Transient Thermal Airflow Simulations in Data Center Using Multiple Graphics Processing Units

Sjölund, Johannes; Summers, J. L.; Ljung, Anna‐Lena

doi:10.2139/ssrn.4312676

Cited by 1 publication

(1 citation statement)

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“…The DC CFD model used in this work was first presented by Sjölund [17] and defines three-dimensional boundary conditions for the room, servers, and Computer Room Air Handler (CRAH) units. A conceptual schematic of the heat rejection is shown in Fig.…”

Section: The DC Simulation Modelmentioning

confidence: 99%

Adaptive Control of Data Center Cooling using Deep Reinforcement Learning

Heimerson

Sjölund

Brännvall

et al. 2022

2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

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In this paper, we explore the use of Reinforcement Learning (RL) to improve the control of cooling equipment in Data Centers (DCs). DCs are inherently complex systems, and thus challenging to model from first principles. Machine learning offers a way to address this by instead training a model to capture the thermal dynamics of a DC. In RL, an agent learns to control a system through trial-and-error. However, for systems such as DCs, an interactive trial-and-error approach is not possible, and instead, a high-fidelity model is needed. In this paper, we develop a DC model using Computational Fluid Dynamics (CFD) based on the Lattice Boltzmann Method (LBM) Bhatnagar-Gross-Krook (BGK) algorithm. The model features transient boundary conditions for simulating the DC room, heat-generating servers, and Computer Room Air Handlers (CRAHs) as well as rejection components outside the server room such as heat exchangers, compressors, and dry coolers. This model is used to train a RL agent to control the cooling equipment. Evaluations show that the RL agent can outperform traditional controllers and also can adapt to changes in the environment, such as equipment breaking down.

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Section: The DC Simulation Modelmentioning

confidence: 99%