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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2501.03286 |
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| _version_ | 1866910774033645568 |
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| author | Oh, Sang-jin Kang, Ju Young Pak, Kyungryeong Kim, Heejung Shin, Sung-chul |
| author_facet | Oh, Sang-jin Kang, Ju Young Pak, Kyungryeong Kim, Heejung Shin, Sung-chul |
| contents | Hull form designing is an iterative process wherein the performance of the hull form needs to be checked via computational fluid dynamics calculations or model experiments. The stern shape has to undergo a process wherein the hull form variations from the pressure distribution analysis results are repeated until the resistance and propulsion efficiency meet the design requirements. In this study, the designer designed a pressure distribution that meets the design requirements; this paper proposes an inverse design algorithm that estimates the stern shape using deep learning. A convolutional neural network was used to extract the features of the pressure distribution expressed as a contour, whereas a multi-task learning model was used to estimate various sections of the stern shape. We estimated the stern shape indirectly by estimating the control point of the B-spline and comparing the actual and converted offsets for each section; the performance was verified, and an inverse design is proposed herein |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03286 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inverse Design of Optimal Stern Shape with Convolutional Neural Network-based Pressure Distribution Oh, Sang-jin Kang, Ju Young Pak, Kyungryeong Kim, Heejung Shin, Sung-chul Machine Learning Fluid Dynamics Hull form designing is an iterative process wherein the performance of the hull form needs to be checked via computational fluid dynamics calculations or model experiments. The stern shape has to undergo a process wherein the hull form variations from the pressure distribution analysis results are repeated until the resistance and propulsion efficiency meet the design requirements. In this study, the designer designed a pressure distribution that meets the design requirements; this paper proposes an inverse design algorithm that estimates the stern shape using deep learning. A convolutional neural network was used to extract the features of the pressure distribution expressed as a contour, whereas a multi-task learning model was used to estimate various sections of the stern shape. We estimated the stern shape indirectly by estimating the control point of the B-spline and comparing the actual and converted offsets for each section; the performance was verified, and an inverse design is proposed herein |
| title | Inverse Design of Optimal Stern Shape with Convolutional Neural Network-based Pressure Distribution |
| topic | Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2501.03286 |