Journal of the Korean Society of Visualization (한국가시화정보학회지)

The Korean Society of Visualization (한국가시화정보학회)
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Pixel level prediction of dynamic pressure distribution on hull surface based on convolutional neural network

합성곱 신경망 기반 선체 표면 압력 분포의 픽셀 수준 예측

  • Received : 2022.07.04
  • Accepted : 2022.07.26
  • Published : 2022.07.31
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Abstract

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

Keywords

Acknowledgement

이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 한국 연구재단의 지원을 받아 수행된 연구임(No. 2022R1A2C2010821).

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