Data center

Energy (signal processing)

Work (physics)

Mathematics

Agronomy

Stencil

Biology

Operating system

Statistics

Engineering

Controller (irrigation)

Heat sink

Electronics cooling

Computer science

Computational science

Water cooling

Mechanical engineering

Computational Mechanics

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.

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