4 papers · 1 filter
Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
Henning Schwarz, Pyei Phyo Lin, Jens-Peter M. Zemke +1
Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dynamics. In recent years, the u…
Data-driven pressure field prediction for ships in regular sea states
Malte Loft, Henning Schwarz, Thomas Rung
Merchant shipping is responsible for more than 90% of the global trade and has a significant environmental impact, accounting for over 2% of global greenhouse gas emissions. Theref…
Monolithic 3D numerical modeling of granular cargo movement on bulk carriers in waves
Wibke Düsterhöft-Wriggers, Thomas Rung
A novel monolithic approach for simulating vessels in waves with granular cargo is presented using a Finite Volume framework. This model integrates a three-phase Volume of Fluid me…
A two-phase Volume of Fluid approach to model rigid-perfectly plastic granular materials
Wibke Düsterhöft-Wriggers, Svenja Schubert, Thomas Rung
Granular flow problems characterized by large deformations are widespread in various applications, including coastal and geotechnical engineering. The paper deals with the applicat…