6 papers
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…
Disentangled Latent Spaces for Reduced Order Models using Deterministic Autoencoders
Henning Schwarz, Pyei Phyo Lin, Jens-Peter M. Zemke +1
Data-driven reduced-order models based on autoencoders generally lack interpretability compared to classical methods such as the proper orthogonal decomposition. More interpretabil…
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…
Machine Learning based Prediction of Ditching Loads
Henning Schwarz, Micha Überrück, Jens-Peter M. Zemke +1
We present approaches to predict dynamic ditching loads on aircraft fuselages using machine learning. The employed learning procedure is structured into two parts, the reconstructi…
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…