Energy efficient thermal management of data centers via open multi-scale design

Data centers are computing infrastructure facilities that house arrays of electronic racks containing high power dissipation data processing and storage equipment whose temperature must be maintained within allowable limits. The heat generated by the electronic equipment and the costs of powering the cooling systems in data centers are increasing continually. This requires the typical air cooling system in data centers to be designed more intelligently or augmented by other techniques. Having concluded that typical designs of air-cooling systems are not efficient and even adequate anymore for current and upcoming data centers, a research question is raised to identify and satisfy the needed design specifications and framework of new energy efficient thermal solutions, considering the design environment of the next generation data centers.;In this research, the sustainable and reliable operations of the electronic equipment in data centers are shown to be possible through the Open Engineering Systems paradigm. After the open design requirements of current air cooling and future multi-scale cooling systems in data centers are identified, a design approach is developed to bring adaptability and robustness, two main features of open systems, in multi-scale convective systems such as data centers. The presented approach is centered on the integration of three constructs: a) a Proper Orthogonal Decomposition (POD) based multi-scale modeling approach, b) compromise Decision Support Problem (cDSP), and c) robust design to overcome the challenges in thermal-fluid modeling, having multiple objectives, and inherent variability management, respectively. The method is verified to achieve an adaptable, robust, and energy efficient thermal design of an air-cooled data center cell with an annual increase in the power consumption for the next 10 years. The results show a 12-46% reduction in the energy consumption of the center in addition to being adjustable to the newer IT equipment and higher heat loads compared with a traditional design. Compared with an optimal solution, a robust solution can reduce the variability in the thermal response by 73.8% with only 7.8% increase in the center energy consumption.;Also, a design approach based on POD based modeling and power profiling of IT equipment is presented and used to bring adaptability and concurrency for coordinated minimization of cooling and IT power consumption in future open data centers. The results for a test case show the design approach results in 12-70% saving in the total energy consumption of the data center cell in different scenarios, compared with traditional design of data centers.;Two new POD based reduced order thermal modeling methods are presented to simulate multi-parameter dependent temperature field in multi-scale thermal/fluid systems such as data centers. The methods are discussed and compared with each other through application to similar data center cells. The method results in average error norm of ∼ 6% for different sets of design parameters, while it can be up to ∼250 times faster than CFD/HT simulation in an iterative optimization technique. Also, a simpler reduced order modeling approach centered on POD technique with modal coefficient interpolation is validated against experimental measurements in an operational data center facility. It is found that the average error in POD re-construction is 0.68 °C or 3.2%, compared with the experimentally measured data for two different values of CRAC flow rates.