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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Prong Segmentation using Point Set Transformers in Multiple View Neutrino Detectors

NOvA is a long-baseline neutrino experiment studying neutrino oscillations by detecting neutrinos from the NuMI beam at Fermilab. Its physics analysis relies on accurate prong segmentation, which involves matching each hit to its source particle and identifying the particle type. This task has commonly been addressed using a combination of traditional clustering algorithms and convolutional neural networks (CNNs). However, NOvA’s detector design presents data as two sparse and decoupled 2D images (XZ and YZ views) rather than a native 3D representation, posing a significant challenge for traditional CNN-based models. In this talk, we propose a novel neural network based on the Point Set Transformer. By treating detector hits as sparse point clouds and implementing a cross-view attention mechanism, our model enables efficient information mixing between both views. Evaluated on NOvA simulated data, our model achieves superior accuracy while requiring significantly fewer computational resources compared to other models. Furthermore, the model demonstrates great performance when applied to Liquid Argon Time Projection Chamber (LArTPC) data, which shows its potential as a universal prong segmentation algorithm for multiple view neutrino detectors.

Liu, Jiaxi [UC, Irvine]↗
DOE OSTI2014

Structure transitions induced by the Hall term in homogeneous and isotropic magnetohydrodynamic turbulence

Hall effects on local structures in homogeneous, isotropic, and incompressible magnetohydrodynamic turbulence are studied numerically. The transition of vortices from sheet-like to tubular structures induced by the Hall term is found, while the kinetic energy spectrum does not distinguish the two types of structures. It is shown by the use of the sharp low-pass filter that the transition occurs not only in the scales smaller than the ion skin depth but also in a larger scale. The transition is related with the forward energy transfer in the spectral space. Analyses by the use of the sharp low-pass filter show that the nonlinear energy transfer associated with the Hall term is dominated by the forward transfer and relatively local in the wave number space. A projection of the simulation data to a Smagorinsky-type sub-grid-scale model shows that the high wave number component of the Hall term may possibly be replaced by the model effectively.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Development of Efficient Process for Manufacturing of Thermoplastic Composites with Tailored Properties (CRADA 511)

This is a collaborative effort between Battelle Memorial Institute as manager and operator of Pacific Northwest National Laboratory (PNNL) and ESI North America Inc. (“ESI” or “Participant”) to apply computation and data analytics to the challenge of light weighting with a focus on the battery enclosures of electric vehicles (EVs). EVs use heavy batteries to increase range and power. A complex-shaped battery enclosure is required to meet a host of challenging performance requirements. The ability to virtually develop composite parts such as battery enclosure with tailored properties to meet required performance will be highly valuable to the automotive industry. However, efficient simulation of composite-manufacturing processes remains a challenging issue since simulation involves multiscale models in space and time, highly non-linear and anisotropic behavior, strongly coupled multi-physics, and complex geometries. This work will advance the state of the art by reducing the computational burden of composite optimization by using simulation data from a limited number of configurations off-line and then developing a reduced order model (ROM) using data analytics and machine learning (ML). Develop a data driven approach to link features of the material and manufacturing processes to the mechanical properties of thermoplastic composite parts.

42 ENGINEERING↗
DOE OSTI2018

Assessing the Implementation of an SDM at Sandia and LLNL

This report assesses the proposed implementation of a Scientific Data Management system that could be co-developed by LLNL and SNL and installed at each lab. In the current view, the systems would not be connected to each other. This report examines the different collaboration possibilities and status of planning and implementation of the teams at LLNL and SNL. The report uses identified requirements from previous work that has been done and documented in [1]. In that work, a set of simulation data management requirements was developed during a series of meetings, starting from requirements developed by Sandia Laboratory. These requirements are expected to evolve, and it is recommended that these requirements be revisited and updated for the following reasons: 1- Advancement in the computing hardware and software which leads to more processing power and better data movement speeds. 2- Advancement in computer algorithms which creates opportunities for better data management and analysis. 3- The need to expand and add to the capabilities of the SDM tools due to advancement in the applications software and due to new requirements by the scientific community. This report builds upon the experience from the work that has been done by some of the team members and the current SDM development efforts at LLNL and Sandia.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. A series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.

