G

GNNs

For this project, the idea is to implement a graph neural network (GNN) model with an encoder-processor-decoder architecture and train it using results from mesh-based simulations of a wing in an airflow. The simulations are generated using OpenVSP, a medium-fidelity solver that applies the vortex lattice method to compute the scalar pressure coefficient field across the wing mesh. This field is used to derive aerodynamic curves (lift, drag and moment coefficients) and draw conclusions about the wing profile’s performance. Simulations are automated to cover a wide range of parameters such as Mach number, angle of attack, sweep angle and for different airfoil profile (NACA0012, NACA2412, and NACA23012)

The goal of this project is to compare the results generated by the conventional OpenVSP software with the model’s predictions for new cases not used in training. The limitations of each model as well as the computational resource costs are also studied.

Controversy quantification of topics on twitter, based on user probability to participate in a controversy topic, using GNN and NLP models.