heat them.
Instead of simply dropping the particles through the heated
zone, a chevron wire mesh is employed in the receiver to slow
the free fall of particles, increasing residence time and
temperature rise per fall length. The overall experiment uses a
calibrated source of concentrated radiation from a solar
simulator as the energy input and measures the energy
collection from the temperature rise in a mass flow rate of
particulate dropped through the test receiver. With a measured
energy rise of the particulate and known energy input, the
particle heating receiver efficiency can be calculated.
The results here are from data collected using a small scale
receiver employing an improved mixer stage to achieve a better
exit flow measurement. The apparatus utilizes the Georgia
Tech Solar Simulator, which uses a bank of high intensity
xenon lamps to simulate a concentrated solar source. These
results show the thermal efficiency of a small scale particle
heating receiver from 35-160ºC to be around 85-92%.
INTRODUCTION
The experiment covered in this paper is an ongoing
iteration of test receiver designs utilizing Georgia Tech’s Solar
Simulator (GTSS). What is learned here is used to support key
components of a larger-scale receiver design involving the
Department of Energy’s SunShot program. The GTSS consists
of a bank of 7 xenon lamps, all focused down to a point
approximately 80 mm in diameter, Figure 1. This device serves
as a convenient artificial concentrated solar irradiation source
and can output concentration ratios well in excess of 1000 suns.
Figure 1. GTSS test of Focal Plane
Figure 2. Former recirculating OLDS elevator
configuration
Improvements on the previous particle heating receiver
(PHR) apparatus resulted in a switch from a recirculating
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 144
OLDS elevator configuration, Figure 2, to a large single pass
hopper particle system for better temperature stability and mass
flow sensing. The previous setup also tested likely receiver
designs, covering two receiver configurations: a simple free-
falling curtain and a chevron wire-mesh design to inhibit free
flow. These will be repeated and further studied. The GTSS
has also undergone considerable improvements to correct some
electrical issues as well as refinements in the operation,
alignment, and focus of the lamps. As a result a more nearly
uniform (but not perfect) hot spot can now be generated with
about 80% of the incident radiation falling within an
approximately 80 mm diameter circle. The updated apparatus
can simulate the high fluxes expected in practical operations
(~250 to as much as 2000 kW/m2).
The current apparatus has been developed to provide a
single-pass high-temperature transient test employing the
GTSS. This apparatus consists of a 25.4 L supply hopper that
employs a knife valve to remotely release particles through the
PHR test unit. Another identical 25.4L hopper is set up on a
scale to capture the particles that pass through the PHR test
unit. The hoppers are interchangeable and are designed for
repeated use, allowing for the pre-heated particles of a previous
run to be used as the inlet particles for a subsequent run through
switching the locations of hoppers after a test. During testing a
fixed flow rate into the irradiated test receiver region located at
the focus, Figure 3, thereby simulates the operation of a small
representative subset of a larger PHR. The flow rate is regulated
utilizing a perforated plate, which simultaneously disperses and
controls the flow to prevent regional overloading in the receiver
as well as ensuring a constant flow rate in the saturated state.
Figure 3. Receiver Test Region
An important addition to the PHR test unit assembly been a
mixer section. In the prior experimental iteration [4], local
differences in the flux exposure caused both a lateral and depth
wise gradient in the resulting mass flow of heated particles. To
more clearly determine the overall PHR efficiency, it was
deemed necessary to establish a mixed temperature of the
exiting PHR flow. The solution came in the form of a series of
linearly converging funnels, with each stage oriented 90º off
from the one above (see Figure 4). The results show that four
of these stages could produce a well-mixed exit flow
temperature particularly if the particle inlet temperature to the
PHR was uniform. Further measurement improvements include
a better thermocouple placement and catch design for getting a
clean particle saturated flow temperature. The results of this
setup to measure PHR efficiency are what will be reported in
this paper.
Figure 4. Model of two of the mixer stages
LITERATURE REVIEW One purpose of the SunShot project is to explore and
improve the particle heating receiver component of the solar
concentrator power cycle. This is a relatively new field of
research, so there are a limited range of studies with which to
compare.