Kemp, Samuel↗
DOE OSTI2016

Capture zone area distributions for nucleation and growth of islands during submonolayer deposition

A fundamental evolution equation is developed to describe the distribution of areas of capture zones (CZs) associated with islands formed by homogeneous nucleation and growth during submonolayer deposition on perfect flat surfaces. This equation involves various quantities which characterize subtle spatial aspects of the nucleation process. These quantities in turn depend on the complex stochastic geometry of the CZ tessellation of the surface, and their detailed form determines the CZ area distribution (CZD) including its asymptotic features. For small CZ areas, behavior of the CZD reflects the critical island size, i. For large CZ areas, it may reflect the probability for nucleation near such large CZs. Predictions are compared with kinetic Monte Carlo simulation data for models with two-dimensional compact islands with i = 1 (irreversible island formation by diffusing adatom pairs) and i = 0 (adatoms spontaneously convert to stable nuclei, e.g., by exchange with the substrate).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

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

Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. Furthermore, a series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

TH-CD-201-07: Experimentally Investigating Proton Energy Deposition On the Microscopic Scale Using Fluorescence Nuclear Track Detectors

Purpose: In order to further understand the interplay between proton physics and radiobiology it is necessary to consider proton energy deposition on the microscopic scale. In this work we used Fluorescent Nuclear Track Detectors (FNTDs) to experimentally investigate proton energy deposition, track-by-track. Methods: We irradiated 8×4×0.5mm{sup 3} FNTD chips (Landauer Inc) at seven water depths along a pristine proton Bragg peak with range=12cm. After irradiation, the FNTDs were scanned using a confocal microscope (FV1200, Olympus) with a high-power red laser and an oil-immersion objective lens (UPLSAPO60XO, NA=1.35). 10 slice image stacks were acquired with a slice-thickness of 2µm at multiple positions across each FNTD. Image-based analyses of track radius and track “mass” (integrated signal intensity) were performed using trackpy. For comparison, Monte Carlo simulated data were obtained using TOPAS and TOPAS-nBio. Results: Excellent correlation was observed between median track mass and TOPAS dose-averaged linear energy transfer. The resolution of the imaging system was determined insufficient to detect a relationship between track radius and exposure depth. Histograms of track mass (i) displayed strong repeatability across positions within an FNTD and (ii) varied in peak position and shape as a function of depth. TOPAS-nBio simulations implemented on the nanometer scale using physics lists from GEANT4-DNA yielded energy deposition distributions for individual protons and electrons scored within a virtual FNTD. Good agreement was found between these simulated datasets and the FNTD track mass distributions. Conclusion: Robust experimental measurements of the integral energy deposited by individual proton tracks can be performed using FNTDs. Monte Carlo simulations offer an exceedingly powerful approach to the quantification of proton energy deposition on the microscopic scale, but whilst they have been well validated at the macroscopic level, their microscopic validation is far from complete. Our results demonstrate that FNTD-based study can play an important role in addressing this deficit. Tracy Underwood gratefully acknowledges the support of the European Commission under an FP7 Marie Curie International Outgoing Fellowship for Career Development (#630064).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo

Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process uncertainty; which most functional alignment methods cannot handle. A pair of functions can also have multiple optimal alignment solutions, which is not addressed in current literature. In this paper, a flexible Bayesian approach to functional alignment is presented, which appropriately accounts for noise in the data without any pre-smoothing required. Additionally, by running parallel MCMC chains, the method can account for multiple optimal alignments via the multi-modal posterior distribution of the warping functions. To most efficiently sample the warping functions, the approach relies on a modification of the standard Hamiltonian Monte Carlo to be well-defined on the infinite-dimensional Hilbert space. In this work, this flexible Bayesian alignment method is applied to both simulated data and real data sets to show its efficiency in handling noisy functions and successfully accounting for multiple optimal alignments in the posterior; characterizing the uncertainty surrounding the warping functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common “black box” type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices. For example, a pair of phase space points that are in close proximity to each other but on opposite sides of a separatrix may be situated on opposite sides of a transition state, while phase space points occurring early in a simulation that are distant from a separatrix are likely to belong to trajectories that are highly biased toward the product state associated with the basin of attraction to which they belong. In addition to inferring mechanistic details about multiple-pathway chemical reactions, our method can also be used to increase reactive trajectory sampling efficiency in molecular simulations via rejection sampling, with trajectories leading to undesired product states being identified and terminated early in the simulation rather than being carried to completion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Microscopic analysis of saturable absorbers: Semiconductor saturable absorber mirrors versus graphene

Fully microscopic many-body calculations are used to study the influence of strong sub-picosecond pulses on the carrier distributions and corresponding optical response in saturable absorbers used for mode-locking—semiconductor (quantum well) saturable absorber mirrors (SESAMs) and single layer graphene based saturable absorber mirrors (GSAMs). Unlike in GSAMs, the saturation fluence and recovery time in SESAMs show a strong spectral dependence. While the saturation fluence in the SESAM is minimal at the excitonic bandgap, the optimal recovery time and least pulse distortion due to group delay dispersion are found for excitation higher in the first subband. For excitation near the SESAM bandgap, the saturation fluence is about one tenth of that in the GSAM. At energies above the bandgap, the fluences in both systems become similar. A strong dependence of the saturation fluence on the pulse width in both systems is caused by carrier relaxation during the pulse. The recovery time in graphene is found to be about two to four times faster than that in the SESAMs. The occurrence of negative differential transmission in graphene is shown to be caused by dopant related carriers. In SESAMs, a negative differential transmission is found when exciting below the excitonic resonance where excitation induced dephasing leads to an enhancement of the absorption. Comparisons of the simulation data to the experiment show a very good quantitative agreement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Benchmarking magnetised three-wave coupling for laser backscattering: analytic solutions and kinetic simulations

