Shuxun Li1,2, Yuhao Tian1,2,*, Guolong Deng1,2, Wei Li1,2, Yinggang Hu1,2, Xiaoya Wen1,2
FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.8, pp. 1809-1837, 2025, DOI:10.32604/fdmp.2025.065877
- 12 September 2025
Abstract The conventional Shear Stress Transport (SST) k–ω turbulence model often exhibits substantial inaccuracies when applied to the prediction of flow behavior in complex regions within axial flow control valves. To enhance its predictive fidelity for internal flow fields, this study introduces a novel calibration framework that integrates an artificial neural network (ANN) surrogate model with a particle swarm optimization (PSO) algorithm. In particular, an optimal Latin hypercube sampling strategy was employed to generate representative sample points across the empirical parameter space. For each sample, numerical simulations using ANSYS Fluent were conducted to evaluate the flow characteristics,… More >
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