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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quasi-differential neutron induced neutron emissions from 235 U, and 239 Pu

Uncertainty in nuclear reaction cross sections, angular distributions, and other nuclear data directly impact how well simulations of nuclear systems represent physical observations. Here, to determine how well the nuclear data in ENDF/B-VIII.0, JEFF-3.3, and JENDL-4.0 evaluations describe the physical behaviour of 235 U, and 239 Pu when subjected to a neutron flux, the neutron emission spectrum was measured for carbon, 93.0% 235 U, and 93.9% 239 Pu samples, and compared against detailed MCNP6 simulations. The measurements were performed at the Los Alamos Neutron Science Center using a quasi-differential method previously developed at Rensselaer Polytechnic Institute. The measurement spanned 0.82–20 MeV and 30–150 degrees. The measurements show there are a significant number of discrepancies between library predictions of the neutron yield and physical observation. A few of the main discrepancies found are described in this paper. Based on these results a new evaluation utilizing these results for carbon, 235 U, and 239 Pu is recommended.

235U↗
DOE OSTI2022

Molecular simulation of the structural and thermodynamic properties of n-alkane/brine interfacial systems with nonionic surfactants

All-atom molecular dynamics (MD) simulations are used to study the structural and thermodynamic properties of water + n-heptane interfacial systems in the presence of salinity (NaCl, NaI, KCl, and KI) and two different nonionic surfactants. Excellent qualitative and quantitative agreement with interfacial tension experimental data is obtained, but this requires applying a charge scaling factor to the ionic species, suggesting the likely role of polarizability effects. Tensoactive behavior is observed for the NaI and KI systems, coinciding with layering of the iodide species near the interface. Further, there are significant interaction differences between the surfactants and the different ionic species, but the structural and thermodynamic behavior of the surfactant molecules is relatively unaffected by the specific salt species. Our analysis suggest that the surfactant-surfactant interactions play a critical role in determining the interfacial behavior, with a much smaller impact associated with the composition of the brine phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Compact representation and long-time extrapolation of real-time data for quantum systems using the ESPRIT algorithm

Representing real-time data as a sum of complex exponentials provides a compact form that enables both denoising and extrapolation. As a fully data-driven method, the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is agnostic to the underlying physical equations, making it broadly applicable to various observables and experimental or numerical setups. In this work, we consider applications of the ESPRIT algorithm primarily to extend real-time dynamical data from simulations of quantum systems. We evaluate ESPRIT's performance in the presence of noise and compare it to other extrapolation methods. We demonstrate its ability to extract information from short-time dynamics to reliably predict long-time behavior and determine the minimum time interval required for accurate results. We discuss how this insight can be leveraged in numerical methods that propagate quantum systems in time, and we show how ESPRIT can predict infinite-time values of dynamical observables, offering a purely data-driven approach to characterizing quantum phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Combining Physics and Machine Learning for the Next Generation of Molecular Simulation

Simulating molecules and atomic systems at quantum accuracy is a grand challenge for science in the 21 st century. Quantum-accurate simulations would enable the design of new medicines and the discovery of new materials. The defining problem in this challenge is that quantum calculations on large molecules, like proteins or DNA, are fundamentally impossible with current algorithms. In this work, we explore a range of different methods that aim to make large, quantum-accurate simulations possible. We show that using advanced classical models, we can accurately simulate ion channels, an important biomolecular system. We show how advanced classical models can be implemented in an exascale-ready software package. Lastly, we show how machine learning can learn the laws of quantum mechanics from data and enable quantum electronic structure calculations on thousands of atoms, a feat that is impossible for current algorithms. Altogether, this work shows that combining advances in physics models, computing, and machine learning, we are moving closer to the reality of accurately simulating our molecular world.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Extending SST vanadis to Add SIMT Functional Units

Sandia National Laboratories is currently investigating scalable architectural simulation capabilities, with a focus on simulating and evaluating highly scalable supercomputers for high-performance computing applications. This exploration is driven by the shift toward more specialized forms of compute and the need for a more diverse set of accurate models. This project will explore the use of General-Purpose Graphical Processing Units (GPGPUs) in high-performance computing using both physical systems and new simulator models – traditional GPUs as well as tightly-coupled SIMT accelerators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Towards quantum simulations in particle physics and beyond on noisy intermediate-scale quantum devices

We review two algorithmic advances that bring us closer to reliable quantum simulations of model systems in high-energy physics and beyond on noisy intermediate-scale quantum (NISQ) devices. The first method is the dimensional expressivity analysis of quantum circuits, which allows for constructing minimal but maximally expressive quantum circuits. The second method is an efficient mitigation of readout errors on quantum devices. Both methods can lead to significant improvements in quantum simulations, e.g. when variational quantum eigensolvers are used. This article is part of the theme issue ‘Quantum technologies in particle physics’.

