Prediction of Spectral Response for Explosion Separation Based on DeepONet
Strong shock waves generated during the pyrotechnic separation process of aerospace vehicles can cause high-frequency damage or even structural failure to the vehicle’s structure. Existing structural designs for shock attenuation typically rely on shock response spectra methods, which require multip...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/4/310 |
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| author | Xiaoqi Chen Zhanlong Qu Yuxi Wang Zihao Chen Ganchao Chen Xiao Kang Ying Li |
| author_facet | Xiaoqi Chen Zhanlong Qu Yuxi Wang Zihao Chen Ganchao Chen Xiao Kang Ying Li |
| author_sort | Xiaoqi Chen |
| collection | DOAJ |
| description | Strong shock waves generated during the pyrotechnic separation process of aerospace vehicles can cause high-frequency damage or even structural failure to the vehicle’s structure. Existing structural designs for shock attenuation typically rely on shock response spectra methods, which require multiple finite element calculations to determine the optimal geometric parameters, leading to relatively low efficiency. In this work, we propose a spectral response prediction method for spacecraft structures using the Deep Operator Network (DeepONet). This method preserves the physical relationships between input variables, modularizes geometric and positional input data, and outputs the spectral response. We integrate this neural model to analyze the impact of spacecraft structural parameters on shock resistance performance, revealing that circumferential reinforcement has the most significant influence on shock resistance. Then, we conduct a detailed analysis of the DeepONet model, noting that models with a higher number of neurons per layer train more quickly but are prone to overfitting. Additionally, we find that focusing on specific frequency bands for spectral response prediction yields more accurate results. |
| format | Article |
| id | doaj-art-e3cd54d6156449bb8dd61002b3a7b38e |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-e3cd54d6156449bb8dd61002b3a7b38e2025-08-20T02:17:14ZengMDPI AGAerospace2226-43102025-04-0112431010.3390/aerospace12040310Prediction of Spectral Response for Explosion Separation Based on DeepONetXiaoqi Chen0Zhanlong Qu1Yuxi Wang2Zihao Chen3Ganchao Chen4Xiao Kang5Ying Li6School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430064, ChinaChina Academy of Launch Vehicle Technology, Beijing 100076, ChinaInstitute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, ChinaInstitute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, ChinaInstitute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, ChinaInstitute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, ChinaInstitute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, ChinaStrong shock waves generated during the pyrotechnic separation process of aerospace vehicles can cause high-frequency damage or even structural failure to the vehicle’s structure. Existing structural designs for shock attenuation typically rely on shock response spectra methods, which require multiple finite element calculations to determine the optimal geometric parameters, leading to relatively low efficiency. In this work, we propose a spectral response prediction method for spacecraft structures using the Deep Operator Network (DeepONet). This method preserves the physical relationships between input variables, modularizes geometric and positional input data, and outputs the spectral response. We integrate this neural model to analyze the impact of spacecraft structural parameters on shock resistance performance, revealing that circumferential reinforcement has the most significant influence on shock resistance. Then, we conduct a detailed analysis of the DeepONet model, noting that models with a higher number of neurons per layer train more quickly but are prone to overfitting. Additionally, we find that focusing on specific frequency bands for spectral response prediction yields more accurate results.https://www.mdpi.com/2226-4310/12/4/310explosive separationDeepONetfrequency domain machine learningneural network analysis |
| spellingShingle | Xiaoqi Chen Zhanlong Qu Yuxi Wang Zihao Chen Ganchao Chen Xiao Kang Ying Li Prediction of Spectral Response for Explosion Separation Based on DeepONet Aerospace explosive separation DeepONet frequency domain machine learning neural network analysis |
| title | Prediction of Spectral Response for Explosion Separation Based on DeepONet |
| title_full | Prediction of Spectral Response for Explosion Separation Based on DeepONet |
| title_fullStr | Prediction of Spectral Response for Explosion Separation Based on DeepONet |
| title_full_unstemmed | Prediction of Spectral Response for Explosion Separation Based on DeepONet |
| title_short | Prediction of Spectral Response for Explosion Separation Based on DeepONet |
| title_sort | prediction of spectral response for explosion separation based on deeponet |
| topic | explosive separation DeepONet frequency domain machine learning neural network analysis |
| url | https://www.mdpi.com/2226-4310/12/4/310 |
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