A CFD-Based Data-Driven Reduced Order Modeling Method for Damaged Ship Motion in Waves

A simple CFD-based data-driven reduced order modeling method was proposed for the study of damaged ship motion in waves. It consists of low-order modeling of the whole concerned parameter range and high-order modeling for selected key scenarios identified with the help of low-order results. The difference between the low and high-order results for the whole parameter range, where the main trend of the physics behind the problem is expected to be captured, is then modeled by some commonly used machine learning or data regression methods based on the data from key scenarios which is chosen as Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) in this study. The final prediction is obtained by adding the results from the low-order model and the difference. The low and high-order modeling were conducted through computational fluid dynamics (CFD) simulations with coarse and refined meshes. Taking the roll Response Amplitude Operator (RAO) of a DTMB-5415 ship model with a damaged cabin as an example, the proposed physics-informed data-driven model was shown to have the same level of accuracy as pure high-order modeling, whilst the computational time can be reduced by 22 similar to 55% for the studied cases. This simple reduced order modeling approach is also expected to be applicable to other ship hydrodynamic problems.

Automated summary

A CFD-based data-driven reduced order modeling method was proposed for studying damaged ship motion in waves. It involved low-order modeling of the entire parameter range and high-order modeling for selected key scenarios. The difference between the low and high-order results was then modeled using machine learning or data regression methods. The proposed model showed comparable accuracy to pure high-order modeling while significantly reducing computational time for the studied cases.