Session: 06-05-04 Marine Hydrodynamics - IV

Submission Number: 181347

Comparative Assessment of Advanced Turbulence Models for Submarine Hydrodynamic Performance Prediction 

This study explores the influence of turbulence model selection on the prediction of submarine hydrodynamic performance in computational fluid dynamics (CFD) simulations, with a specific focus on frictional resistance, pressure resistance, and fluid characteristics at various ship speeds. Two turbulence models were compared: the traditional Shear Stress Transport (SST) model and the Anisotropic Shear Stress Transport model (ASST), further modified by Durbin (ASST-Durbin). The analysis revealed that the choice of turbulence model has a significant impact on resistance prediction and the characteristics of the flow field. In particular, the ASST-Durbin model exhibited higher prediction accuracy, outperforming the SST model in a wide range of performance metrics. The ASST-Durbin model showed a strong correlation with experimental data, indicating its superior accuracy and reliability in simulating complex flow phenomena. It was especially effective in predicting frictional resistance, as it demonstrated a strong agreement with experimental results in regions of the submarine, such as the bow and stern, where complex flow features like flow separation and trailing vortices are present. The study used the Defense Advanced Research Projects Agency (DARPA) Submarine Universal Body Fully Appended (SUBOFF) standard submarine model to conduct simulations, further emphasizing the ASST-Durbin model’s suitability for analyzing intricate flow fields around submarines.

Presenting Author: Zhengyang Pang Shanghai Jiaotong University

Presenting Author Biography: His research focuses on computational fluid dynamics and hydrodynamic modeling of submarines.

Authors:

Zhengyang Pang Shanghai Jiaotong University
Yunxiang You Shanghai Jiaotong University
Panpan Han Shanghai Jiaotong University
Xiaoping Qiu Shanghai Junyu Information Technology Limited
Qiao Ma Shanghai Junyu Information Technology Limited
Kaijian Wu Shanghai Junyu Information Technology Limited