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) by Waki et al., which has been adopted successfully to solve reaction-diffusion boundary value problems by Mevissen et al. 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.