The Perturbation Neural Network Surrogate Model Method for Size-Topology Synthetical Optimization of Wing Rib Trailing Edges With Flap Tracks

XIE Chuan; XU Chao; ZHOU Danfa; YAO Weixing

doi:10.21656/1000-0887.440033

Volume 45 Issue 1

Jan. 2024

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Article Navigation > Applied Mathematics and Mechanics > 2024 > 45(1): 61-71

XIE Chuan, XU Chao, ZHOU Danfa, YAO Weixing. The Perturbation Neural Network Surrogate Model Method for Size-Topology Synthetical Optimization of Wing Rib Trailing Edges With Flap Tracks[J]. Applied Mathematics and Mechanics, 2024, 45(1): 61-71. doi: 10.21656/1000-0887.440033

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The Perturbation Neural Network Surrogate Model Method for Size-Topology Synthetical Optimization of Wing Rib Trailing Edges With Flap Tracks

doi: 10.21656/1000-0887.440033

XIE Chuan^1
,,
XU Chao¹,
ZHOU Danfa²,
YAO Weixing^{1, 3
,
,}

1.
State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China
2.
Shanghai Electro-Mechanical Engineering Institute, Shanghai 201109, P.R.China
3.
Key Laboratory of Fundamental Science for National Defense-Advanced Design Technology of Flight Vehicle, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China

Received Date: 2023-02-13
Rev Recd Date: 2023-03-15
Publish Date: 2024-01-01

Abstract

Abstract

The design of wing rib trailing edges with flap tracks requires the determination of sizes of the rib edge strips, the webs and the topological shapes of the rib webs. Therefore, a perturbation neural network surrogate model method was proposed for the size-topology synthetical optimization. The basic idea is that, based on the sensitivity of topology optimization to parameters, the perturbation is introduced in the DOE samples to capture the topological mutation points by means of the filtering measure, and reduce the numerical noise, which greatly improves the prediction accuracy of the surrogate model. With the topology optimization process viewed as a black box, the surrogate model for the size variables and topology optimized structural responses was directly built up. Finally, optimization was carried out on the surrogate model to obtain the optimal combination of structural sizes and topological shapes. A typical calculation example of wing rib trailing edge optimization demonstrates the validity and superiority of the proposed method.
- perturbation neural network,
- size optimization,
- topology optimization,
- surrogate model,
- wing rib

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References(21)

References

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