Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance
The application of corrugated steel sandwich panels on ships requires excellent structural performance in impact resistance, which is often achieved by increasing the weight without giving full play to the characteristics of the structure. Considering the mechanical properties of sandwich panels und...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/11/9/1378 |
| _version_ | 1797518190365900800 |
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| author | Li Ke Kun Liu Guangming Wu Zili Wang Peng Wang |
| author_facet | Li Ke Kun Liu Guangming Wu Zili Wang Peng Wang |
| author_sort | Li Ke |
| collection | DOAJ |
| description | The application of corrugated steel sandwich panels on ships requires excellent structural performance in impact resistance, which is often achieved by increasing the weight without giving full play to the characteristics of the structure. Considering the mechanical properties of sandwich panels under static and impact loading, a multi-objective optimal method based on a back-propagation (BP) neural network and a genetic algorithm developed in MATLAB is proposed herein. The evaluation criteria for this method included structural mass, static and dynamic stress, static and dynamic deformation, and energy absorption. Before optimization, representative sample points were obtained through numerical simulation calculations. Then, the functional relationship between the design and output variables was generated using the BP neural network. Finally, a standard genetic algorithm (SGA) and an adaptive genetic algorithm (AGA) were used for multi-objective optimization analysis with the established function to obtain the best result. Through this study, a new design concept with high efficiency and reliability was developed to determine the structural parameters that provide the best impact resistance using limited sample points. |
| first_indexed | 2024-03-10T07:26:41Z |
| format | Article |
| id | doaj.art-0a153f471a43487da414df63560ed28c |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T07:26:41Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-0a153f471a43487da414df63560ed28c2023-11-22T14:12:57ZengMDPI AGMetals2075-47012021-08-01119137810.3390/met11091378Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact ResistanceLi Ke0Kun Liu1Guangming Wu2Zili Wang3Peng Wang4State Key Laboratory for Disaster Prevention & Mitigation of Explosion & Impact, Army Engineering University of PLA, Nanjing 210007, ChinaSchool of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaChina Ship Development and Design Center, Shanghai 201108, ChinaSchool of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaState Key Laboratory for Disaster Prevention & Mitigation of Explosion & Impact, Army Engineering University of PLA, Nanjing 210007, ChinaThe application of corrugated steel sandwich panels on ships requires excellent structural performance in impact resistance, which is often achieved by increasing the weight without giving full play to the characteristics of the structure. Considering the mechanical properties of sandwich panels under static and impact loading, a multi-objective optimal method based on a back-propagation (BP) neural network and a genetic algorithm developed in MATLAB is proposed herein. The evaluation criteria for this method included structural mass, static and dynamic stress, static and dynamic deformation, and energy absorption. Before optimization, representative sample points were obtained through numerical simulation calculations. Then, the functional relationship between the design and output variables was generated using the BP neural network. Finally, a standard genetic algorithm (SGA) and an adaptive genetic algorithm (AGA) were used for multi-objective optimization analysis with the established function to obtain the best result. Through this study, a new design concept with high efficiency and reliability was developed to determine the structural parameters that provide the best impact resistance using limited sample points.https://www.mdpi.com/2075-4701/11/9/1378corrugated steel sandwich panelimpact resistancemulti-objective optimal designBP neural networkgenetic algorithm |
| spellingShingle | Li Ke Kun Liu Guangming Wu Zili Wang Peng Wang Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance Metals corrugated steel sandwich panel impact resistance multi-objective optimal design BP neural network genetic algorithm |
| title | Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance |
| title_full | Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance |
| title_fullStr | Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance |
| title_full_unstemmed | Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance |
| title_short | Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance |
| title_sort | multi objective optimization design of corrugated steel sandwich panel for impact resistance |
| topic | corrugated steel sandwich panel impact resistance multi-objective optimal design BP neural network genetic algorithm |
| url | https://www.mdpi.com/2075-4701/11/9/1378 |
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