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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

Enhancing Data Center Sustainability with a 3D CNN-Based CFD Surrogate Model

Soumyendu Sarkar · Avisek Naug · Zachariah Carmichael · Vineet Gundecha · Ashwin Ramesh Babu · Antonio Guillen-Perez · Ricardo Luna Gutierrez

Abstract: Thermal Computational Fluid Dynamics (CFD) models analyze airflow and heat distribution in data centers, but their complex computations hinder efficient energy-saving optimizations for sustainability. We introduce a new method to acquire data and model 3D Convolutional Neural Network (CNN) based surrogates for CFDs, which predict a data center's temperature distribution based on server workload, HVAC airflow rate, and temperature set points. The surrogate model's predictions are highly accurate, with a mean absolute error of 0.31°C compared to CFD-based ground truth temperatures. The surrogate model is three orders of magnitude faster than CFDs in generating the temperature maps for similar-sized data centers, enabling real-time applications. It helps to quickly identify and reduce temperature hot spots($7.7%) by redistributing workloads and saving cooling energy($2.5%). It also aids in optimizing server placement during installation, preventing issues, and increasing equipment lifespan. These optimizations boost sustainability by reducing energy use, improving server performance, and lowering environmental impact.

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