Shape optimization of peristaltic pumps transporting rigid particles in Stokes flow

TL;DR

This paper introduces a computational method for optimizing the shape of peristaltic pumps that transport rigid particles in Stokes flow, minimizing energy dissipation while meeting specific transport criteria.

Contribution

It develops a boundary integral-based shape optimization framework leveraging flow linearity to efficiently compute shape sensitivities for multiphase flows.

Findings

Validated shape derivative formulas against finite differences.

Demonstrated optimal pump shapes under various constraints.

Achieved efficient shape optimization using boundary integral methods.

Abstract

This paper presents a computational approach for finding the optimal shapes of peristaltic pumps transporting rigid particles in Stokes flow. In particular, we consider shapes that minimize the rate of energy dissipation while pumping a prescribed volume of fluid, number of particles and/or distance traversed by the particles over a set time period. Our approach relies on a recently developed fast and accurate boundary integral solver for simulating multiphase flows through periodic geometries of arbitrary shapes. In order to fully capitalize on the dimensionality reduction feature of the boundary integral methods, shape sensitivities must ideally involve evaluating the physical variables on the particle or pump boundaries only. We show that this can indeed be accomplished owing to the linearity of Stokes flow. The forward problem solves for the particle motion in a slip-driven pipe flow while the adjoint problems in our construction solve quasi-static Dirichlet boundary value problems backwards in time, retracing the particle evolution. The shape sensitivities simply depend on the solution of one forward and one adjoint (for each shape functional) problems. We validate these analytic shape derivative formulas by comparing against finite-difference based gradients and present several examples showcasing optimal pump shapes under various constraints.