Beyond first-order accuracy in continuous-forcing immersed boundary methods, and their well-conditioned projection-based solution

TL;DR

This paper presents a refined immersed boundary method that achieves better-than-first-order accuracy by incorporating missing terms, demonstrated through Poisson and Navier-Stokes problems, and integrated into a well-conditioned projection-based solver.

Contribution

It introduces a higher-order continuous-forcing immersed boundary approach using a smoothed indicator function, improving accuracy and conditioning without heuristic corrections.

Findings

Empirical second-order convergence in Poisson problems.

Achieves slightly sub-second-order accuracy in Navier-Stokes simulations.

Framework suggests potential for second-order or higher accuracy.

Abstract

We introduce a refined immersed boundary (IB) methodology that is better-than-first-order accurate in practice, while preserving key properties of "continuous-forcing" IB approaches that retain a singular source term in the governing equations. Our method leverages a smoothed indicator (Heaviside) function, following ideas from multiphase flow and immersed layers formulations, to recast the IB solution as a composite of distinct interior and exterior fields. We demonstrate that, when cast through this composite-solution lens, prior continuous-forcing IB methods can be seen as neglecting terms in the governing and constraint equations that restrict the solution to first-order accuracy. We incorporate these terms to systematically improve accuracy without the need for heuristic corrections. In canonical Poisson problems, we empirically demonstrate second-order convergence, and in incompressible Navier-Stokes simulations the method achieves slightly sub-second-order performance. While our present study focuses on these cases, the framework suggests a path towards second-order accuracy or higher, with further extensions. This perspective reframes accuracy limitations typically attributed to IB schemes. Although continuous-forcing IB methods are often reported to be only first-order accurate, we show that neither smoothing nor interface interpolation inherently restricts attainable order. Moreover, we naturally incorporate this higher-order formulation into a projection-based solution process. The resulting algorithm simultaneously mitigates the spurious surface stresses produced by ill-conditioned linear systems and reduces sensitivity to geometric resolution, addressing both conditioning and accuracy concerns within a unified approach.