A method, system, device, medium, and product for optimizing the structural parameters of a small aircraft.

CN122133262APending Publication Date: 2026-06-02BEIHANG UNIV

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: BEIHANG UNIV
Filing Date: 2026-02-28
Publication Date: 2026-06-02

AI Technical Summary

Technical Problem

Existing technologies for optimizing the structural parameters of small aircraft suffer from problems such as high computational costs, large resource consumption, limited accuracy of the mapping proxy model, and inability to dynamically adjust, resulting in low optimization accuracy and reliability.

Method used

An incremental sampling training method using Kriging and artificial neural network models is adopted to integrate a performance index prediction model. Through incremental sampling training, a large number of prior samples are not required, and the model performance is dynamically adjusted to improve the reliability and accuracy of the prediction model.

Benefits of technology

It improves the accuracy and reliability of structural parameter optimization for small aircraft, reduces computational costs and resource consumption, and enhances the model's generalization ability and efficiency.

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Abstract

This application discloses a method, system, device, medium, and product for optimizing the structural parameters of a small aircraft, relating to the field of aircraft design technology. The method for optimizing the structural parameters of a small aircraft includes: obtaining multiple sets of preset values for the structural parameters of the small aircraft; determining the final integrated predicted values of the five performance indicators corresponding to each set of preset values based on the preset values of the structural parameters and performance indicator prediction models for five performance indicators; the performance indicator prediction model for any current performance indicator is obtained by integrating a Kriging model and an artificial neural network model using an incremental sampling training method; and filtering the preset values based on the final integrated predicted values of the five performance indicators corresponding to each set of preset values to obtain the optimal values of the structural parameters of the small aircraft. This application improves the optimization accuracy and reliability of the structural parameters of small aircraft.

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