Previous research at SNL [5] has examined alternative
receiver designs for a high efficiency particulate solar receiver.
These designs feature a receiver cavity box with a free falling
particulate curtain. Their work focused on varying the cavity
depth, the ceiling slope angle, the specular properties of the
walls and the back geometry in order to improve the efficiency
of the solar receiver. In comparison to their base receiver design
with a vertical aperture, the new design increases the theoretical
thermal efficiency from 72.3% to 86.8%. SNL’s work on the
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 145
free falling curtain is a core PHR candidate for the SunShot
program. The main drawback is the significant particle
acceleration of the free fall setup, a downside of which the
chevron mesh design seeks to eliminate.
Tan et al. [6] also looked at falling particle receivers. Their
research mainly dealt with computational simulation of wind
effects on the receiver. An aerowindow was a suggested
addition to the aperture of solid particle solar receivers. The
aerowindow acts as an air curtain which would help prevent the
loss of heated air to the ambient conditions. In addition, it has
the added advantage of ensuring particle retention. While
noteworthy ideas, there is little here in terms of physical tests or
measurements against which to compare receiver efficiencies.
Xiao et al. [7] is perhaps one of the closest experiments to
ours, mainly through the use of lamps and physical receiver.
Zhejiang University used a similar Xenon-arc lamp bank as a
solar simulator to test a spiral solid particle solar receiver. This
receiver cavity resembled a sunken helical spiral and employed
a top facing aperture with a glass window cover. The receiver
was experimentally measured to achieve a temperature rise
exceeding 350°C for a single pass with a 19.3 kW/m2
focal flux
off the lamps. The receiver had an optical efficiency of 84%
and a thermal efficiency of 60%. The general setup is somewhat
similar to our test, although there are significant differences in
receiver geometry, orientation, and particle flow regimes.
Rӧger et al. [8] looked at different Solid Particle Receiver
(SPR) designs utilizing varying particle recirculation schemes
in order to maximize the particle heating. The general particle
recirculation schemes focused on increasing particle residence
time to in turn increase the thermal efficiency of the receiver.
While the studies do not rigorously account for convective
losses, they did highlight the need to increase particle residence
time in the receiver.
EXPERIMENTAL SETUP
The chevron mesh design to be tested is also similar to the
inlet region of a falling curtain in terms of average particle fall
speed. As an objective, the ultimate form of the system will be
able to investigate high-temperature collection efficiency and
provide empirical data to support detailed computer modeling.
The basic design for the system can be seen in Figure 5. The
cone of light shown in the image illustrates a representative
inlet cone from the seven solar simulator lamps. To achieve a
suitable efficiency determination, three key measurements had
to be taken. First the quantity of thermal input into the PHR
cavity from the lamps had to be established. This was done
using a water cooled calorimeter. Next the mass flux rate of
particles passing through the PHR needed to be recorded. This
was accomplished by controlling the inlet area and capturing
the mass flow via scale measurements. The remaining critical
measurement is of the temperature change across the PHR test
section, where the addition of a mixer stage was important in
establishing a clean particle exit temperature. The calorimeter,
PHR mass flux, and PHR temperature difference were the key
parameter measurements to be set up for this experiment.
Figure 5. Single pass test apparatus
Calorimeter
To determine the incident solar irradiation the GT SunShot
Research Team has been assisting the GTSS operators in
developing a water cooled cavity calorimeter, similar to the one
produced by Groer and Neumann [9], to accurately measure the
amount of concentrated radiation being delivered to the
receiver. For the mixed temperature PHR tests conducted with
the single pass receiver, one and three lamps were used.
