A digital-twin and machine-learning framework for precise heat and energy management of data-centers

摘要

The massive growth in data-centers has led to increased interest and regulations for management of waste heat and its utilization. This work seeks to develop a combined Digital-Twin and Machine-Learning framework to optimize such systems by controlling both the ventilation and the cooling of the bases of data units/processors in the system. This framework ascertains optimal cooling strategies to deliver a target temperature in the system using a minimum amount of energy. A model problem is constructed for a data-center, where the design variables are the flow rates and air-cooling at multiple ventilation ports and ground-level conduction-based base-cooling of processors. A thermo-fluid model, based on the Navier-Stokes equations and the first law of thermodynamics, for the data-center is constructed and a rapid, stencil-based, iterative solution method is developed. This is then combined with a genomic-based machine-learning algorithm to develop a digital-twin (digital-replica) of the system that can run in real-time or faster than the actual physical system, making it suitable as either a design tool or an adaptive controller. Numerical examples are provided to illustrate the framework.