Real-time simulation of fluid flows for digital twins of LNG tankers

During transportation of Liquefied Natural Gas (LNG) in tanker ships, violent liquid sloshing can appear. We propose a digital twin to predict in real-time the operational envelope of a tanker. A critical part of the digital twin is the fluid dynamics model, for which we propose a novel approach: a multi-fidelity neural network. This approach uses a combination of a low- and high-fidelity model to significantly decrease the computational cost. The key element is the presence of the simplified physics in the low-fidelity solver, which results in a neural network that is capable of enhancing low-fidelity solutions also outside the training set.

About the speaker

Yous van Halder studied Applied Mathematics at the University of Technology Eindhoven. Interest in fluid mechanics problems emerged after an internship at the Centre for Turbulence Research (Stanford University). In 2016 he joined the SLING program as a PhD at CWI in Amsterdam, and he works on developing deep-learning based uncertainty quantification methods for fluid mechanics problems. He is supervised by dr. ir. B. Sanderse and prof. dr. ir. B. Koren.