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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multiple Coulomb scattering for muon energy reconstruction in SBND Monte Carlo simulations

The Short Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) located 110 meters downstream of the Booster Neutrino Beam target at Fermilab \cite{SBN}. The LArTPC is a detector technology with unprecedented position and calorimetric resolution for precise neutrino studies. The most dominant neutrino interaction topology in SBND is charged-current quasi-elastic muon neutrino-argon interactions, where the final state consists of a muon and a proton. Here, muons often travel long distances, sometimes exiting the detector before stopping. This loss of information creates a challenge for accurate energy estimation. There have been efforts in the MicroBooNE and ICARUS collaborations to improve energy estimation using a phenomenon known as Multiple Coulomb Scattering (MCS), which describes the jagged path taken by muons through a detector due to continuous Coulomb interactions with argon atoms. Building on these efforts, my project aims to improve an MCS-based approach to estimating the energies of exiting muons in SBND.

Puig, Xilonem Angeles [Mt. Holyoke Coll.; Fermilab↗
DOE OSTI2025

GPU-friendly surface model for Monte-Carlo detector simulations

The demands for Monte-Carlo simulation are drastically increasing with the Large Hadron Collider’s high-luminosity upgrade, and are expected to exceed the currently available compute resources. At the same time, modern high-performance computing has adopted powerful hardware accelerators, particularly GPUs. The AdePT and Celeritas projects aim to address the demanding computational needs by leveraging these heterogeneous computing architectures. While both have successfully ported realistic detector simulations to GPUs using the VecGeom library, the complexity of geometry modeling emerged as a bottleneck. Thread divergence and high register usage were degrading the GPU performance. Therefore, a new, GPU-friendly surface-based model has been introduced in the VecGeom library that decomposes the divergent code of the 3D primitive solids into simpler and more balanced surface algorithms. In this work, we present the latest developments, focusing on the additions required to efficiently model complex setups like the CMS Phase-2 geometry. This includes memory reduction techniques, and adding accelerating structures for faster traversal.

Diederichs, Severin [CERN]↗
DOE OSTI2021

Dispersion calibration for the National Ignition Facility electron–positron–proton spectrometers for intense laser matter interactions

Electron–positron pairs, produced in intense laser–solid interactions, are diagnosed using magnetic spectrometers with image plates, such as the National Ignition Facility Electron–Positron–Proton Spectrometers (EPPSs). Although modeling can help infer the quantitative value, the accuracy of the models needs to be verified to ensure measurement quality. The dispersion of low-energy electrons and positrons may be affected by fringe magnetic fields near the entrance of the EPPS. We have calibrated the EPPS with six electron beams from a Siemens Oncor linear accelerator (linac) ranging in energy from 2.7 MeV to 15.2 MeV as they enter the spectrometer. A Geant4 Tool for Particle Simulation Monte Carlo simulation was set up to match depth dose curves and lateral profiles measured in water at 100 cm source–surface distance. An accurate relationship was established between the bending magnet current setting and the energy of the electron beam at the exit window. The simulations and measurements were used to determine the energy distributions of the six electron beams at the EPPS slit. Furthermore, analysis of the scanned image plates together with the determined energy distribution arriving in the spectrometer provides improved dispersion curves for the EPPS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Simulations of ion heating due to ion-acoustic instabilities in presheaths

Particle-in-cell, direct simulation Monte Carlo simulations reveal that ion-acoustic instabilities excited in presheaths can cause significant ion heating. Ion-acoustic instabilities are excited by the ion flow toward a sheath when the neutral gas pressure is small enough and the electron temperature is large enough. A series of 1D simulations were conducted in which neutral plasma (electrons and ions) was uniformly sourced with an ion temperature of 0.026 eV and different electron temperatures (0.1 eV–50 eV). Ion heating was observed when the electron-to-ion temperature ratio exceeded the minimum value predicted by linear response theory to excite ion-acoustic instabilities at the sheath edge (T e /T i ≈ 28). When this threshold was exceeded, the temperature equilibration rate between ions and electrons rapidly increased near the sheath so that the local temperature ratio did not significantly exceed the threshold for instability. This resulted in significant ion heating near the sheath edge, which also extended back into the bulk plasma; presumably due to wave reflection from the sheath. Furthermore, this ion-acoustic wave heating mechanism was found to decrease for higher neutral pressures, where ion-neutral collisions damp the ion-acoustic waves and ion heating is instead dominated by inelastic collisions in the presheath.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Optimized collision-specific parameters for binary mixtures of nitrogen, oxygen, argon, and helium

Recently proposed collision-specific parameters for direct simulation Monte Carlo simulations are tested for binary mixtures of nitrogen, oxygen, and argon. Near ambient conditions, the traditional collision-averaged parameters are highly accurate, whereas the collision-specific parameters are not. The simulated transport using the collision-averaged parameters for mixtures with helium, however, is found to be inaccurate. Therefore, we propose a novel method to determine molecular parameters by combining the Chapman–Enskog theory with empirical mixing rules and experimental data. The optimized parameters are highly accurate for the binary mixtures of nitrogen, oxygen, and argon and greatly improve the simulated transport for the helium mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Efficient Subset Simulation using Hamiltonian Neural Network enhanced Markov Chain Monte Carlo Methods

The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Thermal and dynamic partition of dumbbell interstitials in complex concentrated alloys

Complex concentrated alloys (CCAs) are promising candidates for applications in extreme conditions, such as irradiation where interstitial mediated diffusion is important. In CCAs with N principal elements, $\frac{N(N+1)}{2}$ types of dumbbell interstitials exist. Currently, there is no way to predict the thermal partition (fractional concentration at equilibrium) and the dynamic partition (fractional time an interstitial spends during diffusion) of each type of dumbbell interstitial. To mitigate this issue, this work proposes a theoretical model for computing the equilibrium concentrations and thermal partition of dumbbell interstitials in CCAs and validates the model using grand canonical Monte Carlo simulations. Lattice kinetic Monte Carlo simulations show that the thermal partition is equivalent to the dynamic partition, and both are governed by composition and formation energies of dumbbells. As a result, the model proposed provides a foundation for understanding radiation enhanced diffusion and induced segregation in CCAs under irradiation.

36 MATERIALS SCIENCE↗