Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning
Flow-induced vibration (FIV) is a phenomenon in which the flow passing through a structure exerts periodic forces on the structure. Most studies on FIVs focus on suppressing this phenomenon. However, the Marine Renewable Energy Laboratory (MRELab) at the University of Michigan, USA, has developed a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/15/7163 |
| _version_ | 1827686592810057728 |
|---|---|
| author | Gi-yong Kim Chaeog Lim Eun Soo Kim Sung-chul Shin |
| author_facet | Gi-yong Kim Chaeog Lim Eun Soo Kim Sung-chul Shin |
| author_sort | Gi-yong Kim |
| collection | DOAJ |
| description | Flow-induced vibration (FIV) is a phenomenon in which the flow passing through a structure exerts periodic forces on the structure. Most studies on FIVs focus on suppressing this phenomenon. However, the Marine Renewable Energy Laboratory (MRELab) at the University of Michigan, USA, has developed a technology called the vortex-induced vibration for aquatic clean energy (VIVACE) converters that reinforces FIV and converts the energy in tidal currents to electrical energy. This study introduces the experimental data of the VIVACE converter and the associated method using deep neural networks (DNNs) to predict the dynamic responses of the converter. The DNN was trained and verified with experimental data from the MRELab, and the findings show that the amplitudes and frequencies of a single cylinder in the FIV predicted by the DNN under various test conditions were in good agreement with the experimental data. Finally, based on both the predicted and experimental data, the optimal power envelope of the VIVACE converter was generated as a function of the flow speed. The predictions using DNNs are expected to be more accurate as they can be trained with more experimental data in the future and will help to substantially reduce the number of experiments on FIVs. |
| first_indexed | 2024-03-10T09:18:04Z |
| format | Article |
| id | doaj.art-a3128b0190ac45a3918b9ba444cf66c6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:18:04Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a3128b0190ac45a3918b9ba444cf66c62023-11-22T05:25:22ZengMDPI AGApplied Sciences2076-34172021-08-011115716310.3390/app11157163Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep LearningGi-yong Kim0Chaeog Lim1Eun Soo Kim2Sung-chul Shin3Department of Naval Architecture and Ocean Engineering, Pusan National University, Busan 46241, KoreaDepartment of Naval Architecture and Ocean Engineering, Pusan National University, Busan 46241, KoreaDepartment of Naval Architecture and Ocean Engineering, Pusan National University, Busan 46241, KoreaDepartment of Naval Architecture and Ocean Engineering, Pusan National University, Busan 46241, KoreaFlow-induced vibration (FIV) is a phenomenon in which the flow passing through a structure exerts periodic forces on the structure. Most studies on FIVs focus on suppressing this phenomenon. However, the Marine Renewable Energy Laboratory (MRELab) at the University of Michigan, USA, has developed a technology called the vortex-induced vibration for aquatic clean energy (VIVACE) converters that reinforces FIV and converts the energy in tidal currents to electrical energy. This study introduces the experimental data of the VIVACE converter and the associated method using deep neural networks (DNNs) to predict the dynamic responses of the converter. The DNN was trained and verified with experimental data from the MRELab, and the findings show that the amplitudes and frequencies of a single cylinder in the FIV predicted by the DNN under various test conditions were in good agreement with the experimental data. Finally, based on both the predicted and experimental data, the optimal power envelope of the VIVACE converter was generated as a function of the flow speed. The predictions using DNNs are expected to be more accurate as they can be trained with more experimental data in the future and will help to substantially reduce the number of experiments on FIVs.https://www.mdpi.com/2076-3417/11/15/7163deep learningflow-induced vibration (FIV)renewable energydynamic response |
| spellingShingle | Gi-yong Kim Chaeog Lim Eun Soo Kim Sung-chul Shin Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning Applied Sciences deep learning flow-induced vibration (FIV) renewable energy dynamic response |
| title | Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning |
| title_full | Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning |
| title_fullStr | Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning |
| title_full_unstemmed | Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning |
| title_short | Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning |
| title_sort | prediction of dynamic responses of flow induced vibration using deep learning |
| topic | deep learning flow-induced vibration (FIV) renewable energy dynamic response |
| url | https://www.mdpi.com/2076-3417/11/15/7163 |
| work_keys_str_mv | AT giyongkim predictionofdynamicresponsesofflowinducedvibrationusingdeeplearning AT chaeoglim predictionofdynamicresponsesofflowinducedvibrationusingdeeplearning AT eunsookim predictionofdynamicresponsesofflowinducedvibrationusingdeeplearning AT sungchulshin predictionofdynamicresponsesofflowinducedvibrationusingdeeplearning |