As AI workloads push data centers to thermal extremes, conventional cooling methods are increasingly strained. Gamma Technologies‘ GT-SUITE aims to address this through a digital twin framework that integrates physics-based simulation with AI-powered control. According to the GT blog, the approach covers:
- Physics-based modeling of advanced cooling architectures, including passive, refrigerant-based Rear Door Heat Exchanger systems with gravity-driven, phase-change circulation
- Machine learning metamodeling — the built-in ML Assistant aims to generate NARX metamodels from simulation data, capturing system dynamics while significantly reducing computational complexity
- Model Predictive Control (MPC) to proactively optimize temperature, airflow, and energy use across multi-rack environments
- Seamless integration with Simulink and embedded control hardware via C code or MEX exports
The platform aims to serve as a comprehensive development pathway, from system design and virtual validation to intelligent control deployment, enabling data centers to move toward greater energy efficiency, reduced operational costs, and improved thermal reliability as power densities continue to rise.
Image courtesy: Gamma Technologies
