4 th International Conference On Building Energy, Environment Design optimisation of a double pass PV/T-solar air heater integrated with heat pipes W. Fan 1 , G. Kokogiannakis 1 and Z. Ma 1 1 Sustainable Buildings Research Centre (SBRC), University of Wollongong, Wollongong, NSW 2522, Australia SUMMARY A multi-objective design optimisation strategy for a novel design of hybrid photovoltaic thermal collector-solar air heater (PVT-SAH) system integrated with heat pipes and fins is developed to maximise its thermal and electrical efficiencies. A dynamic model of the hybrid PVT-SAH system is first developed. A sensitivity study using global sensitivity analysis (GSA) is then implemented to identify the significant parameters to the objective functions. Lastly, the multi-objective optimisation problem is formulated using the key parameters identified, objective functions defined, genetic algorithm optimization technique and a decision-making method. The results showed that the GSA effectively reduced the optimisation size from 13 to 7. For the PVT-SAH system with different lengths, both the thermal efficiency and electrical efficiency were improved by 3.2-67.5% and 9.9-25.2% respectively, in comparison to a baseline design. The thermal efficiency of the optimised system was ranged from 53.5 to 60% for different lengths of the PVT-SAH system. INTRODUCTION Heat pipe is a heat transfer enhancement technology which utilises evaporation and condensation of the inner fluid to transfer a large amount of thermal energy. Although the heat pipes have been widely applied in cooling of electrical devices, there are a limited number of studies reported on the integration of heat pipes with photovoltaic thermal collectors (PVT) or solar air heater (SAH) systems (Gang et al. 2011; Wu et al. 2011). In this study, a novel design of hybrid PVT-SAH system integrated with heat pipes is developed (Figure 1 and Figure 2) to increase the outlet air temperature in order to drive rotary desiccant cooling systems. The hybrid system can be decomposed into two subsystems for the convenience of analysis: a PVT module and a SAH module (Figure 2). The PVT module consists of the glass cover 1, PV panel, back plate, heat pipes and the lower air channel. The SAH module includes the glass cover 2, absorber plate, upper channel and the inserted longitude fins. When operating, the ambient air is first circulated to the lower channel where it is heated up by the heat generated from the heat pipes. The heated air is then circulated to the upper channel where the absorber plate of SAH will further increase its temperature (Figure 2(b)). The PVT and SAH modules are linked by a connection layer (using insulation and adhesive materials) and the heat exchanges between upper and lower channels could also occur through this connection layer. The design of this PVT-SAH system includes various design parameters such as geometric and operational factors, and the effects of these parameters on the thermal and electrical performance of the system are usually nonlinear and complex. Improper selection of the values of these parameters could decrease the performance of such systems and further affect the feasibility of using PVT-SAH to drive rotary desiccant cooling systems. In this study, the performance of a hybrid PVT-SAH system was optimised to maximise its useful thermal efficiency and net electrical efficiency. An optimisation strategy using a mix of global sensitivity analysis (GSA) and genetic algorithm (GA) was used to facilitate the identification of the optimal design. Figure 1. Overview of the hybrid double pass PVT-SAH system integrated with heat pipes Figure 2. Section-view of (a) the hybrid PVT-SAH integrated with heat pipes; (b) the upper and lower air flow channels ISBN: 978-0-646-98213-7 COBEE2018-Paper277 page 834
6
Embed
International Conference On Building Energy, … · A multi-objective design optimisation strategy for a novel design of hybrid photovoltaic thermal collector-solar air heater (PVT-SAH)
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
4th International Conference On Building Energy, Environment
Design optimisation of a double pass PV/T-solar air heater integrated with heat pipes
W. Fan1, G. Kokogiannakis1 and Z. Ma1
1 Sustainable Buildings Research Centre (SBRC),
University of Wollongong, Wollongong, NSW 2522, Australia
SUMMARYA multi-objective design optimisation strategy for a novel
design of hybrid photovoltaic thermal collector-solar air
heater (PVT-SAH) system integrated with heat pipes and fins
is developed to maximise its thermal and electrical
efficiencies. A dynamic model of the hybrid PVT-SAH system
is first developed. A sensitivity study using global sensitivity
analysis (GSA) is then implemented to identify the significant
parameters to the objective functions. Lastly, the
multi-objective optimisation problem is formulated using the
where 𝐼𝑡 is the total radiation (𝐽) on the PVT-SAH system
during a time step; 𝑁 is the total time steps; ∆𝑡 is the length
of a time step (𝑠); 𝑗 is the 𝑗𝑡ℎ time step; 𝑇𝑜𝑢𝑡 and 𝑇𝑖𝑛 is
the outlet and inlet air temperatures (℃) respectively; 𝐶𝑓
and 𝑚𝑓 are the heat capacity (𝐽/𝑘𝑔 ∙ 𝐾) and mass flow rate
(𝑘𝑔/𝑠) of flowing air, respectively.