Science & Technology - Other Topics↗
DOE OSTI2023

Using Kinetic Entropy to Understand Dissipation in Re-connection and Turbulence

This report summarizes results obtained at Space Science Institute (SSI) under a collaborative project funded by the NSF-DOE partnership in Basic Plasma Physics. The overall goal of the project was to perform a study of kinetic entropy as a diagnostic of dissipation in several archetypal models, such as magnetic reconnection and decaying plasma turbulence. The specific task of the SSI group was to perform collisionless and weakly collisional Particle-in-Cell (PIC) simulations modeling these systems. Overall, the project has demonstrated that, as expected, kinetic entropy carries an imprint of weakly collisional processes commonly thought to be associated with dissipation and thus provides an important diagnostic of dissipation. Specific entropy-related metrics have been proposed that could be used as quantitative measures of dissipation. These metrics have been implemented as diagnostics in PIC simulations and their utility, as well as relation to other, previously proposed, measures of dissipation have been demonstrated. Further, PIC simulations were used to investigate a general question of how energy is dissipated in magnetized weakly collisional turbulence in both non-relativistic and relativistic plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit Ansätze

Adaptive quantum variational algorithms are particularly promising for simulating strongly correlated systems on near-term quantum hardware, but they are not yet viable due, in large part, to the severe coherence time limitations on current devices. In this paper, we introduce an algorithm called TETRIS-ADAPT-VQE (tiling efficient trial circuits with rotations implemented simultaneously adaptive derivative-assembled problem-tailored variational quantum eigensolver), which iteratively builds up variational a few operators at a time in a way dictated by the problem being simulated. This algorithm is a modified version of the ADAPT-VQE algorithm, in which the one-operator-at-a-time rule is lifted to allow for the addition of multiple operators with disjoint supports in each iteration. TETRIS-ADAPT-VQE results in denser but significantly shallower circuits, without increasing the number of controlled- gates or variational parameters. Its advantage over the original algorithm in terms of circuit depths increases with the system size. Moreover, the expensive step of measuring the energy gradient with respect to each candidate unitary at each iteration is performed only a fraction of the time compared with ADAPT-VQE. These improvements bring us closer to the goal of demonstrating a practical quantum advantage on quantum hardware. Published by the American Physical Society 2024

Anastasiou, Panagiotis G. (ORCID:0000000256601791)↗
DOE OSTI2020

Multiphase outflows in post-starburst E+A galaxies - II. A direct connection between the neutral and ionized outflow phases

ABSTRACT Post-starburst E+A galaxies are systems that hosted a powerful starburst that was quenched abruptly. Simulations suggest that these systems provide the missing link between major merger ULIRGs and red and dead ellipticals, where AGN feedback is responsible for the expulsion or destruction of the molecular gas. However, many details remain unresolved and little is known about AGN-driven winds in this short-lived phase. We present spatially resolved IFU spectroscopy with MUSE/VLT of SDSS J124754.95-033738.6, a post-starburst E+A galaxy with a recent starburst that started 70 Myr ago and ended 30 Myr ago, with a peak SFR of $\sim 150\, \mathrm{M_{\odot }\,yr^{ -1}}$. We detect disturbed gas throughout the entire field of view, suggesting triggering by a minor merger. We detect fast-moving multiphased gas clouds, embedded in a double-cone face-on outflow, which are traced by ionized emission lines and neutral NaID emission and absorption lines. We find remarkable similarities between the kinematics, spatial extents, and line luminosities of the ionized and neutral gas phases, and propose a model in which they are part of the same outflowing clouds, which are exposed to both stellar and AGN radiation. Our photoionization model provides consistent ionized line ratios, NaID absorption optical depths and EWs, and dust reddening. Using the model, we estimate, for the first time, the neutral-to-ionized gas mass ratio (about 20), the sodium neutral fraction, and the size of the outflowing clouds. This is one of the best ever observed direct connections between the neutral and ionized outflow phases in AGN.

