Investigation on artificial intelligence for the prediction of aeroacoustic performances and controlling parameters optimization of aircraft

WU Junqiang; YANG Dangguo; ZHANG Lin; GONG Tianchi; ZHOU Fangqi; WANG Yan; LI Yang

doi:10.11729/syltlx20210073

Volume 36 Issue 3

Jul. 2022

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2022 > 36(3): 33-43

WU J Q，YANG D G，ZHANG L，et al. Investigation on artificial intelligence for the prediction of aeroacoustic performances and controlling parameters optimization of aircraft[J]. Journal of Experiments in Fluid Mechanics, 2022，36（3）：33-43. doi: 10.11729/syltlx20210073

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Investigation on artificial intelligence for the prediction of aeroacoustic performances and controlling parameters optimization of aircraft

doi: 10.11729/syltlx20210073

WU Junqiang^{1, 2
,},
YANG Dangguo^{1, 2
,
,},
ZHANG Lin²,
GONG Tianchi³,
ZHOU Fangqi^{1, 2},
WANG Yan³,
LI Yang²

1.
State Key Laboratory of Aerodynamics, China Aerodynamics Research and Development Center, Mianyang　621000, China
2.
High Speed Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang　621000, China
3.
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing　210016, China

Received Date: 2021-07-19
Accepted Date: 2022-01-26
Rev Recd Date: 2021-12-28

Available Online: 2022-05-07

Publish Date: 2022-07-04

Abstract

Abstract

Accurate aerodynamic characteristic data under different conditions is the prerequisite and fundamental guarantee for the fast design of a flight vehicle, the improvement of a control system, the evaluation of performances and performance appraisal. The cross synthesis between the machine learning technology （ML） based on deep neural network （DNN） and fluid mechanics is developing fast and has achieved remarkable progresses in the modification of turbulence models, modeling of systems, prediction of the aerodynamic and aeroacoustic characteristics, optimization of control parameters and reconstruction of the flow field. To effectively apply the powerful representative capability of DNN, according to the demand of intelligent optimization and design of weapon bays, this paper first established a database of aerodynamic loads for flows past cavities and then built deep forward neural network model for the prediction of aerodynamic loads. To enhance the robustness of the model, random search and Bayesian optimization are introduced during the training of the model. Numerical results show that the trained DNN model is able to predict the aerodynamic loads and aeroacoustic characteristics accurately and efficiently, which provides a useful tool for the prediction and control of the aeroacoustic characteristics of the cavity.
- artificial intelligence,
- data-driven,
- aerodynamic performance,
- cavity flows,
- machine learning,
- controlling parameters optimization

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References(33)

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