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).
Authors
- Yun-Fei Fu (ORCID: https://orcid.org/0009-0005-1083-8389)
- Yifan Guo (ORCID: https://orcid.org/0000-0002-3078-9968)
- Shuzhi Xu
- Yao Sun (ORCID: https://orcid.org/0000-0002-7268-9201)
Institutions
- University of Alberta (CA)
- Shandong University of Science and Technology (CN)
- The University of Osaka (JP)
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