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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Accelerated characterization of graphite/epoxy composites

A method to predict the long term compliance of unidirectional off-axis laminates from short term laboratory tests is presented. The method uses an orthotropic transformation equation and the time-stress-temperature superposition principle. Short term tests are used to construct master curves for two off-axis unidirectional laminates with fiber angles of 10 and 90 degrees. Analytical predictions of long term compliance for 30 and 60 degrees laminates are made. Comparisons with experimental data are also given.

Griffith, W. I.↗
NASA NTRS1980

Accelerated characterization of graphite/epoxy composites

A method to predict the long-term compliance of unidirectional off-axis laminates from short-term laboratory tests is presented. The method uses an orthotropic transformation equation and the time-stress-temperature superposition principle. Short-term tests are used to construct master curves for two off-axis unidirectional laminates with fiber angles of 10 deg and 90 deg. In addition, analytical predictions of long-term compliance for 30 deg and 60 deg laminates are made. Comparisons with experimental data are also given.

Griffith, W. I.↗
NASA NTRS1976

The multigrid method: Fast relaxation

A multi-level grid method was studied as a possible means of accelerating convergence in relaxation calculations for transonic flows. The method employs a hierarchy of grids, ranging from very coarse (e.g. 4 x 2 mesh cells) to fine (e.g. 64 x 32); the coarser grids are used to diminish the magnitude of the smooth part of the residuals, hopefully with far less total work than would be required with optimal iterations on the finest grid. To date the method was applied quite successfully to the solution of the transonic small-disturbance equation for the velocity potential in conservation form. Nonlifting transonic flow past a parabolic arc airfoil is the example studied, with meshes of both constant and variable step size.

South, J. C., Jr.↗
NASA NTRS1976

Application of a multi-level grid method to transonic flow calculations

A multi-level grid method was studied as a possible means of accelerating convergence in relaxation calculations for transonic flows. The method employs a hierarchy of grids, ranging from very coarse to fine. The coarser grids are used to diminish the magnitude of the smooth part of the residuals. The method was applied to the solution of the transonic small disturbance equation for the velocity potential in conservation form. Nonlifting transonic flow past a parabolic arc airfoil is studied with meshes of both constant and variable step size.

South, J. C., Jr.↗
DOE OSTI2025

Numerical simulations of three-dimensional ion crystal dynamics in a Penning trap using the fast multipole method

We simulate the dynamics, including laser cooling, of three-dimensional (3-D) ion crystals confined in a Penning trap using a newly developed molecular dynamics-like code. The numerical integration of the ions’ equations of motion is accelerated using the fast multipole method to calculate the Coulomb interaction between ions, which allows us to efficiently study large ion crystals with thousands of ions. In particular, we show that the simulation time scales linearly with ion number, rather than with the square of the ion number. By treating the ions’ absorption of photons as a Poisson process, we simulate individual photon scattering events to study laser cooling of 3-D ellipsoidal ion crystals. Initial simulations suggest that these crystals can be efficiently cooled to ultracold temperatures, aided by the mixing of the easily cooled axial motional modes with the low frequency planar modes. In our simulations of a spherical crystal of 1000 ions, the planar kinetic energy is cooled to several millikelvin in a few milliseconds while the axial kinetic energy and total potential energy are cooled even further. This suggests that 3-D ion crystals could be well suited as platforms for future quantum science experiments.

Zaris, John (ORCID:0009000196476323)↗
NASA NTRS2000

Assessment of MCRM Boost Assist from Orbit for Deep Space Missions

Report provides results of analysis for the beamed energy driven MHD Chemical Rocket Motor (MCRM) for application to boost from orbit to escape for deep space and interplanetary missions. Parametric analyses were performed in the mission to determine operating regime for which the MCRM provides significant propulsion performance enhancement. Analysis of the MHD accelerator was performed numerical computational methods to determine design and operational features necessary to achieve Isp on the order of 2,000 to 3,000 seconds. Algorithms were developed to scale weights for the accelerator and power supply. Significant improvement in propulsion system performance can be achieved with the beamed energy driven MCRM. The limiting factor on achievable vehicle acceleration is the specific power of the rectenna.

