Parametric on performance of surrogate models in hydraulic optimization of centrifugal pumps
In order to solve the problem of the lack of system theoretical basis for the determination of surrogate model in the optimization of hydraulic performance of centrifugal pump,a parametric analysis was conducted based on 3 400 pipeline pump design samples,three commonly used surrogate models in centrifugal pump optimization were evaluated:artificial neural networks,response surface models,and Kriging models.The results,comparing model performance and prediction accuracy across various sample sizes,show that the response surface model struggle with accuracy in multi-parameter optimiza-tion scenarios,while Kriging models and artificial neural networks excel in single-component optimiza-tions.For more complex objectives or multi-component optimizations,only deep feedforward neural net-works with multiple hidden layers demonstrate some reliability,but still fall short of meeting all optimi-zation requirements.Additionally,the study reveals that single-layer feedforward neural networks per-form best with smaller sample sizes(1.0 to 1.2 times the total number of parameters),while deep feedforward and cascade feedforward networks prove more reliable when the sample size exceeds twice the total number of parameters.
万方数据
维普
