Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning
The Fabry–Pérot (FP) cavity is the essential component of an ultra-stable laser (USL) for gravitational wave detection, which couples multiple physics fields (optical/thermal/mechanical) and requires ultra-high precision. Aiming at the deficiency of the current single physical field optimization, a...
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13115 |
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| author | Hang Zhao Fanchao Meng Zhongge Wang Xiongfei Yin Lingqiang Meng Jianjun Jia |
| author_facet | Hang Zhao Fanchao Meng Zhongge Wang Xiongfei Yin Lingqiang Meng Jianjun Jia |
| author_sort | Hang Zhao |
| collection | DOAJ |
| description | The Fabry–Pérot (FP) cavity is the essential component of an ultra-stable laser (USL) for gravitational wave detection, which couples multiple physics fields (optical/thermal/mechanical) and requires ultra-high precision. Aiming at the deficiency of the current single physical field optimization, a multi-physics and multi-objective optimization method for fixing the cubic FP cavity based on data learning is proposed in this paper. A multi-physics coupling model for the cubic FP cavity is established and the performance is obtained via finite element analysis. The key performance indices (<i>V</i>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>w</mi><mi>F</mi></msub></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>w</mi><mi>F</mi></msub></semantics></math></inline-formula>) and key design variables (<i>d</i>, <i>l</i>, <i>F</i>) are determined considering the features of the FP cavity. Different data learning models (NN, RSF, KRG) are established and compared based on 49 sets of data acquired by orthogonal experiments, with the results showing that the neural network has the best performance. NSGA-II is adopted as the optimization algorithm, the Pareto optimal front is obtained, and the optimal combination of design variables is finally determined as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>{</mo><mn>5</mn><mo>,</mo><mn>32</mn><mo>,</mo><mn>250</mn><mo>}</mo></mrow></semantics></math></inline-formula>. The performance after optimization proves to be greatly improved, with the displacement under the fixing force and vibration test both decreased by more than 60%. The proposed optimization strategy can help in the design of the FP cavity, and could enlighten other optimization fields as well. |
| first_indexed | 2024-03-08T21:01:35Z |
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| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:01:35Z |
| publishDate | 2023-12-01 |
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| spelling | doaj.art-6c235ec4bfc04471b1f11b45bcc8a5cb2023-12-22T13:51:30ZengMDPI AGApplied Sciences2076-34172023-12-0113241311510.3390/app132413115Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data LearningHang Zhao0Fanchao Meng1Zhongge Wang2Xiongfei Yin3Lingqiang Meng4Jianjun Jia5School of Physics and Photoelectric Engineering, Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Taiji Laboratory for Gravitational Wave Universe, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, ChinaSchool of Physics and Photoelectric Engineering, Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Taiji Laboratory for Gravitational Wave Universe, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, ChinaSchool of Physics and Photoelectric Engineering, Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Taiji Laboratory for Gravitational Wave Universe, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, ChinaSchool of Physics and Photoelectric Engineering, Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Taiji Laboratory for Gravitational Wave Universe, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, ChinaSchool of Physics and Photoelectric Engineering, Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Taiji Laboratory for Gravitational Wave Universe, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, ChinaSchool of Physics and Photoelectric Engineering, Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Taiji Laboratory for Gravitational Wave Universe, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, ChinaThe Fabry–Pérot (FP) cavity is the essential component of an ultra-stable laser (USL) for gravitational wave detection, which couples multiple physics fields (optical/thermal/mechanical) and requires ultra-high precision. Aiming at the deficiency of the current single physical field optimization, a multi-physics and multi-objective optimization method for fixing the cubic FP cavity based on data learning is proposed in this paper. A multi-physics coupling model for the cubic FP cavity is established and the performance is obtained via finite element analysis. The key performance indices (<i>V</i>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>w</mi><mi>F</mi></msub></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>w</mi><mi>F</mi></msub></semantics></math></inline-formula>) and key design variables (<i>d</i>, <i>l</i>, <i>F</i>) are determined considering the features of the FP cavity. Different data learning models (NN, RSF, KRG) are established and compared based on 49 sets of data acquired by orthogonal experiments, with the results showing that the neural network has the best performance. NSGA-II is adopted as the optimization algorithm, the Pareto optimal front is obtained, and the optimal combination of design variables is finally determined as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>{</mo><mn>5</mn><mo>,</mo><mn>32</mn><mo>,</mo><mn>250</mn><mo>}</mo></mrow></semantics></math></inline-formula>. The performance after optimization proves to be greatly improved, with the displacement under the fixing force and vibration test both decreased by more than 60%. The proposed optimization strategy can help in the design of the FP cavity, and could enlighten other optimization fields as well.https://www.mdpi.com/2076-3417/13/24/13115FP cavitymulti-physics couplingfinite element methoddata learningsurrogate modelevolutionary algorithm |
| spellingShingle | Hang Zhao Fanchao Meng Zhongge Wang Xiongfei Yin Lingqiang Meng Jianjun Jia Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning Applied Sciences FP cavity multi-physics coupling finite element method data learning surrogate model evolutionary algorithm |
| title | Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning |
| title_full | Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning |
| title_fullStr | Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning |
| title_full_unstemmed | Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning |
| title_short | Multi-Physics and Multi-Objective Optimization for Fixing Cubic Fabry–Pérot Cavities Based on Data Learning |
| title_sort | multi physics and multi objective optimization for fixing cubic fabry perot cavities based on data learning |
| topic | FP cavity multi-physics coupling finite element method data learning surrogate model evolutionary algorithm |
| url | https://www.mdpi.com/2076-3417/13/24/13115 |
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