Search NASA⌕ Search

DOE OSTI · 1478317

A Pebble Tracking Transport Algorithm for Pebble Bed Reactor Analysis

Abstract

Pebble bed reactor (PBR) designs pose unique challenges to modeling and simulation. The challenges originate from the multiple levels of heterogeneity partly due to random packing in and flow through the core of fuel pebbles. This paper proposes a novel pebble tracking transport (PTT) algorithm to address some of the challenges facing PBR analysis. Each individual pebble is tracked and modeled on an unstructured mesh of which the nodes are aligned with the pebble centroids. The neutron transport equation is solved on this pebble tracking mesh with pre-assembled elemental matrices. These elemental matrices are independent of the cross sections and the streaming directions, thus can be assembled before the calculation without introducing too much memory overhead especially when combined with spatial domain decomposition. Numerical results show that these matrices can be assembled efficiently. PTT is implemented in Rattlesnake, the INL MOOSE-based multigroup radiation transport application. Both the angular and spatial convergence are investigated and the results indicate that low order angular and spatial resolution is typically sufficient for analysis. A complete work flow for PBR analysis with PTT including, pebble packing, meshing, cross section generation, solution of the transport equation, postprocessing, etc. is demonstrated with a simplified reactor with 41,048 pebbles.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wang, Yaqi, Ortensi, Javier, Schunert, Sebastian, Laboure, Vincent. 2018-04-01. A Pebble Tracking Transport Algorithm for Pebble Bed Reactor Analysis. https://www.osti.gov/biblio/1478317

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↗