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Optimization of Cooling System for Data Center Case Study: PAU ITB Data Center
Mukaffi A.R.I.a, Arief R.S.a, Hendradjit W.a, Romadhon R.a
a Department of Engineering Physics, Bandung Institute of Technology, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 The Authors.Data center is a facility that consumes a lot of energy. From the total energy used by data center, IT equipments as its major component consume energy by 50%, and cooling system consumes energy by 37%. There are various strategies to improve the efficiency of the cooling system in order to save energy consumption. Because data center operates continuously, so that the potential of disturbance and shut down due to overheating should be avoided. Optimization is performed by using the Computational Fluid Dynamics (CFD) simulation. Assessment of PAU data center shows that the metric Rack Cooling Index (RCI) for data center cooling systemis 100% and the metric Power Usage Effectiveness (PUE) is 2.04. This value indicates that the energy consumption of data centercooling system is not efficient. Simulation results indicate the presence of mixing air between hot air and cold air and cause less efficient of the cooling process. Optimization is carried out by adding blanking panels, cold aisle containment, and changing the layout of the components and shelves. The result shows that the value of RCI achieved at 100% for RCIhigh and 99% for RCIlow. Meanwhile, the PUE value is reduced to 1.92. The energy used by cooling system is also reduced by 1.28kW.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Computational fluid dynamics simulations,Cooling process,Data center cooling,Data centers,IT equipment,Power usage,Power Usage Effectiveness (PUE),Rack cooling indices[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]cooling system,data center,energy efficiency,Power Usage Effectiveness,Rack Cooling Index[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.proeng.2017.03.088[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]