Solutions of Polynomial Systems Derived from the Steady Cavity Flow Problem

TL;DR

This paper introduces an algorithm that enumerates all solutions of discretized steady cavity flow polynomial systems by leveraging sparse semidefinite programming relaxations to identify minimal kinetic energy solutions.

Contribution

The paper develops a novel enumeration algorithm for polynomial systems from cavity flow problems using SDPR, enabling the identification of solutions with minimal kinetic energy.

Findings

Successfully derived the k smallest kinetic energy solutions.

Solutions converge to minimal energy solutions as SDPR order increases.

Algorithm demonstrates effectiveness on various cavity flow scenarios.

Abstract

We propose a general algorithm to enumerate all solutions of a zero-dimensional polynomial system with respect to a given cost function. The algorithm is developed and is used to study a polynomial system obtained by discretizing the steady cavity flow problem in two dimensions. The key technique on which our algorithm is based is to solve polynomial optimization problems via sparse semidefinite programming relaxations (SDPR), which has been adopted successfully to solve reaction-diffusion boundary value problems recently. The cost function to be minimized is derived from discretizing the fluid's kinetic energy. The enumeration algorithm's solutions are shown to converge to the minimal kinetic energy solutions for SDPR of increasing order. We demonstrate the algorithm with SDPR of first and second order on polynomial systems for different scenarios of the cavity flow problem and succeed in deriving the $k$ smallest kinetic energy solutions. The question whether these solutions converge to solutions of the steady cavity flow problem is discussed, and we pose a conjecture for the minimal energy solution for increasing Reynolds number.