Multi-objective design optimization of stent-grafts for the aortic arch

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Multi-objective design optimization of stent-grafts for the aortic arch
Title:
Multi-objective design optimization of stent-grafts for the aortic arch
Journal Title:
Materials & Design
Publication Date:
18 February 2023
Citation:
Liu, Z., Chen, G., Ong, C., Yao, Z., Li, X., Deng, J., & Cui, F. (2023). Multi-objective design optimization of stent-grafts for the aortic arch. Materials & Design, 227, 111748. https://doi.org/10.1016/j.matdes.2023.111748
Abstract:
Complications of thoracic endovascular aortic repair are closely related to the mechanical properties of implanted stent-grafts. Compared with straight vessels, the complex morphology of aortic arch imposes strict requirements on stent-graft properties. This study designed and verified an aortic stent-graft by optimizing a specially constructed commercial descending stent-graft. The main structural parameters including strut height, strut number, strut radius, wire diameter, and graft thickness were set as the design variables. The essential mechanical properties (flexibility and radial support force) of stent-grafts relevant to the mentioned variables were selected as objective functions. Surrogate model and multi-objective genetic algorithms were adopted to implement the optimization process. Finally, the optimized stent-graft was virtually implanted into an ideal aortic arch to verify its suitability. The optimization was completed after 16 iterations, demonstrating that the optimization method used in this study was efficient. The results showed that the flexibility of the near-optimal stent-graft improved over 50% and the radial support force was extremely close to that of the original design. Compared with the original design, the sealing effect of the optimized stent-graft was significantly improved. The results of this study provide guidelines and a feasible method for designing aortic arch stent-grafts.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the A*STAR - HMT HTPO-BEP RUN2 Grant
Grant Reference no. : C211318010

This study was supported by:- 1) Ministry of Science and Technology of the People´s Republic of China - Singapore Joint Research Program (Grant No. 2016YFE0117200) 2) Ministry of Science and Technology of the People´s Republic of China the China -the National Natural Science Foundation of China (Grant No. 52208309) 3) China Ministry of Civil Affairs - the China Postdoctoral Science Foundation (Grant No. 2022 M710870)
Description:
ISSN:
0264-1275
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