Enhancing Data Center Sustainability with a 3D CNN-Based CFD Surrogate Model (Papers Track)

Soumyendu Sarkar (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Zachariah Carmichael (University of Notre Dame); Vineet Gundecha (Hewlett Packard Enterpise); Avisek Naug (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Ricardo Luna Gutierrez (Hewlett Packard Enterprise)

Paper PDF Poster File Recorded Talk NeurIPS 2023 Poster Cite
Buildings Computer Vision & Remote Sensing


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.

Recorded Talk (direct link)