Multifidelity Topology Design for Thermal–Fluid Devices via SEMDOT Algorithm

Designing thermal–fluid devices that reduce peak temperature while limiting pressure loss is challenging because high-fidelity (HF) Navier–Stokes–convection simulations make direct HF-driven topology optimization computationally expensive. This study presents a two-dimensional, steady, laminar multifidelity topology design framework for thermal–fluid devices operating in a low-to-moderate Reynolds number regime. A computationally efficient low-fidelity (LF) Darcy–convection model is used for topology optimization, where SEMDOT decouples geometric smoothness from the analysis field to produce CAD-ready boundaries. The LF optimization minimizes a P-norm aggregated temperature subject to a prescribed volume fraction constraint; the inlet–outlet pressure difference and the P-norm parameter are varied to generate a diverse candidate set. All candidates are then evaluated using a steady incompressible HF Navier–Stokes–convection model in COMSOL 6.3 under a consistent operating condition (fixed flow; pressure drop reported as an output). In representative single- and multi-channel case studies, SEMDOT designs reduce the HF peak temperature (e.g., ~337 K to ~323 K) while also reducing the pressure drop (e.g., ~18.7 Pa to ~12.6 Pa) relative to conventional straight-channel layouts under the same operating point. Compared with a conventional RAMP-based pipeline under the tested settings, the proposed approach yields a more favorable Pareto distribution (normalized hypervolume 1.000 vs. 0.923).

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Publication Details

Journal
Computation
Published
2026-01-12
DOI
https://doi.org/10.3390/computation14010019
Citations
1
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
45.17
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article

Multifidelity Topology Design for Thermal–Fluid Devices via SEMDOT Algorithm

Yun-Fei Fu, Yifan Guo, Shuzhi Xu, Yao Sun
1 citations
Computation
Advanced Multi-Objective Optimization Algorithms
45.17
article

Multifidelity Topology Design for Thermal–Fluid Devices via SEMDOT Algorithm

Yun-Fei Fu, Yifan Guo, Shuzhi Xu, Yao Sun
article en
1 citations

Abstract

Designing thermal–fluid devices that reduce peak temperature while limiting pressure loss is challenging because high-fidelity (HF) Navier–Stokes–convection simulations make direct HF-driven topology optimization computationally expensive. This study presents a two-dimensional, steady, laminar multifidelity topology design framework for thermal–fluid devices operating in a low-to-moderate Reynolds number regime. A computationally efficient low-fidelity (LF) Darcy–convection model is used for topology optimization, where SEMDOT decouples geometric smoothness from the analysis field to produce CAD-ready boundaries. The LF optimization minimizes a P-norm aggregated temperature subject to a prescribed volume fraction constraint; the inlet–outlet pressure difference and the P-norm parameter are varied to generate a diverse candidate set. All candidates are then evaluated using a steady incompressible HF Navier–Stokes–convection model in COMSOL 6.3 under a consistent operating condition (fixed flow; pressure drop reported as an output). In representative single- and multi-channel case studies, SEMDOT designs reduce the HF peak temperature (e.g., ~337 K to ~323 K) while also reducing the pressure drop (e.g., ~18.7 Pa to ~12.6 Pa) relative to conventional straight-channel layouts under the same operating point. Compared with a conventional RAMP-based pipeline under the tested settings, the proposed approach yields a more favorable Pareto distribution (normalized hypervolume 1.000 vs. 0.923).

ComputationVol. 14(1)
University of Alberta (CA), Shandong University of Science and Technology (CN), The University of Osaka (JP)
Affordable and clean energy
Openalex Percentile: Top 1%
Advanced Multi-Objective Optimization Algorithms
45.17
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Multifidelity Topology Design for Thermal–Fluid Devices via SEMDOT Algorithm — Yun-Fei Fu, Yifan Guo, et al. · Computation (2026) | TGRS Research Map | TGRS