Physics Informed Machine Learning for Accelerated Ship Hydrodynamic Optimization

Prosjektet har primært fokusert på å bygge en pålitelig metode for å beregne skrogmotstand uten vedheng, ved hjelp av open source- verktøyet OpenFOAM. Resultater for en modell av R/V Gunnerus ser lovende ut sammelignet med resultatene fra det kommersielle verktøyet STARCCM+ og modelltestdata fra Sintef OCEAN. Den resulterende beregningsmodellen vil være basis for å bygge opp et CFD datasett av høy kvalitet.

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".