This research gauges the ability of deep reinforcement learning (DRL) techniques to assist control conjugate heat transfer systems governed by coupled Navier–Stokes and equations. It uses a novel, “degenerate” version proximal policy optimization (PPO) algorithm, intended for situations where optimal be learnt neural network does not depend on state, as is notably case in open-loop problems. Th...

The Lattice Boltzmann Method (LBM) was used for simulation of a gas flow and conjugate heat transfer in fixed packed beds with solid particles inside. D2Q9 version of the Factorized Central Moment (FCMLBM) is used for a gas flow computation. Heating transfer in the bed is described by second distribution function set and multiple relaxation time lattice Boltzmann method (DDF MRT LBM) is used. N...

Abstract The primary challenges for simulating a turbulent flow over micro‐structured surface arise from the two hugely disparate spatial length scales. For fluid–solid coupled conjugate heat transfer (CHT), there is also time‐scale disparity. present work addresses scale disparities based on two‐scale framework. disparity, dual meshing employed to couple global coarse‐mesh domain with local fi...