Baron, Dalya↗
DOE OSTI2025

nys_psy (NYgrid Model Translation to the Sienna framework) (SWR-25-63)

This repository contains the translation of the NYgrid model, developed by the Anderson Energy Lab at Cornell University, into the Sienna Framework. The baseline model is based on 2019 data. The 2040 version of the model features a unified, correlated dataset of various generation and load profiles spanning 22 years. The methodology used to generate these data is detailed in the following paper The scripts for data generation are available in the ny-clcpa2050 repository. Note: While this test system is designed to simulate the power flow of the New York State transmission system, it does not represent the actual transmission network.

Liu, Vivienne [National Renewable Energy Laborator↗
DOE OSTI2023

Ripple-Type Voltage Control for Extreme-Event Contingencies: Preprint

Frequent and intense extreme events make grid operation unprecedentedly challenging. Disruptive events could lead to dangerous voltage drops and even voltage collapse if corrective actions are not quickly taken. In this paper, we present a real-time algorithm for voltage control suitable for mitigating electric grid damage scenarios. In our strategy, when agents (generators, substations) experience a dangerous undervoltage, they first respond locally. When the local control resources are depleted, agents seek assistance from peer nodes over a communication network. The algorithm is simulated on a realistic test transmission system. Using fragility curve methodology, we simulate hurricane damages to the components of the synthetic 2000-bus grid representing the ERCOT system. Although being tested over a damaged grid after a hurricane event, our algorithm can be equally successfully applied to any other emergency low-voltage situation.

extreme weather events↗
DOE OSTI2026

Chasing Gamma-Ray Signals from Binary Neutron Star Coalescences with the Cherenkov Telescope Array: Prospects and Observing Strategies

The detection of gravitational waves (GWs) from a binary neutron star (BNS) merger by Advanced LIGO and Advanced Virgo (GW170817), together with its electromagnetic counterpart, the short gamma-ray burst GRB 170817A, heralded the birth of multimessenger astronomy. The detection of TeV emission from GRBs motivates follow-up observations with the Cherenkov Telescope Array Observatory (CTAO), which is ideal for detecting such signals due to its unprecedented sensitivity, rapid response, and wide-field survey capabilities. The aim of this work is to evaluate GeV–TeV GW follow-up strategies for CTAO using a multistep simulation pipeline and to estimate the expected rate of joint GW–GRB detections during observing run O5. Using a simulated sample of BNS systems with corresponding GW detections, gamma-ray emission is simulated through phenomenological prescriptions based on the observed population of short GRBs, including off-axis jet scenarios. CTAO observations are simulated to account for instrument response, sky tiling strategies, integration times, and varying observing conditions. Strategies with variable and constant integration times are investigated. We find that, via an optimized follow-up strategy, about 5% of simulated GW-associated short GRBs produce GeV–TeV radiation detectable by CTAO. Detectability is strongly influenced by the jet opening angle and viewing angle, suggesting that even rough estimates of the viewing angle in GW alerts could enhance targeting. This framework motivates future follow-ups of GW-detectable events, including neutron star–black hole mergers, and further supports the development of advanced strategies incorporating galaxy distributions and synergies with future detectors such as the Einstein Telescope.

Abe, S. [University of Tokyo] (ORCID:0000000172503↗
DOE OSTI2023

Plasma production and ion heating systems for the Material Plasma Exposure eXperiment

