117
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

Efficient local smoothed particle hydrodynamics with precomputed patches

, &
Pages 63-71 | Received 05 Oct 2017, Accepted 24 Dec 2017, Published online: 28 Jan 2018
 

ABSTRACT

This paper presents an improved method for applying smoothed particle hydrodynamics within a nested Lagrangian domain of fluid particles. In our previous implementation, ghost particles, generated by Poisson-disk sampling to enclose the region occupied by fluid particles, transferred necessary physical quantities from the outer domain to the fluid. Using this technique, the local fluid motion agreed with results simulated by SPH over the entire domain. However, the cost of generating ghost particles was burdensome. We propose herein a much less expensive, patch-based sampling method to generate ghost particles. In this new approach, the ghost particles are generated locally around each fluid particle and corresponding physical quantities are determined using the local ghost particles. Furthermore, we introduce a new formulation that determines the physical quantities of ghost particles from the outer domain, and transfers them to the local fluid particles.

2010 AMS SUBJECT CLASSIFICATIONS:

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by JSPS KAKENHI (grant nos. JP00351320 and JP17J00443).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,129.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.