A dual grid-based deep reinforcement learning and computational fluid dynamics method for flow control and its application to nucleate pool boiling

Abstract

This paper presents a proposition of a dual grid (DG)-based coupled deep reinforcement learning (DRL) and computational fluid dynamics (CFD) method for active flow control. The DG-DRL-CFD method uses a dual-resolution grid, with a coarser grid for the training phase and a finer grid for the testing phase of the DRL-CFD method. Further, after a validation study for our DRL-CFD-based parallelized in-house solvers, this paper presents a performance study for a periodic suction/ejection-based drag reduction for a cylinder in a channel-confined flow. Using an immersed boundary method for CFD on a Cartesian grid, this novel DG-DRL-CFD method is shown to result in almost same accuracy (within 1%) in a substantially reduced computational time as compared to the traditional DRL-CFD method (on the finer uniform grid). Finally, using a sharp interface level set method for an axisymmetric two-phase flow, this paper presents an application of the DR-DRL-CFD method for an oscillating base plate-based enhancement of heat transfer during nucleate pool boiling. As compared to our recent CFD study-based parametric study for the nucleate pool boiling problem, the present DG-DRL-CFD method leads to a profile and frequency of plate-oscillation that results in a larger value of average Nusselt number Nuavg for the plate. The coupled DRL-CFD method leads to plate oscillation profile giving higher enhancement in Nuavg, compared to an open-loop control strategy that involves a parametric sweep involving multiple simulations.