基于自适应随机优化的连续阵风关键载荷预测

doi:10.7527/S1000-6893.2018.22383

Abstract

Abstract: Continuous gust load is one of the main loads for commercial airplane. In the design stage, any loop of model update involves the calculation of thousands of gust load cases; however, only a few cases constitute the critical load envelop, and are needed for strength check. Therefore, a rapid identification method for gust critical load is developed in this paper. First of all, using the two-level full factorial (2LFF) sampling technique, a set of initial calculation cases is obtained. With the calculated load values, a reliable surrogate model is established based on the multivariate adaptive regression spline (MARS). Secondly, on this basis, adaptive stochastic optimization technology is adopted to achieve active search of the critical gust case and load. Finally, using the test case of the lateral continuous gust load, the validation and the investigation on the influence of parameters are carried out. The calculation results show that the proposed method can effectively and accurately predict the gust critical load, and the prediction error of critical load is less than 1%.

Key words: surrogate model, multivariate adaptive regression spline(MARS), adaptive stochastic optimization, continuous gust, critical load

CLC Number:

V211.47

XIAO Yu. Prediction of critical continuous gust load based on adaptive stochastic optimization[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(2): 522383-522383.

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Prediction of critical continuous gust load based on adaptive stochastic optimization

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