Objective function 2 (net electrical efficiency):
𝑒𝑓𝑓𝑛𝑒𝑡−𝑒𝑙𝑒 = ∑ (𝑄𝑒𝑙𝑒𝑗
− 𝑄𝑓𝑎𝑛𝑗
)𝑗=𝑁𝑗=1
∑ 𝐼𝑡𝑗𝑗=𝑁
𝑗=1⁄ (3)
where 𝑄𝑒𝑙𝑒 is the electricity ( 𝐽 ) generated by the PV
system; 𝑄𝑓𝑎𝑛 is the electricity (𝐽) consumed by the fan. The
friction coefficient used to calculate the 𝑄𝑓𝑎𝑛 can be found in
Bergman and Incropera (2011).
2.4. Design parameters and constraints
The number of the parameters considered for the design of
the hybrid PVT-SAH system is 13 (Table 1), which can be
grouped into three categories including geometrical, material
related and operational parameters. Defining the values or
ranges of the design parameters is crucial to preventing the
infeasible optimisation solutions. In this study, the values or
ranges of the design parameters were determined from
literature review and in case where there are no previous
published values, the ranges were set as wide as possible. A
summary of the considered design parameters and their
constraints is presented in Table 1. It is worth to note that the
solar properties of PV cells and absorber plate were fixed
and not optimised in this study. The PV cells considered in
this study were polycrystalline silicon with a solar absorption
of 0.9 and the reference average electrical efficiency of 18%.
Using solar selective coating, the absorber plate has the
solar absorption and emissivity of 0.95 and 0.05,
respectively.
Table 1 The specification of the candidate design parameters of the
hybrid PVT-SAH system
Candidate design parameters
Constraints Remarks
Material (Design 2006)
Steel; copper and aluminum
The material used to construct the absorber/fins and bottom plate which affects the input values of physical-thermal properties in the thermal model of PVT-SAH (such as thermal conductivity, heat capacity and density) .
PV covering factor 𝑟_𝑝𝑣
[0.1,0.9] The ratio of 𝑊_𝑃𝑉 to (𝑊_𝑃𝑉+𝑊_𝑆𝐴𝐻) as shown in Figure 2.
Number of fins 𝑁_𝑓𝑖𝑛
[1,50] The number of fins over the width of SAH (𝑊_𝑆𝐴𝐻) as shown in Figure 2.
Thickness of air gap 1 𝑡_𝑔𝑎𝑝1(m)
[0.01, 0.08] As shown in Figure 2
Thickness of air gap 2 𝑡_𝑔𝑎𝑝2(m)
[0.01, 0.08] As shown in Figure 2
Thickness of fins 𝑡_𝑓𝑖𝑛(m)
[0.001, 0.003]
As shown in Figure 2
Thickness of absorber plate 𝑡_𝑝(m)
[0.001, 0.003]
As shown in Figure 2
Thickness of glass cover 𝑡_𝑔𝑙𝑎𝑠𝑠
[0.001,0.005] As shown in Figure 2
Number of heat pipes (𝑁_ℎ𝑝)
[10, 220] The number of heat pipes installed along the length of PVT-SAH system.
4th International Conference On Building Energy, Environment
Channel depth of lower channel 𝐻_𝑙𝑜𝑤𝑒𝑟(m) (Sun et al. 2010)
[0.01, 0.1] Channel depth of the PVT module.
Channel depth of the upper channel (𝐻_𝑢𝑝𝑝𝑒𝑟) (m)
[0.01, 0.1] The distance between the absorber plate and the bottom plate as shown in Figure 2.
Thickness of the connection layer (𝑡_𝑙𝑖𝑛𝑘) (m) (Board 2003)
[0, 0.1] The thickness of insulation layer between the SAH and PVT as shown in Figure 2. Glass wool was used in this study with a thermal conductivity of 0.04 W/m.KMass flow rate
(m_f) (𝑘𝑔 ℎ)⁄ [50, 700] -
2.5. Optimisation technique and decision-making method
Multi-objective genetic algorithm (MOGA) (Deb et al. 2000)
was used to optimise the significant design parameters that
were determined from the sensitivity analysis. The MOGA is
a specific class of genetic algorithm, which was developed
based on Pareto sorting technique. The optimisation will
result in a set of Pareto optimal solutions in a single run
which are non-dominated with respect to each other. The
non-dominated solution means the value of one objective
cannot be further improved unless sacrificing the other
objective. The advantage of the Pareto based multi-objective
optimisation is that it provides the insight of the trade-off
relationships among the several objectives and helps the
designer to make decisions. The MOGA therefore
overcomes many shortcomings of traditional aggregative
methods which combine all objectives into a weighted-sum
and only result in a single optimal solution.
The decision-making is essential for multi-objective design
optimisation problems to identify the final optimal design
from the set of Pareto fronts. The decision-making process is
generally performed depending on the engineering
experience or which objective function is more important to
the decision makers. In this study, the TOPSIS method is
used to determine the final optimal design. Two hypothetical
points, namely ideal point and worst point, were assumed to
assist in the decision-making process. The Pareto front
which has the shortest geometric distance from the ideal
point and the longest distance from the worst point was
determined to be the final optimal design. As the objective
functions are of different dimensions and scales, it is
necessary to normalize the objective functions before
implementing the TOPSIS method. The details about the
normalisation and implementation of TOPSIS method can be
found in Hwang et al. (1993).
