Uncertainty Quantification of the Neutronics Model of the U-Battery with Reduced Models

High-fidelity models are computationally intensive to work with in many-query applications, such as the design process of small modular reactors. A reduced order model of the high-fidelity model can still accurately determine the quantities of interest with only a fraction of the computational cost, and thus can potentially solve the...

Nonintrusive reduced order modelling using locally adaptive sparse grids and local basis interpolation

Sensitivity analysis and uncertainty quantification of nuclear reactors requires many expensive high-fidelity simulations. To approximate the dynamics of such a time and parameter dependent system efficiently and effectively, reduced order modelling (ROM) is used. In previous research, a ROM was constructed which used a combination of proper...

A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems

We present an approach to build a reduced-order model for nonlinear, time-dependent, parametrized partial differential equations in a nonintrusive manner. The approach is based on combining proper orthogonal decomposition (POD) with a Smolyak hierarchical interpolation model for the POD coefficients. The sampling of the high-fidelity model to...

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...