arXiv subjects

Reshma Ughade

Publications and source records attributed to Reshma Ughade.

3 recordsLinked to original sources

arXiv2026

Low Contrast Absorber Localization in Heat Pipe Microreactors Using Physics Informed Muon Tomography for Reactivity Monitoring

Microreactors require nonintrusive monitoring methods that can provide information about internal control states without opening or modifying the sealed system. In heat pipe microreactors, rotating control drums regulate reactivity by changing the angular orientation of neutron absorber coatings around the reflector region. This study applies the previously developed $μ$TRec muon scattering tomography framework to absorber coating localization in the heat pipe microreactor geometry. Momentum informed $M$-value imaging is used with the spectrometer information to evaluate whether the angular orientation of absorber coatings on twelve control drums can be inferred from reconstructed muon scattering images. Two absorber materials are considered. Hafnium is used as a high contrast reference case, while boron carbide, $B_4C$, is used as the primary low contrast case because its density is close to that of the surrounding beryllium reflector. Both laser generated and cosmic ray muon sources are evaluated for absorber-in and absorber-out drum configurations. The reconstructed transverse images and one dimensional drum profiles show clear hafnium localization with both source types. For $B_4C$, absorber localization is achieved with the controlled laser generated source when the number of events is increased, while the cosmic ray case remains limited by weak absorber contrast and artifacts near high scattering regions. The study defines the imaging conditions under which $μ$TRec based $M$-value tomography can support control drum absorber localization for passive microreactor monitoring. These findings also support safeguards by design by informing the selection of absorber and reflector materials, dimensions, and coating thickness that improve nonintrusive detectability.

physics.app-ph↗

arXiv2026

Non-intrusive Monitoring of Sealed Microreactor Cores Using Physics-Informed Muon Scattering Tomography With Momentum Measurements

Next-generation microreactors enable remote deployment and semi-autonomous operation, but compact, sealed, heterogeneous cores limit conventional safeguard approaches that rely on access and bulk accountancy. Limited inspection access and complex internal geometry reduce sensitivity to localized anomalies such as missing fuel. Here we demonstrate missing-fuel detection in microreactor scale geometries using muon scattering tomography under realistic cosmic-ray conditions. We introduce $μ$TRec, a physics-informed framework that reconstructs event-level curved muon trajectories by combining a Gaussian multiple Coulomb scattering model with Bayesian updating, then maps scattering density through voxel wise M-values for core integrity verification. We evaluate a representative hexagonal core containing 61 fuel flakes with embedded control drums and shutdown rods, using both idealized 5 GeV muons and zenith-angle-dependent 0-60 GeV cosmic-ray spectra. A single missing fuel flake is detected with $3\times 10^{6}$ muons at 50 mm voxel resolution. Incorporating per-muon momentum further increases detectability by up to 149.85% for laser-driven sources and 105.11% for cosmic-ray sources relative to momentum-agnostic reconstruction. The approach remains robust under practical detector limits, with only an 8.88% reduction in detectability for 10 mm spatial resolution and 10% energy resolution. Compared with PoCA, $μ$TRec delivers 326.13% to 392.14% higher detectability at equal muon counts, enabling faster defect identification.

physics.app-ph↗

arXiv2025

$μ$TRec: A Muon Trajectory Reconstruction Algorithm for Enhanced Scattering Tomography

Cosmic ray muons enable non-invasive imaging of dense structures through multipleCoulomb scattering (MCS), with scattering angles dependent on atomic number (Z). Traditional algorithms like Point of Closest Approach (PoCA) assume single scattering, limiting accuracy. This work presents the $μ$TRec algorithm, which models muon paths using a Bayesian approach with Gaussian approximations, accounting for MCS and energy loss. $μ$TRec is applied to simulate muon imaging of dry storage casks (DSCs) used for spent nuclear fuel, with four loading configurations: fully loaded, one row missing, one assembly missing, and half assembly missing. The results demonstrate improved accuracy and resolution in identifying missing assemblies compared to conventional methods. It is observed that the $μ$TRec algorithm exhibits markedly superior performance over the classical PoCA method achieving respective improvements of 122% in signal-to-noise ratio (SNR), 35% in contrast-to-noise ratio (CNR), and 201% in detection power (DP) for the case of one missing fuel assembly with a muon flux of $10^6$ and a voxel size of 5 cm. Furthermore, μTRec supports high-resolution reconstruction with voxel sizes as small as 1 cm which enables the accurate localization of structural components such as the 2.5 cm thick steel canister. Notably, $μ$TRec is also capable of reliably detecting a single missing fuel assembly at a muon flux as low as $10^5$, a task that remains infeasible using PoCA under the same conditions.

physics.app-ph↗