Colloquium: Anastasios Panagiotopoulos (FPT)

Reduced Order Models (ROMs) have been combined with Computational Fluid Dynamics (CFD) data to predict an aircraft's dynamics in all possible maneuvers. ROMs enable the efficient utilization of high-fidelity CFD data, providing valuable insights into flight dynamic effects. This thesis project took place at the Netherlands Aerospace Center (NLR). The NLR in cooperation with TUDelft, has developed a ROM method for predicting unsteady aerodynamic loads of air vehicles. The current ROM approach combines the Proper Orthogonal Decomposition (POD) of pressure distribution with a Long Short-Term Memory (LSTM) type Neural Network (NN). So far, the POD-LSTM ROM method predicts the pressure distribution well in the incompressible flow regime. However, the increased number of spatial POD modes required to accurately represent the shock discontinuities in a pressure distribution poses challenges to POD-LSTM ROM. This leads to high computational costs, rendering the application of the POD-LSTM ROM in transonic flows impractical. Therefore, this thesis aims to set the foundation for expanding the POD-LSTM ROM for predicting the pressure distribution over sections of the DLR-F22 model in transonic conditions. This research introduced a novel approach to address the increased number of spatial POD modes needed to approximate shock discontinuities in transonic flows. The enriched Proper Orthogonal Decomposition (ePOD) method introduces an enrichment basis into the standard truncated POD basis. The enrichment basis explicitly accounts for pressure discontinuities caused by shock waves, allowing the standard basis to focus on representing the remaining pressure distribution. The results confirm that the ePOD reduces the DoF required to approximate pressure distribution in transonic flows. An LSTM neural network was utilized to forecast the time-dependent coefficients and parameters of the enriched reduced-order basis in unseen flow conditions. The results also showed that the ePOD reduced the complexity of the time-variant parameters of the reduced-order basis compared to the standard POD with the same number of degrees of freedom (DoF), facilitating more efficient training of the neural network.

Supervisor: Dr S.J. Hulshoff