Neural Network-based Optimization of Solid-and Fluid Mechanical Simulations

The following project deals with the optimization of simulation parameters such as the injection location and pitch angle of polyurethane foaming simulations using artificial neural networks. The model's target is to predict quality variables based on the process parameters and the geometry features. Through several evaluations of the model, good parameter combinations can be found which in turn can be used as good initial guesses by high fidelity optimization tools. For handling different mould geometries, a meshing tool has been programmed which transforms variable-sized surface meshes into voxel meshes. Cross-section images of the meshes are then passed together with a series of simulation settings to the neural network which processes the data streams into one set of predictions. The model has been implemented using the TensorFlow interface and trained with a custom generated data set of roughly 10000 samples. The results show well-matching prediction and simulation profiles for the validation cases. The magnitudes of the quality parameters often differ, but the especially relevant areas of optimal injection points are well covered. Good results together with a small model size provide evidence for a feasible and successful extension towards a full 3D application.Double Degree Master with TU Delft in Computer Simulations for Science and EngineeringApplied Mathematics | COSSE (Computer Simulations for Science and Engineering