In:
International Journal for Numerical Methods in Biomedical Engineering, Wiley, Vol. 37, No. 2 ( 2021-02)
Abstract:
Disturbed flow and the resulting non‐physiological wall shear stress (WSS) at the graft‐vein anastomosis play an important role in arteriovenous graft (AVG) patency loss. Modifying graft geometry with helical features is a popular approach to minimise the occurrence of detrimental haemodynamics and to potentially increase graft longevity. Haemodynamic optimisation of AVGs typically requires many computationally expensive computational fluid dynamics (CFD) simulations to evaluate haemodynamic performance of different graft designs. In this study, we aimed to develop a haemodynamically optimised AVG by using an efficient meta‐modelling approach. A training dataset containing CFD evaluations of 103 graft designs with helical features was used to develop computationally low‐cost meta‐models for haemodynamic metrics related to graft dysfunction. During optimisation, the meta‐models replaced CFD simulations that were otherwise needed to evaluate the haemodynamic performance of possible graft designs. After optimisation, haemodynamic performance of the optimised graft design was verified using a CFD simulation. The obtained optimised graft design contained both a helical graft centreline and helical ridge. Using the optimised design, the magnitude of flow disturbances and the size of the anastomotic areas exposed to non‐physiological WSS was successfully reduced compared to a regular straight graft. Our meta‐modelling approach allowed to reduce the total number of CFD model evaluations required for our design optimisation by approximately a factor 2000. The applied efficient meta‐modelling technique was successful in identifying an optimal, helical graft design at relatively low computational costs. Future studies should evaluate the in vivo benefits of the developed graft design.
Type of Medium:
Online Resource
ISSN:
2040-7939
,
2040-7947
Language:
English
Publisher:
Wiley
Publication Date:
2021
detail.hit.zdb_id:
2540968-2
