Shape optimization of fluid cooling channel based on Darcy reduced-order isogeometric analysis

Чэн, Лонг; Wang, Kai; Li, Baotong; Mao, Yicheng

doi:10.1016/j.cam.2022.114262

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…By comparing the results by finite element analysis under the same model, it can obtain a higher accuracy solution with fewer elements. Their findings underscored that adopting isogeometric analysis yielded smoother and more continuous boundaries, leading to a marked reduction in computational cycles compared to alternative methods [77].…”

Section: Nurbs Methodsmentioning

confidence: 94%

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A Mini Review on Fluid Topology Optimization

Li,

Wang,

Zhang

et al. 2023

Materials

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Topology optimization holds great potential to achieve the best performance for various fluid-related applications like aircraft components and microfluidic mixers. This paper reviews comprehensively the technical progress of this field over the last decade from the viewpoint of structural expression. The density-based approach has been widely adopted to design structures due to its simple concept, ease of implementation, and robustness. Different designs using such a pointwise method for systems under Stokes, laminar Navier–Stokes, turbulent, non-Newtonian, and steady-state/unsteady-state fluid flows are reviewed and discussed in depth. Both isogeometric analysis and the moving morphable components/voids methods will demonstrate their advantages regarding integration with computer-aided design. The moving morphable components/voids method also significantly reduces computing costs. From the viewpoint of boundary smoothness, we are most concerned about whether smoother boundaries can reduce objective functions such as energy dissipation even lower. Therefore, this work also concentrates on level set and spline expression methods. Furthermore, we identify isogeometric analysis and machine learning in shaping the field’s future. In addition, the review highlights the following two challenges: achieving accurate fluid model construction and the relatively limited experimental validation of fluid topology optimization outcomes.

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Section: Nurbs Methodsmentioning

confidence: 94%

“…Materials 2023, 16, x FOR PEER REVIEW 9 of 16leading to a marked reduction in computational cycles compared to alternative methods[77].…”

mentioning

confidence: 99%