Optimization of a Heat Recovery Steam Generator Inlet Duct for Flow Uniformity Improvement Based on a Surrogate-Assisted CFD Method
In a combined-cycle power plant, the inlet duct of a heat recovery steam generator (HRSG) involves a transition from a circular inlet to a rectangular outlet with a sudden cross-sectional expansion, which causes a non-uniform velocity distribution upstream of the heat exchanger modules and thereby affects heat-transfer performance and operational reliability. In this study, a surrogate-assisted computational fluid dynamics (CFD) optimization method was applied to improve the outlet flow uniformity of an HRSG inlet duct under geometric constraints. Three-dimensional steady-state CFD analysis was first performed for the baseline geometry, and the outlet velocity deviation was quantified using the area-weighted root-mean-square (RMS) velocity deviation, uRMS. A total of 16 geometric design variables were defined to parameterize the inlet duct shape, and 160 sample geometries were generated using Optimal Latin Hypercube Design and analyzed by CFD to construct the surrogate-modeling database. Among several metamodeling techniques compared against CFD results, the Radial Basis Function Regression model was selected. The selected surrogate model was coupled with a Hybrid Metaheuristic Algorithm to search for an improved geometry. The resulting geometry was verified by CFD analysis. The results showed that the outlet uRMS decreased from 19.035 m/s to 14.669 m/s, corresponding to a reduction of approximately 22.9%, while the average outlet velocity remained nearly unchanged. The total pressure loss of the duct was also reduced by approximately 7.8%. Heat-transfer and broader operating-condition assessments remain necessary before practical implementation.
Authors
- 전승원
- Junghwan Kook (ORCID: https://orcid.org/0000-0002-4342-1871)
- Hyungmo Kim (ORCID: https://orcid.org/0000-0003-4765-6835)
- Hyeonmin Choi
- Taegyun Noh
- Seongmin Kim
Institutions
- Gyeongsang National University (KR)
Publication Details
- Journal
- Energies
- Published
- 2026-10-05
- DOI
- https://doi.org/10.3390/en19194693
- Primary Topic
- Advanced Multi-Objective Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00