4.7 Article

Efficient and accurate adaptive resolution for weakly-compressible SPH

Journal

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2022.115019

Keywords

SPH; Variable spatial resolution; Adaptivity; Weakly-compressible SPH; Incompressible; Solution adaptivity

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In this paper, we propose an accurate and computationally efficient method for incorporating adaptive spatial resolution into weakly-compressible Smoothed Particle Hydrodynamics (SPH) schemes. The method adaptively splits and merges particles in an accurate manner, ensuring optimal number of neighbors for each particle. It allows for geometry-based or solution-based adaptive resolution, enabling simulation of problems with length variations using fewer particles.
In this paper we propose an accurate, and computationally efficient method for incorporating adaptive spatial resolution into weakly-compressible Smoothed Particle Hydrodynamics (SPH) schemes. Particles are adaptively split and merged in an accurate manner. Critically, the method ensures that the number of neighbors of each particle is optimal, leading to an efficient algorithm. A set of background particles is used to specify either geometry-based spatial resolution, where the resolution is a function of distance to a solid body, or solution-based adaptive resolution, where the resolution is a function of the computed solution. This allows us to simulate problems using particles having length variations of the order of 1:250 with much fewer particles than currently reported with other techniques. The method is designed to automatically adapt when any solid bodies move. The algorithms employed are fully parallel. We consider a suite of benchmark problems to demonstrate the accuracy of the approach. We then consider the classic problem of the flow past a circular cylinder at a range of Reynolds numbers and show that the proposed method produces accurate results with a significantly reduced number of particles. We provide an open source implementation and a fully reproducible manuscript.(c) 2022 Elsevier B.V. All rights reserved.

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