2.6. Mathematical modelling of PVT-SAH system
A dynamic model of PVT-SAH system was first developed for
performance prediction. As the design of the present
PVT-SAH system can be considered as the combination of a
heat-pipe PVT module and a finned SAH module, the
prediction model of the whole system can be developed by
coupling a model of a standalone heat pipe PVT system
(Gang et al. 2011) with a model of a finned SAH (Fan et al.
2017). The nature of the coupling was to consider the heat
exchanges between the upper channel and lower channel
through the connection layer and to consider the heat and
mass transfer from the outlet of PVT to the inlet of SAH. The
inlet air temperature of the lower channel (PVT) was equal to
the ambient air temperature and its outlet temperature was
assigned to be the inlet air temperature of the upper channel
(SAH). Due to the mass continuity law, the mass flow rate in
the upper channel was equal to that in the lower channel.
Based on the above assumptions, the corresponding
modifications were made on the energy balance equations of
the existing models (Fan et al. 2017) and (Gang et al. 2011)
for the bottom plate of the SAH and the flowing air node in
the PVT while the remaining energy balance equations were
kept the same as those reported in the literature (Fan et al.
2017; Gang et al. 2011).
The energy balance for the bottom plate of SAH became:
4th International Conference On Building Energy, Environment
optimisation strategy to optimise the hybrid PVT-SAH
system.
Table 3 The optimal design of PVT-SAH and the resulted
performance for different lengths of PVT-SAH system
Length of PVT-SAH system (m)
6 10 13 16 19 22 Optimal performance
η_th (%) 53.5 58.2 60 58.9 59.1 56.3
η_ele (%) 9.76 8.93 8.36 8.11 7.61 8.31
Optimal design of parameters
N_hp 79 93 145 176 161 202
r_pv 0.66 0.56 0.52 0.52 0.47 0.59
H_lower(m)
(m)
0.06 0.06
5
0.08 0.052 0.05
4
0.058
N_fin 13 16 23 31 25 16
H_upper (m)
(m)(m)
0.03
5
0.04 0.05
2
0.088 0.07
1
0.089
t_link (m) 0.1 0.1 0.1 0.1 0.1 0.1
m_f (kg/h) 155 285 370 430 472 596
4. ConclusionsThis study aimed at optimising the useful thermal efficiency
and net electrical efficiency of a heat pipe integrated
PVT-SAH system. For this purpose, a multi-objective design
optimisation strategy using genetic algorithm in combination
with a global sensitivity analysis (GSA) was proposed.
The main conclusions derived from this study are:
• The GSA was effective for dimension reduction. Seven
design parameters were finally determined to be
significant to the objective functions and were
considered in the optimization; • The construction material and the thickness of
construction components (except the insulation layer
between the upper and lower channel) did not have a
significant effect on the thermal and electrical
performance. This indicated that the light-weight
PVT-SAH and using the materials with a lower price
like steel are favored to reduce the upfront costs for
constructing the PVT-SAH system without lessening
the performance, although there are other factors that
could also affect such decisions (e.g. resistance to
corrosion);
• The optimal useful thermal efficiency and net electrical
efficiency cannot be obtained simultaneously. A
decision-making strategy was needed to choose a
compromised design as the optimal design;
• The optimised values of the useful thermal efficiency
and net electrical efficiency were in the ranges of
53.5-60% and 7.61-9.76%, respectively.
• The comparison study demonstrated that
multi-objective optimisation strategy was technically
feasible and could solve the optimisation problem to
maximise both thermal and electrical performance of
hybrid double pass heat pipe PVT- finned SAH
systems.
Acknowledgements The authors would like to thank China Scholarship Council
and University of Wollongong for supporting this study.
5. ReferenceBergman, T.L., Incropera, F.P., 2011. Fundamentals of heat and
mass transfer. John Wiley & Sons. Board, A.B.C., 2003. Building code of Australia. CCH Australia. Cannavó, F., 2012. Sensitivity analysis for volcanic source modeling
quality assessment and model selection. Computers &
Geosciences 44, 52-59. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T., 2000. A fast elitist
non-dominated sorting genetic algorithm for multi-objective
optimization: NSGA-II, International Conference on Parallel
Problem Solving From Nature. Springer, pp. 849-858. Design, C.G.A.E., 2006. The Chartered Institution of Building
Services Engineers. London. Fan, W., Kokogiannakis, G., Ma, Z., Cooper, P., 2017. Development
of a dynamic model for a hybrid photovoltaic thermal collector –
Solar air heater with fins. Renewable Energy 101, 816-834. Gang, P., Huide, F., Tao, Z., Jie, J., 2011. A numerical and
experimental study on a heat pipe PV/T system. Solar energy
85(5), 911-921. Hwang, C.L., Lai, Y.J., Liu, T.Y., 1993. A new approach for multiple
objective decision making. Computers & operations research