Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model
For practical problems with non-convex, large-scale and highly constrained characteristics, evolutionary optimisation algorithms are widely used. However, advanced data-driven methods have yet to be comprehensively applied in related fields. In this study, a surrogate model combined with the Non-dom...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/9/1/18 |
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| author | Jing-Wei Jiang Yang Yang Tong-Wei Ren Fei Wang Wei-Xi Huang |
| author_facet | Jing-Wei Jiang Yang Yang Tong-Wei Ren Fei Wang Wei-Xi Huang |
| author_sort | Jing-Wei Jiang |
| collection | DOAJ |
| description | For practical problems with non-convex, large-scale and highly constrained characteristics, evolutionary optimisation algorithms are widely used. However, advanced data-driven methods have yet to be comprehensively applied in related fields. In this study, a surrogate model combined with the Non-dominated Sorting Genetic Algorithm II-Differential Evolution (NSGA-II-DE) is applied to reduce the low-frequency Discrete-Spectrum (DS) force of propeller noise. Reduction of this force has drawn a lot of attention as it is the primary signal used in the sonar-based detection and identification of ships. In the present study, a surrogate model is proposed based on a trained Back-Propagation (BP) fully connected neural network, which improves the optimisation efficiency. The neural network is designed by analysing the depth and width of the hidden layers. The results indicate that a four-layer neural network with 64, 128, 256 and 64 nodes in each layer, respectively, exhibits the highest prediction accuracy. The prediction errors for the first order of DST, second order of DST and the thrust coefficient are only 0.21%, 5.71% and 0.01%, respectively. Data-Driven Evolutionary Optimisation (DDEO) is applied to a standard high-skew propeller to reduce DST. DDEO and a Traditional Evolutionary Optimisation Method (TEOM) obtain the same optimisation results, while the time cost of DDEO is only 0.68% that of the TEOM. Thus, the proposed DDEO is applicable to complex engineering problems in various fields. |
| first_indexed | 2024-03-10T13:45:57Z |
| format | Article |
| id | doaj.art-e1ad06e4758a4fa7a2de3763d7d517f3 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T13:45:57Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-e1ad06e4758a4fa7a2de3763d7d517f32023-11-21T02:37:29ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-12-01911810.3390/jmse9010018Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate ModelJing-Wei Jiang0Yang Yang1Tong-Wei Ren2Fei Wang3Wei-Xi Huang4Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, ChinaOffice of Science and Technology, Nanjing University, Nanjing 210046, ChinaState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, ChinaAdvanced Propulsion Technology Research Department, China Marine Development and Research Centre, Beijing 100161, ChinaApplied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, ChinaFor practical problems with non-convex, large-scale and highly constrained characteristics, evolutionary optimisation algorithms are widely used. However, advanced data-driven methods have yet to be comprehensively applied in related fields. In this study, a surrogate model combined with the Non-dominated Sorting Genetic Algorithm II-Differential Evolution (NSGA-II-DE) is applied to reduce the low-frequency Discrete-Spectrum (DS) force of propeller noise. Reduction of this force has drawn a lot of attention as it is the primary signal used in the sonar-based detection and identification of ships. In the present study, a surrogate model is proposed based on a trained Back-Propagation (BP) fully connected neural network, which improves the optimisation efficiency. The neural network is designed by analysing the depth and width of the hidden layers. The results indicate that a four-layer neural network with 64, 128, 256 and 64 nodes in each layer, respectively, exhibits the highest prediction accuracy. The prediction errors for the first order of DST, second order of DST and the thrust coefficient are only 0.21%, 5.71% and 0.01%, respectively. Data-Driven Evolutionary Optimisation (DDEO) is applied to a standard high-skew propeller to reduce DST. DDEO and a Traditional Evolutionary Optimisation Method (TEOM) obtain the same optimisation results, while the time cost of DDEO is only 0.68% that of the TEOM. Thus, the proposed DDEO is applicable to complex engineering problems in various fields.https://www.mdpi.com/2077-1312/9/1/18data-driven evolutionary optimisationlow-frequency discrete-spectrum forcepropeller noiseneural network |
| spellingShingle | Jing-Wei Jiang Yang Yang Tong-Wei Ren Fei Wang Wei-Xi Huang Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model Journal of Marine Science and Engineering data-driven evolutionary optimisation low-frequency discrete-spectrum force propeller noise neural network |
| title | Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model |
| title_full | Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model |
| title_fullStr | Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model |
| title_full_unstemmed | Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model |
| title_short | Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model |
| title_sort | evolutionary optimisation for reduction of the low frequency discrete spectrum force of marine propeller based on a data driven surrogate model |
| topic | data-driven evolutionary optimisation low-frequency discrete-spectrum force propeller noise neural network |
| url | https://www.mdpi.com/2077-1312/9/1/18 |
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