Title

Minimisation Of The Wall Shear Stress Gradients In Bypass Grafts Anastomoses Using Meshless Cfd And Genetic Algorithms Optimisation

Keywords

End-to-side distal anastomoses; Genetic algorithms; Intimal hyperplasia; Meshless CFD; Shape optimisation

Abstract

The wall shear stress (WSS) spatial and temporal gradients are two hemodynamics parameters correlated with endothelial damage. Those two gradients become well pronounced in a bypass graft anastomosis geometry where the blood flow patterns are quite disturbed. The WSS gradient minimisation on the host artery floor can be achieved by optimising the anastomosis shape and hence may lead to an improved long-term post-surgical performance of the graft. The anastomosis shape optimisation can be executed via an integrated computational tool comprised of a meshless computational fluid dynamics (CFD) solver and a genetic algorithm (GA) shape optimiser. The meshless CFD solver serves to evaluate the WSS gradients and the GA optimiser serves to search for the end-to-side distal anastomosis (ETSDA) optimal shape that best minimises those gradients. We utilise a meshless CFD method to resolve hemodynamics and a GA for the purpose of optimisation. We consider three different anastomotic models: the conventional ETSDA, the Miller Cuff ETSDA and the hood ETSDA. The results reported herein demonstrate that the graft calibre should always be maximised whether a conventional or Miller Cuff ETSDA model is utilised. Also, it was noted that the Miller Cuff height should be minimised. The choice of an optimal anastomotic angle should be optimised to achieve a compromise between the concurrent minimisations of both the spatial WSS gradient and the temporal WSS gradient. © 2010 Taylor & Francis.

Publication Date

7-22-2010

Publication Title

Computer Methods in Biomechanics and Biomedical Engineering

Volume

13

Issue

1

Number of Pages

35-47

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/10255840903013555

Socpus ID

77950892245 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77950892245

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