Centrifugal pump impeller optimization design method based on feature contribution degree and multi-objective decision

Through Bayesian optimization and SHAP analysis, the centrifugal pump impeller design parameters were screened, combined with multi-objective evolution algorithms and decision-making methods, the problems of low computational efficiency and strong subjectivity in the existing technology were solved, and efficient optimization and reliable design of centrifugal pumps were achieved.

CN120354781APending Publication Date: 2025-07-22JIANGSU UNIV OF SCI & TECH
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Patent Information

Application Number
CN202510434192.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-08
Publication Date
2025-07-22

AI Technical Summary

Technical Problem

The existing centrifugal pump impeller design method relies on empirical formulas, has low computational efficiency and is prone to local optimality, has not introduced feature contribution analysis, has strong subjectivity of parameter selection, lacks systematicity, and cannot find parameters that have an important impact on model results.

Method used

The Bayesian-optimized XGBoost regression model combined with SHAP analysis was used to screen significant feature parameters, optimize the design parameters through multi-objective evolution algorithm and SHAP-TOPSIS decision-making method, introduce the minimum efficiency index MEI index, and dynamically adjust the feature contribution degree and weight.

Benefits of technology

It improves the head and efficiency of centrifugal pumps, reduces computational costs, improves model training efficiency and interpretability, and provides more scientific and reliable design support.

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Abstract

The invention belongs to the technical field of centrifugal pump design, and discloses a centrifugal pump impeller optimization design method based on characteristic contribution degree and multi-objective decision, which comprises the following steps: S1, obtaining an initial model and parameters of the initial model according to a geometric structure of a centrifugal pump, obtaining lift, efficiency and a minimum efficiency index, and constructing an optimization data set; s2, optimizing the data set, constructing a regression model, and screening core optimization variables; s3, sampling to generate a secondary data set according to the core optimization variable, and simulating to obtain a lift and a minimum efficiency index value; a decomposition-based multi-objective evolutionary algorithm is adopted to carry out multi-objective optimization, and a Pareto leading-edge solution set is generated; and S4, constructing an SHAP-TOPSIS decision method, and screening the Pareto frontier solution set obtained in the step S3 to obtain an optimal parameter combination. According to the method, Bayesian optimization, SHAP analysis and multi-objective decision are fused, efficient energy-saving design of the centrifugal pump is achieved, and a new thought is provided for intelligent optimization of complex fluid machinery.
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