Can I read Deep Reinforcement Learning for the Control of Conjugate Heat Transfer with Application to Workpiece Cooling on EtoBox?

Deep Reinforcement Learning for the Control of Conjugate Heat Transfer with Application to Workpiece Cooling by Hachem, Elie; Ghraieb, Hassan; Viquerat, Jonathan; Larcher, Aurélien; Meliga, Philippe is a scholarly article available to read on EtoBox.

What is Deep Reinforcement Learning for the Control of Conjugate Heat Transfer with Application to Workpiece Cooling about?

This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the control of conjugate heat transfer systems governed by the coupled Navier--Stokes and heat equations. It uses a novel, "degenerate" version of the proximal policy optimization (PPO) algorithm, intended for situations where the optimal policy to be learnt by a neural network does not depend on state, as is notably the case in optimization and open-loop control problems. The numerical reward fed to the neural network is computed with an in-house stabilized finite elements environment combining variational multi-scale (VMS) modeling of the governing equations, immerse volume method, and multi-component anisotropic mesh adaptation. Several test cases of natural and forced convection in two and three dimensions are used as testbed for developing the methodology. The approach successfully alleviates the natural convection induced enhancement of heat transfer in a two-dimensional, differentially heated square cavity controlled by piece-wise constant fluctuations of the sidewall temperature. It also proves capable of improving the homogeneity of temperature across the surface of two and three-dim

Author

Hachem, Elie; Ghraieb, Hassan; Viquerat, Jonathan; Larcher, Aurélien; Meliga, Philippe

Published

2020

Language

EN