Physics Informed Machine Learning for Accelerated Ship Hydrodynamic Optimization

The project has focused primarily on building a reliable method for calculating bare hull resistance using the open source OpenFOAM solver. Results on a model of R/V Gunnerus look promising compared with those obtained using the commercial solver STARCCM+ and model test data performed at Sintef Ocean. The resulting calculation template will form the basis for building a high quality CFD dataset.

Today due to the time required to perform RANS CFD simulations, the ability to perform optimization is severely restricted. To address this problem this project seeks to explore the possibilities of state-of-the-art machine learning techniques and utilize them to accelerate the ship design optimization problem. It will review state-of-the-art techniques in classical data-driven machine learning. Then seek to improve upon these by adopting promising hybrid approach of physics-informed machine learning, which allows a model to be trained on a combination of raw data and knowledge of the governing laws of physics, like the Reynolds Averaged Navier Stokes equations. To perform the comparison of the various methods open CFD datasets will be produced, including parametric model to study hull variations on NTNU's research vessel RV "Gunnerus".