Flight Load Computation (FLC) consumes considerable computational time and resources, and promoting FLC efficiency is highly significant to shorten the development cycle and improve the design quality of aircraft. This paper focuses on a data-driven machine learning surrogate model for load analysis based on the Random Forest (RF), which fits the load analysis with important potential and application prospect in this field because of its advantages such as high learning efficiency, strong generalization ability, overfitting avoidance, parameter interpretability, and variable sensitivity analysis. The training data for the RF surrogate model using conventional load analysis methods and the SQL144 framework of NASTRAN are generated, and parameters such as height, Mach number, overload, and pitch angular acceleration serve as the input variables to establish the surrogate model of load prediction in the case of symmetric maneuver. The proposed model is used to predict other conditions and the shear force, bending moment, and torque of roots of wings and horizontal tail. The predicting results are evaluated and verified, demonstrating high accuracy of the RF surrogate model and its ability to drastically promote the FLC efficiency and conduct sensitivity analysis of the flight load to state parameters. This study provides a new approach to the efficient and comprehensive flight load analysis.
LI Haiquan
,
CHEN Xiaoqian
,
ZUO Linxuan
,
ZHAO Zhuolin
. Surrogate model for flight load analysis based on random forest[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022
, 43(3)
: 225640
-225640
.
DOI: 10.7527/S1000-6893.2021.25640
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