Machine Learning Based local Reduced Order Modeling for the prediction of Unsteady Aerodynamic Loads

Advancements in aircraft performance require increasingly complex design processes and tools. Simulating the unsteady non-linear aerodynamic interaction between a maneuvering aircraft and the surrounding flowfield poses serious challenges. High-Fidelity Computational Fluid Dynamics (CFD) methods, based on the numerical solution of the Navier...

Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis

Large-scale complex systems require high-fidelity models to capture the dynamics of the system accurately. For example, models of nuclear reactors capture multiphysics interactions (e.g., radiation transport, thermodynamics, heat transfer, and fluid mechanics) occurring at various scales of time (prompt neutrons to burn-up calculations) and...

A nonintrusive adaptive reduced order modeling approach for a molten salt reactor system

We use a novel nonintrusive adaptive Reduced Order Modeling method to build a reduced model for a molten salt reactor system. Our approach is based on Proper Orthogonal Decomposition combined with locally adaptive sparse grids. Our reduced model captures the effect of 27 model parameters on k_eff of the system and the spatial...

POD-DEIM model order reduction for strain softening viscoplasticity

We demonstrate a Model Order Reduction technique for a system of nonlinear equations arising from the Finite Element Method (FEM) discretization of the three-dimensional quasistatic equilibrium equation equipped with a Perzyna viscoplasticity constitutive model. The procedure employs the Proper Orthogonal Decomposition-Galerkin (POD-G) in...