Figure 6: Calorimeter on Test Stand with Iris Plate
The calorimeter consists of a copper surface coated in high
absorptivity black. When an iris plate is mounted to the front of
the calorimeter, the core serves as a black body, Figure 6. The
core is further swathed in insulation to minimize heat loss. The
assembly is encased in a large steel pipe to provide support as
well as mountings for the iris plate. Line water is run through
copper tubing in the assembly with temperature taps taking the
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 146
incoming and outgoing water stream measurements. A positive
displacement flow meter is also located on the water line to
gauge the water flow rate. The resulting heat inputs of the
lamps are determined by the following equation.
in,out,wp,wInput ww TTcmQ
(1)
where Input
Q is the calculated heat rate from the lamp, wm
is
the recorded flow rate of the line water, wpC , the specific heat
of water, outwT , the measured outlet water temperature, and
inwT , measured the inlet water temperature. In order to apply a
lamp’s heat rate to a different experimental setup, an image of a
Lambertian target at the same focal plane as the calorimeter
was taken. The intensity of the light recorded in the image was
then calibrated to the measured heat rate for that plane at the
diameter of the calorimeter iris. With this and a new image of
the PHR as well as the new iris diameter on the receiver, an
equivalent heat input rate could be correlated to the test wire
mesh PHR. At that time the average of the best apparent image
data and available calorimetry measurements gives a heat rate
of ~4.7 kW into the PHR from three lamps and ~1.7kW for one
lamp operation.
PHR Mass Flux Rate
In order the accurately measure the PHR efficiency it was
critical to determine and control the mass flux passing into the
PHR cavity. To accomplish this, a scale base was characterized
and calibrated over the range of mass available for the PHR
tests. Once calibrated, the scale was used in conjunction with a
series of porous plates to measure the rate of change of the
mass accumulating within the hopper that catches all of the
PHR discharge. To meet the target mass flux goals the porous
plates were modified to particular open area ratios yielding
differing mass flow rates over the same constrained flux area.
The resulting mass flow rate was determined from analysis of
the transient mass accumulation recorded by the scale base.
This, in conjunction with the fixed flux area, allowed the PHR
mass flux to be established.
Base Scale Calibration
Mass calibration of the scale base used to measure the
mass flux rate was done incrementally using an existing hopper
filled with ID50-K particulate and an empty hopper set on a
scale base mounted in the planned PHR test configuration. A
beaker full of ID50-K was then taken out of the full hopper
placed on an OHAUS GT8000 (±0.1g) scale, where it was
weighed and recorded with the beaker’s mass tared out, then
poured into the empty hopper. This process was repeated until
the empty hopper on the scale base was filled to ~75 kg. A few
extra weights were added to increase the calibration range in
case of future modifications to the hopper. This yielded a
calibration for the scale base accurate to within ±2.5 g and
±0.21g/s transiently over the needed test range.
Figure 7. Controlled Perforated Discharge Area
A funnel guide was used to constrain the discharge area of
the perforated plate. The holes within the perforated plate were
controlled to an open area (OA) ratio of 41.5% with pattern of
0.156” diameter openings to meet task mass flux target. The
source perforated plate had 0.156” diameter holes with 0.219”
spacing for an open area ratio of 46%, Figure 7. Seven holes
were blocked to get the open area ratio to 41.5% which yielded
a suitable mass flux rate.
To keep the receiver tests consistent between the large
scale and small scale simulator tests, the particle mass flux of
the GTSS is targeted to be between 65 and 75 kg/m2-s. The
GTSS setup uses the manually calibrated scale base load cell in
order to measure the mass in the outlet hopper. Post processing
is then used in order to transiently measure the mass flow and
flux rates. The rate of change in the load cell signal is
calculated using a five point stencil as shown below,
avg12
218
18
2
t
iiii
dt
id
(2)
where tavg represents the average time differential between each
measurement, ε is the signal voltage and i is the index for each
measurement. A calibration constant Km is then used to convert
the data into a mass flow rate.
Cal
Kdt
dm
(3)
The length, L and width, W of the inlet is used to calculate the
mass flux rate, ṁ” through the receiver.
LWmK
dt
dm
1
(4)
By taking a graph of the accumulated mass against the time one
can see that the average mass flow rate is 0.13 kg/s with an R2
value well above 0.90, indicating a steady mass flow rate,
Figure 8. This lead to an average mass flux rate of 67.68 ± 0.39
kg/m2-s.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 147
Figure 8. Mass Accumulation over Time for 41.5% OA
Utilizing the five point stencil the mass flux rate can be
transiently calculated for each measurement. For the mass flux,
the open area ratio of 41.5% with an average mass flux of 67.68
kg/m2-s fell within the SunShot project target flow parameter
of 65 to 75 kg/m2-s.
