Large-scale backward-curved centrifugal fans without volutes are extensively employed in enclosed air-cooled electric motors owing to their exceptional heat dissipation performance. This category of fans features substantial blade dimensions and a multitude of optimization parameters, which introduce challenges such as diminished predictive accuracy in high-dimensional optimization spaces. To address these issues, this paper proposes a blade optimization design methodology based on a GA-Kriging surrogate model. Sobol’s global sensitivity analysis is first employed to reduce model dimensionality. Subsequently, a high-fidelity aerodynamic performance prediction model is constructed through the integration of a Genetic Algorithm (GA) and a Kriging model. A constrained optimization is then conducted with volumetric flow rate and static pressure as the design objectives, and shaft power along with geometric point coordinates as the constraints. Experimental test results demonstrate that the fan optimized via the surrogate model, while maintaining low prediction error, achieves a 14% increase in volumetric flow rate and a 20% improvement in static pressure. This outcome indicates a significant enhancement in the overall aerodynamic performance.
Zhang et al. (Tue,) studied this question.
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