A novel wasserstein autoencoder-enhanced thermo-mechanical coupled reduced-order model for high pressure turbine blades life monitoring
To facilitate effective assessment of loads (e.g., temperature and stress) and life management of high-pressure turbine (HPT) blades, a wasserstein autoencoder (WAE)-enhanced thermodynamically coupled reduced-order model (ROM) is proposed in this paper. The advanced ROM for the nonlinear thermomecha...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 152; s. 110819 |
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| Jazyk: | angličtina |
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Elsevier Ltd
15.07.2025
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| ISSN: | 0952-1976 |
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| Abstract | To facilitate effective assessment of loads (e.g., temperature and stress) and life management of high-pressure turbine (HPT) blades, a wasserstein autoencoder (WAE)-enhanced thermodynamically coupled reduced-order model (ROM) is proposed in this paper. The advanced ROM for the nonlinear thermomechanical coupling fields is developed by introducing the deep learning model of WAE in the proper orthogonal decomposition (POD) method. The proposed method improves the prediction accuracy of loads in locally focused regions and generalization performance. The accuracy and efficiency of this method are validated through 30 sets of validation conditions. Results indicate that the proposed approach achieves higher accuracy and better generalization performance than traditional POD-based methods, with errors maintained within 10. Additionally, computational speed is improved by nearly 1400 times compared to conventional numerical methods. The WAE-enhanced ROM is applied for load and life assessment of the HPT blades throughout their service life. The evaluation time for a single aeroengine performance parameter is 1.7 s, and for a single flight evaluation, it is 67 s, which highlights the effectiveness of the proposed method in enabling the assessment of the loads and remaining life of HPT blades.
•A novel WAE enhanced thermal coupling ROM is proposed for efficient HPT blade life assessment.•Through the WAE network, lots of POD modes are preserved and compressed into a low-dimensional latent variable space.•The ANN maps the performance parameters to the mode coefficients for fast and accurate temperature and stress evaluation.•The ROM is evaluated in 1.7 seconds for each operating condition and 67 seconds for each flight. |
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| AbstractList | To facilitate effective assessment of loads (e.g., temperature and stress) and life management of high-pressure turbine (HPT) blades, a wasserstein autoencoder (WAE)-enhanced thermodynamically coupled reduced-order model (ROM) is proposed in this paper. The advanced ROM for the nonlinear thermomechanical coupling fields is developed by introducing the deep learning model of WAE in the proper orthogonal decomposition (POD) method. The proposed method improves the prediction accuracy of loads in locally focused regions and generalization performance. The accuracy and efficiency of this method are validated through 30 sets of validation conditions. Results indicate that the proposed approach achieves higher accuracy and better generalization performance than traditional POD-based methods, with errors maintained within 10. Additionally, computational speed is improved by nearly 1400 times compared to conventional numerical methods. The WAE-enhanced ROM is applied for load and life assessment of the HPT blades throughout their service life. The evaluation time for a single aeroengine performance parameter is 1.7 s, and for a single flight evaluation, it is 67 s, which highlights the effectiveness of the proposed method in enabling the assessment of the loads and remaining life of HPT blades.
•A novel WAE enhanced thermal coupling ROM is proposed for efficient HPT blade life assessment.•Through the WAE network, lots of POD modes are preserved and compressed into a low-dimensional latent variable space.•The ANN maps the performance parameters to the mode coefficients for fast and accurate temperature and stress evaluation.•The ROM is evaluated in 1.7 seconds for each operating condition and 67 seconds for each flight. |
| ArticleNumber | 110819 |
| Author | Hu, Dianyin Chen, Gaoxiang Jiang, Zhimin Wang, Xuemin Shen, Tianbao Wang, Rongqiao Chen, Ruoqi Zhao, Yan |
| Author_xml | – sequence: 1 givenname: Rongqiao surname: Wang fullname: Wang, Rongqiao organization: School of Energy and Power Engineering, Beihang University, Beijing, 100191, China – sequence: 2 givenname: Ruoqi orcidid: 0009-0005-0423-2217 surname: Chen fullname: Chen, Ruoqi organization: School of Energy and Power Engineering, Beihang University, Beijing, 100191, China – sequence: 3 givenname: Yan surname: Zhao fullname: Zhao, Yan email: zy_buaa@buaa.edu.cn organization: School of Energy and Power Engineering, Beihang University, Beijing, 100191, China – sequence: 4 givenname: Tianbao surname: Shen fullname: Shen, Tianbao organization: Research Institute of Aeroengine, Beihang University, Beijing, 100191, China – sequence: 5 givenname: Gaoxiang surname: Chen fullname: Chen, Gaoxiang email: chengx@buaa.edu.cn organization: Research Institute of Aeroengine, Beihang University, Beijing, 100191, China – sequence: 6 givenname: Dianyin surname: Hu fullname: Hu, Dianyin organization: Beijing Key Laboratory of Aeroengine Structure and Strength, Beijing, 100191, China – sequence: 7 givenname: Zhimin surname: Jiang fullname: Jiang, Zhimin organization: Research Institute of Aeroengine, Beihang University, Beijing, 100191, China – sequence: 8 givenname: Xuemin surname: Wang fullname: Wang, Xuemin organization: China AECC Sichuan Gas Turbine Establishment, Chengdu, 610000, China |
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| Keywords | Wasserstein autoencoder Loading calculation High-pressure turbine blade Lifetime monitoring Reduced order model |
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