Hybrid reduced order modeling for assembly calculations

Abstract

While the accuracy of assembly calculations has considerably improved due to the increase in computer power enabling more refined description of the phase space and use of more sophisticated numerical algorithms, the computational cost continues to increase which limits the full utilization of their effectiveness for routine engineering analysis. Reduced order modeling is a mathematical vehicle that scales down the dimensionality of large-scale numerical problems to enable their repeated executions on small computing environment, often available to end users. This is done by capturing the most dominant underlying relationships between the model's inputs and outputs. Previous works demonstrated the use of the reduced order modeling for a single physics code, such as a radiation transport calculation. This manuscript extends those works to coupled code systems as currently employed in assembly calculations. Numerical tests are conducted using realistic SCALE assembly models with resonance self-shielding, neutron transport, and nuclides transmutation/depletion models representing the components of the coupled code system.

Development of a Reduced Order Model for Fuel Burnup Analysis

“…In the nuclear field, POD has been successfully applied to neutronics [21][22][23], thermal-hydraulics [24], sensitivity and uncertainty quantification [25,26]. Notwithstanding, there is a lack of literature in the employment in burnup calculations, nevertheless it would be necessary to solve the issue of computational burden previously discussed.…”

An Adjoint Proper Orthogonal Decomposition method for a neutronics reduced order model

“…The adoption of Reduced Order Methods (ROMs) (Hesthaven et al, 2016;Rozza et al, 2008) can be suitable for this aim, especially in the areas of process optimization, control or uncertainty quantification (Chinesta et al, 2016;Gunzburger, 2002;Quarteroni et al, 2011). The interest in Reduced Order Methods for the simulation of complex systems in nuclear field is increased in the last years, being applied to Monte Carlo methods (Aufiero et al, 2016;Aufiero and Fratoni, 2017), to the deterministic transport equations (Bang et al, 2015;Buchan et al, 2015), and diffusion problems (Buchan et al, 2013;Gong et al, 2016;Lorenzi et al, 2015;Sartori et al, 2014). The fields of application of ROMs are not limited to the neutronics modelling but they are employed also in sensitivity analysis and uncertainty quantification (Abdel-Khalik et al, 2013;Bang et al, 2012a) and in thermal-hydraulic context (Lorenzi et al, 2017(Lorenzi et al, , 2016.…”