A meshless CFD approach for evolutionary shape optimization of bypass grafts anastomoses

Abstract

Improving the blood flow or hemodynamics in the synthetic bypass graft end-to-side distal anastomosis (ETSDA) is an important element for the long-term success of bypass surgeries. An ETSDA is the interconnection between the graft and the operated-on artery. The control of hemodynamic conditions through the ETSDA is mostly dictated by the shape of the ETSDA. Thus, a formal ETSDA shape optimization would serve the goal of improving the ETSDA flowfield. Computational fluid dynamics (CFD) is a convenient tool to quantify hemodynamic parameters; also, the genetic algorithm (GA) is an effective tool to identify the ETSDA optimal shape that modify those hemodynamic quantities such that the optimization objective is met. The present article introduces a unique approach where a meshless CFD solver is coupled to a GA for the purpose of optimizing the ETSDA shape. Three anastomotic models are optimized herein: the conventional ETSDA, the Miller cuff ETSDA and the hood ETSDA. Results demonstrate the effectiveness of the proposed integrated optimization approach in obtaining anastomoses optimal shapes.

Keywords:

• end-to-side distal anastomoses
• intimal hyperplasia
• shape optimization
• meshless CFD
• genetic algorithms