Aerodynamic Performance and Noise Optimization of a Parallel Multi-Blade Centrifugal Fan via RBF-Assisted Bayesian Surrogate Optimization

Parallel multi-blade centrifugal fans present a challenge in simultaneously reducing aerodynamic noise and maintaining efficiency. This study presents a multi-objective optimization using a radial basis function (RBF)-assisted Bayesian optimization framework, with three volute parameters (tongue radius, tongue clearance, and axial gap) as design variables. Computational fluid dynamics (CFD) combined with the Ffowcs Williams–Hawkings (FW-H) acoustic analogy was employed to evaluate noise and total pressure efficiency. To reduce computational cost, an RBF surrogate model was constructed from 30 Latin hypercube samples, achieving leave-one-out cross-validation (LOOCV) R2 values of 0.978 and 0.995 for noise and efficiency, respectively. A Bayesian search using the log expected hypervolume improvement (logEHVI) acquisition function was performed on the RBF response surfaces, converging to a hypervolume of approximately 0.72, consistent with an NSGA-II benchmark. Based on household fan requirements, a 70/30 noise-efficiency weighting was adopted, yielding RBF-predicted values of 59.04 dB and 0.545 for the selected low-noise-preference candidate. An independent CFD recalculation yielded 59.19 dB and 0.554. The SPL at the characteristic frequency of 2550 Hz was reduced by 9.9 dB. Flow field analysis revealed that the optimized tongue clearance weakened the impingement on the volute tongue and suppressed unsteady vortex shedding. This framework provides an efficient strategy for multi-objective aerodynamic and acoustic optimization of parallel centrifugal fan systems.