Plasma production and ion heating in the Material Plasma Exposure eXperiment (MPEX), whose design is nearing completion, is accomplished using continuous wave RF power with average power density up to 1.6 MW/m2 at the plasma interface. Plasma is produced using helicon waves coupled through a single helical antenna at 13.56 MHz with power supplied by three 100 kW fixed-frequency RF generators feeding a power combiner network followed by the matching network and launcher. The helicon source can utilize various gasses including hydrogen, deuterium, and helium, with a magnetic field strength in the source region up to 0.2 T, and maximum device |B| of 2.5 T. Power is coupled to ions via ion cyclotron heating at the fundamental resonance using a pair of phased helical antennas operating in the frequency range 4-9 MHz, that launch waves towards the resonance from the antenna region where ω > ωci. ICH power is supplied by a single 500 kW tunable RF transmitter through a 90° power splitter and matching/decoupling network. In the case of both the helicon and ICH systems the antennas are located external to the vacuum, with power transferred through novel water-cooled coaxial vacuum windows consisting of fused quartz outer cylinders and silicon nitride inner cylinders with forced convection water cooling between them. The antenna enclosures are pressurized with dry air to 3 bar absolute for voltage standoff.Several 3-D COMSOL models have been created to simulate the two systems. A model of the helicon region utilizing a cold plasma dielectric tensor with accurate magnetic field and realistic plasma density profiles has been used to calculate the plasma loading/complex antenna input impedance at the launcher feed for various ne and |B| values, for the purpose of estimating power handling. It also incorporates the geometry of all launcher structures relevant to this determination. A still more detailed model of the launcher together with a lossy dielectric plasma surrogate has been used to determine RF electric field values and power losses in the device components.Similar models have been produced to predict the performance and power handling of the ICH launcher. For this device the impedance matrix of the two-element antenna array is calculated using a warm plasma model, necessary to properly determine the wave propagation and absorption.

Goulding, Richard↗
DOE OSTI2022

Machine learning predictions for local electronic properties of disordered correlated electron systems

We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems, which means local electronic properties depend mainly on the immediate environment. A ML model is developed to encode this complex dependence of local quantities on the neighborhood. We demonstrate our approach using the square-lattice Anderson-Hubbard model, which is a paradigmatic system for studying the interplay between Mott transition and Anderson localization. We develop a lattice descriptor based on the group-theoretical method to represent the on-site random potentials within a finite region. The resultant feature variables are used as input to a multilayer fully connected neural network, which is trained from data sets of variational Monte Carlo (VMC) simulations on small systems. We show that the ML predictions agree reasonably well with the VMC data. Our work underscores the promising potential of ML methods for multiscale modeling of correlated electron systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems

Quantum computing (QC) and deep learning have shown promise of supporting transformative advances and have recently gained popularity in a wide range of areas. Here, this paper proposes a hybrid QC-based deep learning framework for fault diagnosis of electrical power systems that combine the feature extraction capabilities of conditional restricted Boltzmann machine with an efficient classification of deep networks. Computational challenges stemming from the complexities of such deep learning models are overcome by QC-based training methodologies that effectively leverage the complementary strengths of quantum assisted learning and classical training techniques. The proposed hybrid QC-based deep learning framework is tested on a simulated electrical power system with 30 buses and wide variations of substation and transmission line faults, to demonstrate the framework’s applicability, efficiency, and generalization capabilities. High computational efficiency is enjoyed by the proposed hybrid approach in terms of computational effort required and quality of diagnosis performance over classical training methods. In addition, superior and reliable fault diagnosis performance with faster response time is achieved over state-of-the-art pattern recognition methods based on artificial neural networks (ANN) and decision trees (DT).

42 ENGINEERING↗
DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Noninvasive interrogation of local flow phenomena in twisted tape swirled flow via positron emission particle tracking ($\mathrm{PEPT}$)

Twisted tape inserts are often used to enhance the performance of heat exchangers. Previous simulations suggest the existence of secondary vortices and modified axial velocity profiles in these swirled flows, with only limited experimental evidence for these predictions. Here in this work, positron emission particle tracking (PEPT) is used to interrogate turbulent twisted tape swirl flow in a pipe. PEPT is a noninvasive, radiotracer based flow measurement technique that does not require optical access, making it suitable for measurements in a number of engineering flow systems. The existence of the modified velocity profile and secondary flows is confirmed using PEPT, and flow is seen to still be developing 20 diameters downstream of the twisted channel entrance. Secondary vortices are further confirmed by the presence of increased vorticity in these regions, and turbulent fluctuations are seen to increase in magnitude near these flow structures. These results are in line with predictions and point to possible regions of reduced heat transfer and hot spots in twisted tape based heat exchangers. Suggestions are made for future experiments to investigate heated flows, utilizing the ability of PEPT to image flows in opaque systems for validating simulation of flows in engineering equipment, such as nuclear heat transport equipment.

42 ENGINEERING↗