Machine-Learning-Based Control of Perturbed and Heated Channel Flows

Abstract

A reinforcement learning algorithm is coupled to a thermal lattice-Boltzmann method to control flow through a two-dimensional heated channel narrowed by a bump. The algorithm is allowed to change the disturbance factor of the bump and receives feedback in terms of the pressure loss and temperature increase between the inflow and outflow region of the channel. It is trained to modify the bump such that both fluid mechanical properties are rated equally important. After a modification, a new simulation is initialized using the modified geometry and the flow field computed in the previous run. The thermal lattice-Boltzmann method is validated for a fully developed isothermal channel flow. After 265 simulations, the trained algorithm predicts an averaged disturbance factor that deviates by less than 1 % from the reference solution obtained from 3,400 numerical simulations using a parameter sweep over the disturbance factor. The error is reduced to less than 0.1 % after 1,450 simulations. A comparison of the temperature, pressure, and streamwise velocity distributions of the reference solution with the solution after 1,450 simulations along the line of the maximum velocity component in streamwise direction shows only negligible differences. The presented method is hence a valid method for avoiding expensive parameter space explorations and promises to be effective in supporting shape optimizations for more complex configurations, e.g., in finding optimal nasal cavity shapes.