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Minimisation of the wall shear stress gradients in bypass grafts anastomoses using meshless CFDand genetic algorithms optimisation

机译:使用无网格CFD和遗传算法优化来最小化旁路移植物吻合中的壁切应力梯度

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摘要

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.
机译:壁切应力(WSS)的空间和时间梯度是与内皮损伤相关的两个血液动力学参数。这两个梯度在旁路血管吻合术的几何结构中非常明显,在这种几何结构中,血流模式非常混乱。可以通过优化吻合形状来实现主动脉底部WSS梯度最小化,因此可以改善移植物的长期术后性能。可以通过由无网格计算流体力学(CFD)求解器和遗传算法(GA)形状优化器组成的集成计算工具来执行吻合形状优化。无网格CFD求解器用于评估WSS梯度,而GA优化器则用于搜索能最大程度地减小这些梯度的端到端远端吻合(ETSDA)最佳形状。我们利用无网格CFD方法来解决血液动力学问题,并采用GA进行优化。我们考虑了三种不同的吻合模型:传统的ETSDA,米勒袖套ETSDA和引擎盖ETSDA。本文报道的结果表明,无论采用常规还是Miller Cuff ETSDA模型,都应始终使移植物的口径最大化。另外,应注意的是,米勒袖带的高度应减至最小。最佳吻合角度的选择应进行优化,以在空间WSS梯度和时间WSS梯度的同时最小化之间达成折衷。

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