Understanding magnetised laser–plasma interactions is important for controlling magneto-inertial fusion experiments and developing magnetically assisted radiation and particle sources. For nanosecond pulses at non-relativistic intensities, interactions are dominated by coherent three-wave interactions, whose nonlinear coupling coefficients became known only recently when waves propagate at oblique angles with the magnetic field. In this paper, backscattering coupling coefficients predicted by warm-fluid theory are benchmarked using particle-in-cell simulations in one spatial dimension, and excellent agreements are found for a wide range of plasma temperatures, magnetic field strengths and laser propagation angles, when the interactions are mediated by electron-dominant hybrid waves. Systematic comparisons between theory and simulations are made possible by a rigorous protocol. On the theory side, the initial boundary value problem of linearised three-wave equations is solved, and the transient-time solutions allow the effects of growth and damping to be distinguished. On the simulation side, parameters are carefully chosen and calibration runs are performed to ensure that comparisons are well controlled. Fitting simulation data to analytical solutions yields numerical growth rates that match theory predictions within error bars. Although warm-fluid theory is found to be valid for a wide parameter range, genuine kinetic effects have also been observed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

3rd Annual Earth System Grid Federation and 3rd Annual Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Face-to-Face Meeting Report December 2013

The climate and weather data science community gathered December 3–5, 2013, at Lawrence Livermore National Laboratory, in Livermore, California, for the third annual Earth System Grid Federation (ESGF) and Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Meeting, which was hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UV-CDAT are global collaborations designed to develop a new generation of open-source software infrastructure that provides distributed access and analysis to observed and simulated data from the climate and weather communities. The tools and infrastructure developed under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change, while the F2F meetings help to build a stronger climate and weather data science community and stronger federated software infrastructure. The 2013 F2F meeting determined requirements for existing and impending national and international community projects; enhancements needed for data distribution, analysis, and visualization infrastructure; and standards and resources needed for better collaborations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulation-trained machine learning models for Lorentz transmission electron microscopy

Understanding the collective behavior of complex spin textures, such as lattices of magnetic skyrmions, is of fundamental importance for exploring and controlling the emergent ordering of these spin textures and inducing phase transitions. It is also critical to understand the skyrmion–skyrmion interactions for applications such as magnetic skyrmion-enabled reservoir or neuromorphic computing. Magnetic skyrmion lattices can be studied using in situ Lorentz transmission electron microscopy (LTEM), but quantitative and statistically robust analysis of the skyrmion lattices from LTEM images can be difficult. In this work, we show that a convolutional neural network, trained on simulated data, can be applied to perform segmentation of spin textures and to extract quantitative data, such as spin texture size and location, from experimental LTEM images, which cannot be obtained manually. This includes quantitative information about skyrmion size, position, and shape, which can, in turn, be used to calculate skyrmion–skyrmion interactions and lattice ordering. We apply this approach to segmenting images of Néel skyrmion lattices so that we can accurately identify skyrmion size and deformation in both dense and sparse lattices. The model is trained using a large set of micromagnetic simulations as well as simulated LTEM images. This entirely open-source training pipeline can be applied to a wide variety of magnetic features and materials, enabling large-scale statistical studies of spin textures using LTEM.

McCray, Arthur R. C. (ORCID:0000000160774698)↗
DOE OSTI2017

Time delay analysis of the lensed quasar SDSS J1001+5027

We modify the algorithm we proposed in Aghamousa and Shafieloo for the time delay estimation of strongly lensed systems incorporating the weighted cross-correlation and weighted summation of correlation coefficients. We show the high performance of this algorithm by applying it to Time Delay Challenge (TDC1) simulated data. We apply then our proposed method to the light curves of the lensed quasar SDSS J1001+5027 since this system has been well studied by other groups, to compare our results with their findings. In this work we propose a new estimator, the “mirror” estimator, along with a list of criteria for reliability testing of the estimation. Our mirror estimator results are −117.1{sub −3.7}{sup +7.1} and −117.1{sub −8.8}{sup +7.2} using simple Monte Carlo simulations and simulated light curves provided by Rathna Kumar et al., respectively. Although the TDC1 simulations do not reflect the properties of the SDSS J1001+5027 light curves, using these simulations results in a smaller uncertainty, which shows that the higher quality observations can lead to a substantially more precise time delay estimation. Our time delay estimation is in agreement with the findings of the other groups for this strongly lensed system, and the difference in the size of the error bars reflects the importance of appropriate light curve simulations.

79 ASTRONOMY AND ASTROPHYSICS↗