A numerical approach to the optimal control of thermally convective flows

TL;DR

This paper introduces an efficient numerical method for the optimal control of thermally convective flows, utilizing operator splitting, finite element discretization, and BFGS optimization to handle the complex Boussinesq equations.

Contribution

The paper develops a novel numerical approach combining operator splitting, finite element methods, and BFGS optimization for controlling thermally convective flows modeled by Boussinesq equations.

Findings

Method effectively decomposes complex equations into simpler linear problems.

Numerical experiments demonstrate promising efficiency.

Strategy for step size selection improves BFGS performance.

Abstract

The optimal control of thermally convective flows is usually modeled by an optimization problem with constraints of Boussinesq equations that consist of the Navier-Stokes equation and an advection-diffusion equation. This optimal control problem is challenging from both theoretical analysis and algorithmic design perspectives. For example, the nonlinearity and coupling of fluid flows and energy transports prevent direct applications of gradient type algorithms in practice. In this paper, we propose an efficient numerical method to solve this problem based on the operator splitting and optimization techniques. In particular, we employ the Marchuk-Yanenko method leveraged by the $L^2-$projection for the time discretization of the Boussinesq equations so that the Boussinesq equations are decomposed into some easier linear equations without any difficulty in deriving the corresponding adjoint system. Consequently, at each iteration, four easy linear advection-diffusion equations and two degenerated Stokes equations at each time step are needed to be solved for computing a gradient. Then, we apply the Bercovier-Pironneau finite element method for space discretization, and design a BFGS type algorithm for solving the fully discretized optimal control problem. We look into the structure of the problem, and design a meticulous strategy to seek step sizes for the BFGS efficiently. Efficiency of the numerical approach is promisingly validated by the results of some preliminary numerical experiments.