A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks
The estimation of wind loads on ships and other marine objects represents a continuous challenge because of its implication for various aspects of exposed structure exploitation. An extended method for estimating the wind loads on container ships is presented. The method uses the Generalized Regress...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/8/7/539 |
| _version_ | 1797561903748218880 |
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| author | Jasna Prpić-Oršić Marko Valčić Zoran Čarija |
| author_facet | Jasna Prpić-Oršić Marko Valčić Zoran Čarija |
| author_sort | Jasna Prpić-Oršić |
| collection | DOAJ |
| description | The estimation of wind loads on ships and other marine objects represents a continuous challenge because of its implication for various aspects of exposed structure exploitation. An extended method for estimating the wind loads on container ships is presented. The method uses the Generalized Regression Neural Network (GRNN), which is trained with Elliptic Fourier Descriptors (EFD) of sets of frontal and lateral closed contours as inputs. Wind load coefficients (C<sub>x</sub>, C<sub>y</sub>, C<sub>N</sub>), used as outputs for network training, are derived from 3D steady RANS CFD analysis. This approach is very suitable for assessing wind loads on container ships wherever there is a wind load database for a various container configuration. In this way, the cheaper and faster calculation can bridge the gap for the container configurations for which calculations or experiments have not already been made. The results obtained by trained GRNN are in line with available experimental measurements of the wind loads on various container configuration on the deck of a 9000+ TEU container ship obtained through a series of wind tunnel tests, as well as with performed CFD simulation for the same conditions. |
| first_indexed | 2024-03-10T18:21:12Z |
| format | Article |
| id | doaj.art-cbc1d5a151a3432793e82ad35d0db0f0 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T18:21:12Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-cbc1d5a151a3432793e82ad35d0db0f02023-11-20T07:18:36ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-07-018753910.3390/jmse8070539A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural NetworksJasna Prpić-Oršić0Marko Valčić1Zoran Čarija2Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaThe estimation of wind loads on ships and other marine objects represents a continuous challenge because of its implication for various aspects of exposed structure exploitation. An extended method for estimating the wind loads on container ships is presented. The method uses the Generalized Regression Neural Network (GRNN), which is trained with Elliptic Fourier Descriptors (EFD) of sets of frontal and lateral closed contours as inputs. Wind load coefficients (C<sub>x</sub>, C<sub>y</sub>, C<sub>N</sub>), used as outputs for network training, are derived from 3D steady RANS CFD analysis. This approach is very suitable for assessing wind loads on container ships wherever there is a wind load database for a various container configuration. In this way, the cheaper and faster calculation can bridge the gap for the container configurations for which calculations or experiments have not already been made. The results obtained by trained GRNN are in line with available experimental measurements of the wind loads on various container configuration on the deck of a 9000+ TEU container ship obtained through a series of wind tunnel tests, as well as with performed CFD simulation for the same conditions.https://www.mdpi.com/2077-1312/8/7/539wind loadscontainer shipsReynolds-averaged Navier–Stokes equations (RANS)Generalized Regression Neural Network (GRNN) |
| spellingShingle | Jasna Prpić-Oršić Marko Valčić Zoran Čarija A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks Journal of Marine Science and Engineering wind loads container ships Reynolds-averaged Navier–Stokes equations (RANS) Generalized Regression Neural Network (GRNN) |
| title | A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks |
| title_full | A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks |
| title_fullStr | A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks |
| title_full_unstemmed | A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks |
| title_short | A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks |
| title_sort | hybrid wind load estimation method for container ship based on computational fluid dynamics and neural networks |
| topic | wind loads container ships Reynolds-averaged Navier–Stokes equations (RANS) Generalized Regression Neural Network (GRNN) |
| url | https://www.mdpi.com/2077-1312/8/7/539 |
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