Unsteady aerodynamic modeling research and virtual flight test verification

CHEN Xiang; ZHAN Jingxia; CHEN Ke; WEI Zhongcheng; CAO Yuan

doi:10.11729/syltlx20210143

Volume 36 Issue 3

Jul. 2022

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2022 > 36(3): 65-72

CHEN X，ZHAN J X，CHEN K，et al. Unsteady aerodynamic modeling research and virtual flight test verification[J]. Journal of Experiments in Fluid Mechanics, 2022，36（3）：65-72. doi: 10.11729/syltlx20210143

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Unsteady aerodynamic modeling research and virtual flight test verification

doi: 10.11729/syltlx20210143

Chengdu Aircraft Design & Research Institute, Chengdu　610091, China

Received Date: 2021-10-18
Accepted Date: 2022-04-20
Rev Recd Date: 2022-04-14

Available Online: 2022-07-12

Publish Date: 2022-07-04

Abstract

Abstract

Unsteady aerodynamic modeling involves aerodynamics, flight mechanics, flight control and other fields, which is the key of aircraft high angle of attack database. The traditional aerodynamic model is composed of static aerodynamic, rotating balance and dynamic derivative data, which cannot describe unsteady aerodynamics exactly. Recurrent Neural Network （RNN） structure is a kind of neural network structure for processing and predicting sequence data, which is widely used in the field of artificial intelligence. RNN has the same time–dependent characteristic as unsteady aerodynamics. The application of RNN on the unsteady aerodynamic modeling has been researched. The forced motion test and virtual flight test have been used for unsteady aerodynamic model’s verification. In the forced motion wind tunnel test, comparing the aerodynamic forces of cobra maneuver, the result demonstrates that the RNN model is more accurate than the traditional model. In the virtual flight test, comparing the movement parameters curve of the wind tunnel test and simulation, the results also demonstrate that the RNN model is closer to the wind tunnel test than the traditional dynamic derivative model.
- unsteady aerodynamic,
- aerodynamic model,
- aerodynamic database,
- Recurrent Neural Network,
- virtual flight test

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References(23)

References

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