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when trying to decide which approach is bestfor a given data center. For example, cold
aisle containment is typically less expensive
to implement because perforated tiles are of-
ten located near the rack inlets and therefore
less duct work is required. But by fully con-
taining the cold supply air, the rack exhaust
drives the ambient room temperature up. De-
pending on the resulting room temperature,
this approach may not be comfortable for
service technicians or administration person-
nel working in the room.
The opposite problem occurs with hot aisle
containment, as the entire room becomes part
of the cold supply, driving the ambient room
temperature downward. In this scenario,
however, there is additional heat contributed
by other objects in the room such as walls,
UPSs, lights, and other equipment. The addi-
tional heat tends to increase the ambient tem-
perature in the room, but if the supply air is
well directed towards the rack inlets, the ad-
ditional heat will have less impact on the
equipment. Cost is also a primary decision
factor as containment strategies of any kind
require modifying the data center while in
operation.
Building virtual models of these two ap-
proaches can help ferret out which one is op-
timal for a given data center layout. While
complete cold aisle containment is possible
in a data center with a room return, complete
hot aisle containment is not, since it requires
a ceiling return. Thus two partial contain-
ment strategies were considered in which
impermeable walls are positioned at the ends
of either the hot or cold aisles. The modified
scenarios are shown in Figure 4.
Table 1: Comparison of maximum rack inlet and ambient room temperatures for 8 trials of thebaseline model where one CRAC was shut off for each trial; Simulation 4 generated the worstresults, Simulation 6 the best; Simulation 0 has all CRACs turned on
gions for the cold aisle containmentcase compared to two for the hot aisle
containment case. More contained
space may lead to reduced mixing
between the hot and cold air in the
room. For the cold aisle containment
strategy, the maximum inlet tempera-
ture drops by 4 degrees to 78°F, com-
pared to a drop of only 1 degree for the hot
aisle containment case. Partial cold aisle
containment leads to a 7 degree drop in the
maximum ambient room temperature as well.
Using partial cold aisle containment, the is-
sue of reducing power consumption by the
cooling system can be considered once again.
In Table 3, the results of a CRAC failure
analysis indicate that if the data center now
operates with CRAC C turned off, the maxi-
mum rack inlet temperature is the same as it
was in the baseline case with all CRACs on.
The maximum rack inlet temperature is still
above the ASHRAE recommended maxi-
mum value (80.6°F), but it is well below the
ASHRAE allowed maximum value (90°F).
This exercise is evidence of the importance
of using flow simulation to assess modifica-
tions to a data center
and determine
which, if any, cool-ing units can be dis-
abled to improve
data center effi-
ciency.Table 2 Comparison of maximum rack inlet and ambient room tem-peratures for cold aisle, hot aisle, and no containment strategies withall CRACs operating
Containment Method
Maximum Rack
Inlet Temperature (F)
Maximum Ambient
Room Temperature (F)
No Containment 82 91
Cold Aisle Containment 78 83
Hot Aisle Containment 81 89
Figure 4: a) Partial cold aisle containment and b) partialhot aisle containment, both achieved by placing imper-meable walls at the ends of the aisles
value. However, by adding a simplified par-tial cold aisle containment solution, the reli-
ability of the data center has been increased.
That is, while the data center can be run with
all 8 CRACs on, the results show that if any
unit except CRAC D fails or must be taken
down for servicing, the maximum rack inlet
temperatures will not exceed 90°F.
In summary, this particular data center was
used to illustrate how CFD can be used tocompare some of the many techniques avail-
able to improve PUE. When striving to im-
prove PUE, data center managers should fo-
cus on the Cooling Load Factor as a primary
target, along with the purchase of energy star
rated equipment. If the cooling power con-
sumption values are not readily accessible,
then focusing on the Cooling Capacity to IT
Load Ratio is a reasonable alternative. To
test if reductions in cooling are feasible, CFD
can be effectively used to compare and con-
trast alternative approaches. Of course, mod-
eling is not meant to be a substitute for good
engineering. CFD models are based on as-
sumptions, so the results should be validated
with measurements to ensure that the model
represents real world phenomena. Yet mod-
eling will always produce a relative compari-
son between one design approach with an-
other and is a helpful mechanism for support-
ing the decision making process.
The PUE metric is most heavily influenced
by the power to drive the IT load and the
cooling necessary to sustain the resulting
thermal load. By focusing on how the cold
air is delivered to the servers and the hot air
is returned to the CRACs, the thermal effi-
ciency of a data center can be improved sig-
nificantly. Understanding the air flow pat-
terns presents opportunities to reduce the ex-
isting cooling capacity and its related costs,improve the reliability of the data center, or
add more IT equipment to an existing data
center without the need to add more cooling
capacity. Any of these outcomes will also
reduce the overall data center PUE. By fo-
cusing on improving airflow, managers can
get more output from existing cooling capac-
ity without expensive capital expenditures
associated with adding or upgrading cooling
units. With today’s high density servers and
increased rack thermal loads, traditional
“back of the envelope” calculations are not
sufficient without the aid of a CFD modeling
tool.
Table 3: Maximum rack inlet and room temperatures using partial cold aisle containment for 8trials with one CRAC turned off for each trial; Simulation 3 yields the best results, Simulation 4the worst; Simulation 0 has all CRACs on