Interpolation Based Reduced Order Model of Unsteady Aerodynamics

Researcher: Rahul Halder

Sponsor: Airbus

  • Unsteady Aerodynamics involves nonlinearity due to large shock motion over the wing surface and viscous interaction and therefore high fidelity computationally expensive aerodynamic solver like computational fluid dynamics-based model is essential for the accurate prediction of aerodynamic load and moment at transonic regime. Whereas a Machine Learning based Interpolation Reduced Order Model can predict the aerodynamic loads and moments at several orders of lower computational cost at desirable accuracy.
  • Development of Neural Network based linearized Aeroelastic Solver for the prediction of the Aeroelastic Instabilities.
  • Neural network based Parametric Reduced Order Model development.