Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm |
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Authors: | G Li M Li S Azarm J Rambo Y Joshi |
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Affiliation: | (1) Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA;(2) Shell Global Solutions, Houston, TX 77082, USA;(3) G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA |
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Abstract: | There is an ever increasing need to use optimization methods for thermal design of data centers and the hardware populating
them. Airflow simulations of cabinets and data centers are computationally intensive and this problem is exacerbated when
the simulation model is integrated with a design optimization method. Generally speaking, thermal design of data center hardware
can be posed as a constrained multi-objective optimization problem. A popular approach for solving this kind of problem is
to use Multi-Objective Genetic Algorithms (MOGAs). However, the large number of simulation evaluations needed for MOGAs has
been preventing their applications to realistic engineering design problems. In this paper, details of a substantially more
efficient MOGA are formulated and demonstrated through a thermal analysis simulation model of a data center cabinet. First,
a reduced-order model of the cabinet problem is constructed using the Proper Orthogonal Decomposition (POD). The POD model
is then used to form the objective and constraint functions of an optimization model. Next, this optimization model is integrated
with the new MOGA. The new MOGA uses a “kriging” guided operation in addition to conventional genetic algorithm operations
to search the design space for global optimal design solutions. This approach for optimal design is essential to handle complex
multi-objective situations, where the optimal solutions may be non-obvious from simple analyses or intuition. It is shown
that in optimizing the data center cabinet problem, the new MOGA outperforms a conventional MOGA by estimating the Pareto
front using 50% fewer simulation calls, which makes its use very promising for complex thermal design problems.
Recommended by: Monem Beitelmal |
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Keywords: | Data center Thermal design Multi-objective optimization Genetic algorithm Meta-modeling |
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