Search NASA⌕ Search

DOE OSTI · 1020281

A Coupling Methodology for Mesoscale-informed Nuclear Fuel Performance Codes

Abstract

This study proposes an approach for capturing the effect of microstructural evolution on reactor fuel performance by coupling a mesoscale irradiated microstructure model with a finite element fuel performance code. To achieve this, the macroscale system is solved in a parallel, fully coupled, fully-implicit manner using the preconditioned Jacobian-free Newton Krylov (JFNK) method. Within the JFNK solution algorithm, microstructure-influenced material parameters are calculated by the mesoscale model and passed back to the macroscale calculation. Due to the stochastic nature of the mesoscale model, a dynamic fitting technique is implemented to smooth roughness in the calculated material parameters. The proposed methodology is demonstrated on a simple model of a reactor fuel pellet. In the model, INL’s BISON fuel performance code calculates the steady-state temperature profile in a fuel pellet and the microstructure-influenced thermal conductivity is determined with a phase field model of irradiated microstructures. This simple multiscale model demonstrates good nonlinear convergence and near ideal parallel scalability. By capturing the formation of large mesoscale voids in the pellet interior, the multiscale model predicted the irradiation-induced reduction in the thermal conductivity commonly observed in reactors.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tonks, Michael, Gaston, Derek, Permann, Cody, Millett, Paul, Hansen, Glen, Wolf, Dieter. 2010-10-01. A Coupling Methodology for Mesoscale-informed Nuclear Fuel Performance Codes. https://doi.org/10.1016/j.nucengdes.2010.06.005

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