Shape Optimization with a Flattening-Based Morphing Method
In shape optimization problems, generating variously shaped designs is an important task. In this study, a new design method called the flattening-based morphing method, which can create various designs efficiently based on baseline objects, is proposed. In the flattening-based method, anchor points...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6565 |
| _version_ | 1797480954818723840 |
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| author | Honghee Kim Sahuck Oh |
| author_facet | Honghee Kim Sahuck Oh |
| author_sort | Honghee Kim |
| collection | DOAJ |
| description | In shape optimization problems, generating variously shaped designs is an important task. In this study, a new design method called the flattening-based morphing method, which can create various designs efficiently based on baseline objects, is proposed. In the flattening-based method, anchor points are defined for each baseline object to set correspondence among the baseline objects, and each baseline object is mapped to 2D parametric space in a way that places all corresponding anchor points of the baseline objects at the same location. Then, remeshing is carried out to make the baseline objects’ mesh topologically identical in the parametric space. After these remeshed baseline objects are parameterized back to the physical space, the morphed object is created by computing the positions of its vertices as a weighted sum of the baseline meshes’ vertices. When the flattening-based morphing method is applied to find the optimal shape of a blended-wing body aircraft using an artificial neural network (ANN), the aerodynamic performance enhanced optimal model with an appropriate loading capacity is successfully achieved using three baseline models. The simulation results of the baseline models and optimization results are also provided in the current study. |
| first_indexed | 2024-03-09T22:07:36Z |
| format | Article |
| id | doaj.art-90ce76de41354cb4a3d77968e791d0d3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:07:36Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-90ce76de41354cb4a3d77968e791d0d32023-11-23T19:38:54ZengMDPI AGApplied Sciences2076-34172022-06-011213656510.3390/app12136565Shape Optimization with a Flattening-Based Morphing MethodHonghee Kim0Sahuck Oh1School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 10540, KoreaSchool of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 10540, KoreaIn shape optimization problems, generating variously shaped designs is an important task. In this study, a new design method called the flattening-based morphing method, which can create various designs efficiently based on baseline objects, is proposed. In the flattening-based method, anchor points are defined for each baseline object to set correspondence among the baseline objects, and each baseline object is mapped to 2D parametric space in a way that places all corresponding anchor points of the baseline objects at the same location. Then, remeshing is carried out to make the baseline objects’ mesh topologically identical in the parametric space. After these remeshed baseline objects are parameterized back to the physical space, the morphed object is created by computing the positions of its vertices as a weighted sum of the baseline meshes’ vertices. When the flattening-based morphing method is applied to find the optimal shape of a blended-wing body aircraft using an artificial neural network (ANN), the aerodynamic performance enhanced optimal model with an appropriate loading capacity is successfully achieved using three baseline models. The simulation results of the baseline models and optimization results are also provided in the current study.https://www.mdpi.com/2076-3417/12/13/6565flattening-based morphing methodartificial neural networkoptimizationblended-wing bodylift-to-drag ratio |
| spellingShingle | Honghee Kim Sahuck Oh Shape Optimization with a Flattening-Based Morphing Method Applied Sciences flattening-based morphing method artificial neural network optimization blended-wing body lift-to-drag ratio |
| title | Shape Optimization with a Flattening-Based Morphing Method |
| title_full | Shape Optimization with a Flattening-Based Morphing Method |
| title_fullStr | Shape Optimization with a Flattening-Based Morphing Method |
| title_full_unstemmed | Shape Optimization with a Flattening-Based Morphing Method |
| title_short | Shape Optimization with a Flattening-Based Morphing Method |
| title_sort | shape optimization with a flattening based morphing method |
| topic | flattening-based morphing method artificial neural network optimization blended-wing body lift-to-drag ratio |
| url | https://www.mdpi.com/2076-3417/12/13/6565 |
| work_keys_str_mv | AT hongheekim shapeoptimizationwithaflatteningbasedmorphingmethod AT sahuckoh shapeoptimizationwithaflatteningbasedmorphingmethod |