Source record↗
DOE OSTI2022

DEVELOPMENT OF A QUANTUM ELECTRON BEAM DIAGNOSTIC APPARATUS

Spatial properties of electron beams are the essential parameters needed in particle physics and accelerator research. Beam characterization using optical detection methods through the interaction between photons and electrons such as Compton scattering, or electro-optic sampling of the electric field of electrons have been extensively explored and successfully implemented as non-invasive diagnostics at various accelerator facilities. However, such methods often suffer from inherently low sensitivity. Here we present the study of a new type of electron beam diagnostic device for direct optical imaging of electron beams at various energy levels. The concept is based on the extremely high sensitivity of atoms, prepared in a specific "dark superposition" quantum state, to the external electric or magnetic perturbations induced by the passing charged particle, which allows atoms to change polarization of the probe light or absorb probe light and to fluoresce, enabling direct 3D imaging of the charged particles with high resolution. We report our recent experiment results and the design effort on a compact apparatus intended to be tested with the relativistic electron beams at Jefferson Laboratory.

Zhang, S.↗
NASA NTRS1992

Analysis of crustal structure of Venus utilizing residual Line-of-Sight (LOS) gravity acceleration and surface topography data. A trial of global modeling of Venus gravity field using harmonic spline method

To construct Venus' gravity disturbance field (or gravity anomaly) with the spacecraft-observer line of site (LOS) acceleration perturbation data, both a global and a local approach can be used. The global approach, e.g., spherical harmonic coefficients, and the local approach, e.g., the integral operator method, based on geodetic techniques are generally not the same, so that they must be used separately for mapping long wavelength features and short wavelength features. Harmonic spline, as an interpolation and extrapolation technique, is intrinsically flexible to both global and local mapping of a potential field. Theoretically, it preserves the information of the potential field up to the bound by sampling theorem regardless of whether it is global or local mapping, and is never bothered with truncation errors. The improvement of harmonic spline methodology for global mapping is reported. New basis functions, a singular value decomposition (SVD) based modification to Parker & Shure's numerical procedure, and preliminary results are presented.

Fang, Ming↗
DOE OSTI2021

Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators

The growing numbers of application areas for artificial intelligence (AI) methods have led to an explosion of domain-specific accelerators that could support every new machine learning (ML) algorithm advancement, clearly highlighting the need for a capability to quickly and automatically transition from algorithm definition to hardware implementation and explore design space along a variety of SWaP (size, weight and Power). The software defined architectures (SODA) synthesizer implements a compiler-based modular infrastructure for the end-to-end generation of machine learning accelerators from high-level frameworks to hardware description language. At the same time, neuromorphic computing, by mimicking how the brain operates, promises to perform artificial intelligence tasks at efficiencies orders of magnitude higher than the current conventional tensor-processing based accelerators, as demonstrated by a variety of specialized designs leveraging Spiking Neural Networks (SNNs). Nevertheless, the mapping of an artificial neural network (ANN) to solutions supporting SNNs is still a non-trivial and very device-specific task, and completely lack the possibility to design hybrid systems that integrate conventional and spiking neural models. In this paper we discuss the support for such an integrated generation leveraging the SODA Synthesizer framework and its modular structure. In particular, we present a new MLIR dialect (part of the SODA frontend) that allows expressing spiking neural network features (e.g., available resources, spiking sequences, analog signal reading, etc.) and illustrate how it enables mapping to Spiking Neurons and deployment to the related specialized hardware (which, in the digital domain, could be generated through the other existing layers of the SODA Synthesizer). We then discuss the opportunities for even deeper integration afforded by the hardware compilation infrastructure, providing a path towards the generation of complex heterogeneous artificial intelligence systems.

Curzel, Serena↗
DOE OSTI2025

Laser-spectroscopy testbed for impurity monitoring in liquid metal-cooled fast reactors

A significant challenge in sodium-cooled fast reactors is controlling impurities, in particular oxygen impurities, within the sodium coolant, as they can accelerate corrosion and indicate leaks. Optical methods offer the potential to rapidly detect small concentrations of both gaseous and metal impurities that accelerate corrosion, plug coolant channels, and lead to increased activation of isotopes in the coolant. We present the design and performance of an apparatus designed to enable the application of multiple optical analytical techniques, such as laser-induced breakdown spectroscopy, to detect elemental impurities in the sodium melt with high sensitivity. Here, we experimentally demonstrate the detection of characteristic sodium and oxygen spectral lines in liquid sodium, which sets the stage for the optimization of its analytical sensitivity. A robust sensor of this type integrated with the sodium cooling loop has the potential to significantly improve the safety and operational efficiency of generation IV nuclear reactors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator

