This study investigates uncertainty quantification for field-level ship hull surface pressure predictions using a U-Net-based data-driven model. A speed-conditioned U-Net is trained on a large CFD dataset covering multiple ship types and velocity conditions to predict pressure distributions on hull surfaces. The model outputs the mean pressure and log-variance at each grid location using a negative log-likelihood loss, allowin...

Recent developments in machine learning have enabled prediction models that estimate not only hydrodynamic force coefficients but also full CFD fields. Unlike conventional surrogate models that focus primarily on integrated quantities, such approaches can provide real-time predictions of pressure and wall shear stress distributions, making them highly promising for applications in ship hydrodynamic design where detailed surfac...

Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the e...

In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance and wake distribution based on the positions of flow control fins (FCFs) applied to containerships of various sizes. Both methods utilized data collected through computational fluid dynamics (CFD) analysis. The position of the flow control fin (FCF) and hull form information were utilized as input data...

In this study, the optimal design of flow control fins (FCFs) for a container ship was carried out via a machine learning approach. The conventional design practice for the FCF relied on simulation-based performance evaluation, which demands a large amount of analysis time. Instead of computational fluid dynamics (CFD)-based prediction, artificial neural network (ANN)-based prediction was attempted. Prior to the machine learni...