PHR Temperature Capture
As had been noted in earlier iterations of this solar
simulator PHR experiment [4] there were issues with capturing
a clean PHR exit temperature. The previous method used a
mesh with a grid of thermocouples to get a characterization of
the exit flow. The non-uniformity of the grid thermocouples
brings issues in how best to characterize weight each due the
local mass flow rates. An answer was to instead mix the exiting
stream, getting the complete mass flow rate of particles leaving
the PHR at a uniform mixed temperature.
Figure 9. Particle mixing in a linearly converging funnel
stage
Unlike true fluids, mixing particle flows is fairly difficult.
Particle flows tend to behave in an extremely laminar fashion,
with little turbulent mixing between layers. The employed
mixing solution came in the form of a linearly converging
funnel stage. This constriction allows two captured streams of
flowing particles coming off the bottom of the funnel to freely
collide and turbulently mix, Figure 9. Each stage allows
mixing along one axis. By rotating the subsequent stage by 90º
a more thorough mixing was achieved. Testing showed that
after four stages, particles with a profile temperature similar to
that passing through an irradiated PHR would have a uniform
temperature in the exit stream.
Figure 10. Looking from underneath up at mixing stages
and wire mesh thermocouple catches
To measure the flow exiting the mixing stages, a set of
wire mesh thermocouple flow catches were designed, Figure
10. These catches ensured that the thermocouple beads remain
submerged in particle flow avoiding disruptive air influences
while still allowing for flow bypass to avoid choking and
stoppage issues. Overall this setup in conjunction with
thermocouples located on the controlled perforated discharge
area, Figure 7, permitted suitable temperature readings across
the PHR test unit.
PHR Test Unit
To test the core PHR design concept, a small scale receiver
was built for experimenting in the Solar Simulator Lab. The
receiver’s back wall is 0.102 m by 0.203 m and acts as a
representative portion of a larger receiver that will be used at
SNL. The receiver space is filled with 10 mesh wire chevrons
that slow the falling particles in the irradiated zone. The
targeted focal plane is an inch off the back wall towards the
receiver aperture. According to the GTSS team’s simulator
modeling, that spot should receive approximately 80% of the
irradiative power provided by the lamps. The iris plate that
covers the front of the receiver was built to allow the 80%
portion of the light in while shielding rest of receiver from the
remaining incident irradiation. This also protects the
thermocouples near the receiver from any direct irradiative
exposure.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 148
Figure 11. Solar simulator test PHR with water cooled
shield
The single-pass solar simulator is being tested using ID50-
K particulates. The ID50-K is a primarily alumina comprised
particulate [3] that has high absorptivity and is the candidate
medium for the SunShot PHR. In order to run this test, a water-
cooled Lambertian shield was placed in front of the receiver to
protect it until the lamps reached steady state operation, Figure
11. A few seconds prior to the shield’s retraction, the valve
controlling the particulate flow would be opened to allow for
ID50-K to start passing in a steady flow state through the
receiver. This would begin a test run. The run ends when the
top hopper is nearly exhausted.
MEASUREMENTS Three K-type Thermocouples with a ≥±0.2°C accuracy are
placed into the perforated discharge area to measure the
particulate temperature as it enters the PHR. Four more K-type
thermocouples (also ≥±0.2°C) with wire mesh catches are set at
the bottom of the mixer stages downstream of the PHR to
measure the exiting particle stream temperature. As testing
proceeded it was discovered that a growing temperature
gradient experienced with the hopper inlet particulates makes it
difficult to measure a stable temperature difference across the
PHR at increasingly elevated temperatures.
The efficiency of the receiver was calculated by comparing
the assessed input heat rate from the lamp, Input
Q , to the
measured heat rate gain seen in the particulates, Part
Q
in,out,pp,pPart pp TTcmQ
(5)
The heat rate gain of the particulate is calculated by, , which
is measured mass flow rate of the particulate into the base
hopper, p,pc , the specific heat of ID-50K, out,pT , the average
mixed stream temperature at the receiver discharge or mixed
hopper temperate, and in,pT , the average incoming particulate
stream temperature from the top hopper. The resulting
efficiency is calculated by equation 6.