In this work, we present a general adaptive latent space tuning approach for improving the robustness of machine learning tools with respect to time variation and distribution shift. We demonstrate our approach by developing an encoder-decoder convolutional neural network-based virtual 6D phase space diagnostic of charged particle beams in the HiRES ultrafast electron diffraction (UED) compact particle accelerator with uncertainty quantification. Our method utilizes model-independent adaptive feedback to tune a low dimensional 2D latent space representation of ~1 million dimensional objects which are the 15 unique 2D projections (x, y),...,(z, p z ) of the 6D phase space (x, y, z, p x , p y , p z ) of the charged particle beams. We demonstrate our method with numerical studies of short electron bunches utilizing experimentally measured UED input beam distributions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration

Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data on their own devices. First-order methods, particularly those incorporating variance reduction techniques, are the most widely used FL algorithms due to their simple implementation and stable performance. However, these methods tend to be slow and require a large number of communication rounds to reach the global minimizer. We propose FedOSAA, a novel approach that preserves the simplicity of first-order methods while achieving the rapid convergence typically associated with second-order methods. Our approach applies one Anderson acceleration (AA) step following classical local updates based on first-order methods with variance reduction, such as FedSVRG and SCAFFOLD, during local training. This AA step is able to leverage curvature information from the history points and gives a new update that approximates the Newton-GMRES direction, thereby significantly improving the convergence. We establish a local linear convergence rate to the global minimizer of FedOSAA for smooth and strongly convex loss functions. Numerical comparisons show that FedOSAA substantially improves the communication and computation efficiency of the original first-order methods, achieving performance comparable to second-order methods like GIANT.

Feng, Xue [University of California, Davis]↗
DOE OSTI2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗
DOE OSTI2021

First 3D vector tracking of helium recoils for fast neutron measurements at Super$\mathrm{KEKB}$

Here we present results from the first deployment of novel, high definition, compact gas Time Projection Chambers (TPCs) with pixel chip readout as part of the BEAST II beam background measurement project at SuperKEKB. The TPCs provide detailed 3D imaging of ionization from neutron-induced nuclear recoils in a helium and carbon dioxide target gas mixture at standard temperature and pressure. We present the TPC performance and the neutron backgrounds observed during the initial stage of collider commissioning. We find excellent electron background rejection, leading to background-free nuclear recoil measurements above 50 keV ee , despite the extreme high-background environment. We measure an angular resolution better than 20° for recoil tracks longer than 1.7 mm, corresponding to an average ionization energy of approximately 100 keV ee . We also obtain the full 3D vector direction of helium recoils by utilizing charge profile measurements along the recoil axis, with a correct head/tail assignment efficiency of approximately 80%. With this performance, we present comparisons between measured and simulated event rates, recoil energy spectra, and directional distributions originating from beam–gas and Touschek beam losses at SuperKEKB. We utilize head/tail recognition to distinguish neutron components traveling with positive radial velocity in the Belle II coordinate system from those traveling in the opposite direction. Finally, we present a novel method of discriminating beam–gas interactions from Touschek beam losses that can eliminate the need for dedicated accelerator runs for background measurements. This method is still statistics-limited. However, future studies should be able to verify this method, which in turn could lead to neutron background analysis runs symbiotic with normal Belle II operation. The capabilities demonstrated here also suggest that high definition recoil imaging in gas TPCs is applicable to low energy, low-background experiments, such as directional dark matter searches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics and exposomics studies is to fully characterize the small molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversi-ty present within the metabolome and exposome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemis-try methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alter-native route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solu-tion. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectros-copy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics and exposomics studies. We expect this review will inspire researchers in the field of small molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Metabolism↗
DOE OSTI2020

Characterizing extreme laser intensities by ponderomotive acceleration of protons from rarified gas

Abstract A new method to diagnose extreme laser intensities through measurement of angular and spectral distributions of protons directly accelerated by the laser focused into a rarefied gas is proposed. We simulated a laser pulse focused by an off-axis parabolic mirror by Stratton–Chu integrals, that enables description of laser pulse with different spatial-temporal profiles focusing in a focal spot down to the diffraction limit, that makes our theoretical predictions be a basis for experimental realization. The relationship between characteristics of the proton distributions and parameters of the laser pulse have been analyzed. The analytical and numerical results obtained justify the new method of laser diagnostics. The proposed scheme should be valuable for the commissioning of new extreme intensity laser facilities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