Input
Partr
Q
Q (6)
where the calculated receiver efficiency is the heat rate gain of
the particulate divided by the heat input rate from the lamp.
Solar Simulator PHR Efficiency Testing The Solar Simulator Lab was used to test the small scale
PHR for a for a series of 5 runs with the average temperature
ranging between 35°C to 160°C. The current test apparatus is
an updated iteration replacing the temperature limited Olds
Elevator recirculation method with a single pass insulated
hopper design. These tests were run consecutively using the
batch processing setup to gather receiver efficiency values for
progressively higher temperatures.
The first run is used to calculate the optical efficiency; as
such this test was conducted using only one lamp,
approximately 430 suns. The following five runs were
conducted using 3 lamps, approximately 1050 suns. Subject to
the chosen specific heat, the efficiency for the runs with stable
temperature differences (the initial two in the table had PHR
efficiency’s between 85-92%.
Table 1. Receiver efficiency values for different specific heat sources
The range of calculated efficiencies was dependent on the
choice of various experimental and theoretical methods for
finding the specific heat, Figure 12. SNL specific heat was from
a test conducted using a NETZCH Simultaneous
Thermogravimetry - Differential Scanning Calorimeter (DSC).
The semi-empirical calculation employed IUPACK and NIST
data along with application of the Kopp-Neumann material
compositional law for the specific heat. Clemson University
(CU) had also conducted 4 runs on ID50-K using a NETZCH
Differential Scanning Calorimeter (DSC). The Clemson
specific heat is based off an empirical polynomial regression
model of the four different runs. The DSC used at CU was also
used to measure the specific heat of alumina powder for use as
a calibration standard on the device. This uncertainty was then
used to find the uncertainty of the specific heat measurements
of the data from CU for the ID50-K.
UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 149
Figure 12. Specific heat of ID50-K from using NETZSCH
DSC 404C, NETZSCH STA 409 C/CD and Kopp-Neumann
The first two runs had the most reliable measurements due
to the uniform ambient temperature of the ID50-K in the inlet
hopper. Each efficiency is calculated assuming no uncertainty
in the specific heat model. Consecutive runs have shown that
while an accurate mixed outlet temperature can be measured,
the inlet hopper naturally develops a thermal gradient due to the
ambient conditions of the outlet hopper. As the tests progressed
this lead to progressively larger thermal gradients resulting in
unreliable inlet temperature measurements, which contributed
to the relatively high receiver efficiencies in later runs.
Table 2. Temperature data for the different runs, showing progressively larger non-uniformity at the inlet
Over the consecutive runs the data shows that the change
in average particle temperature begins to decrease between
runs. This is because the valve that is used creates a thermal
leak as well as the thermal mass of the hopper itself. The heat
leak in the experiment, in addition to the non-uniform
temperatures caused by these leaks makes it unrealistic to use
repeated batch runs to reach higher temperatures.
CONCLUSION
The preliminary results of the test show that the discrete
structure receiver using a size 10 mesh can potentially achieve
receiver efficiency greater than 85-92% depending on what
specific heat is used. However, to test the PHR at higher
temperatures the inlet reservoir will need to be preheated to
500°C using a combination of band and air/mixer heater. These
heaters will be kept at a constant temperature using a solid state
relay with PID control over a prolonged period of time. To
ensure a uniform temperature profile within the hopper
reservoir, an air ejector is being modified for use as a small
scale non-mechanical particle mixer. The air that is used within
the ejector will also be preheated to prevent overly cooling the
particles. With these changes, upcoming tests will further focus
on repeating runs at elevated temperatures and comparing runs
with altered mesh layouts as well as a free falling configuration.
Overall these measurements will determine the PHR efficiency
over a wider range of conditions and guide the design path of
the SunShot’s large scale PHR.
ACKNOWLEDGMENT Financial support of the US Department of Energy through the
SunShot research program is recognized and appreciated.
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