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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Telerobotics test bed for space structure assembly

A cooperative research on super long distance space telerobotics is now in progress both in Japan and USA. In this program. several key features will be tested, which can be applicable to the control of space robots as well as to terrestrial robots. Local (control) and remote (work) sites will be shared between Electrotechnical Lab (ETL) of MITI in Japan and Jet Propulsion Lab (JPL) in USA. The details of a test bed for this international program are discussed in this report.

Kitami, M.↗
NASA NTRS2018

The Varying Core Magnetic Field from a Space Weather Perspective

This paper summarizes recent advances in our understanding of geomagnetism, and its relevance to terrestrial space weather. It also discusses specific core magnetic field features such as the dipole moment decay, the evolution of the South Atlantic anomaly, and the location of the magnetic poles that are of importance for the practice of space weather.

Core Field↗
NASA NTRS1981

A comparison of in-cloud HCl concentrations from the NASA/MSFC MDM to measurements for the space shuttle launch

The Multilevel Diffusion Model (MDM) Version 5 was modified to include features of more recent versions. The MDM was used to predict in-cloud HCl concentrations for the April 12 launch of the space Shuttle (STS-1). The maximum centerline predictions were compared with measurements of maximum gaseous HCl obtained from aircraft passes through two segments of the fragmented shuttle ground cloud. The model over-predicted the maximum values for gaseous HCl in the lower cloud segment and portrayed the same rate of decay with time as the observed values. However, the decay with time of HCl maximum predicted by the MDM was more rapid than the observed decay for the higher cloud segment, causing the model to under-predict concentrations which were measured late in the life of the cloud. The causes of the tendency for the MDM to be conservative in over-estimating the HCl concentrations in the one case while tending to under-predict concentrations in the other case are discussed.

Glasser, M. E.↗
NASA NTRS1998

SIM vs. SOS: A Space Interferometry Trade Study

This study was undertaken at the Jet Propulsion Laboratory to identify salient features of two competing instrument designs and to select the design that best meets the goals of the Space Interferometry Mission.

trade-study↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

A review of the Jovian magnetosphere based upon Pioneer 10 and 11

A review and analysis are presented of data derived from the Pioneer 10 and Pioneer 11 flybys of Jupiter on the Jovian plasma, magnetic field, and energetic particles in circumjovian space. The design of the space probes is described along with the principal experiments flown. Overall features of the Jovian magnetosphere are drawn and contrasted with the earth's magnetosphere. The trajectories of the two space probes are contrasted and their data on Jupiter's plasmasphere are correlated. Isointensity contours and count rates of energetic particles are plotted, flux tubes within the Jovian magnetosphere are mapped, the ring current (plasma torus) encircling the planet is described, and possible effects of solar wind and of the moons immersed in Jupiter's magnetosphere are considered.

Trainor, J. H.↗
NASA NTRS1989

Terrain mapping for a roving planetary explorer

A prototype legged vehicle is being developed for an exploratory mission on another planet, conceivably Mars, where it is to traverse uncharted areas and collect material samples. The rover can construct from range imagery a geometric terrain representation; i.e., elevation map that includes uncertainty, unknown areas, and local features. An algorithm for constructing an elevation map from a single range image is presented: by virtue of working in spherical-polar space, the algorithm is independent of the desired map resolution and the orientation of the sensor, unlike algorithms that work in Cartesian space. Also presented is a two-stage matching technique (feature matching followed by iconic matching) that identifies the transformation T corresponding to the vehicle displacement between two viewing positions. To support legged locomotion over rough terrain, methods for evaluating regions of the constructed elevation maps as footholds are developed.

Hebert, M.↗
DOE OSTI2020

Observation of Electrically Tunable van Hove Singularities in Twisted Bilayer Graphene from NanoARPES

The possibility of triggering correlated phenomena by placing a singularity of the density of states near the Fermi energy remains an intriguing avenue toward engineering the properties of quantum materials. Twisted bilayer graphene is a key material in this regard because the superlattice produced by the rotated graphene layers introduces a van Hove singularity and flat bands near the Fermi energy that cause the emergence of numerous correlated phases, including superconductivity. Direct demonstration of electrostatic control of the superlattice bands over a wide energy range has, so far, been critically missing. This work examines the effect of electrical doping on the electronic band structure of twisted bilayer graphene using a back-gated device architecture for angle-resolved photoemission measurements with a nano-focused light spot. A twist angle of 12.2° is selected such that the superlattice Brillouin zone is sufficiently large to enable identification of van Hove singularities and flat band segments in momentum space. Finally, the doping dependence of these features is extracted over an energy range of 0.4 eV, expanding the combinations of twist angle and doping where they can be placed at the Fermi energy and thereby induce new correlated electronic phases in twisted bilayer graphene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification

The previously established ExaCA software for performance portable alloy grain structure simulation has been updated to better represent the solidification behavior during complex alloy processing conditions, such as those encountered during metal additive manufacturing (AM), and for improved performance and scalability. Here, an extension to the time–temperature history input data format and the core ExaCA algorithm to include an arbitrary number of melting and solidification events yielded improved prediction of texture for various melt pool geometries, expanding the range of AM-relevant conditions that can be accurately simulated. Improved heat transport process simulation coupling, including the creation of large raster datasets from single track time–temperature history data and in-memory coupling with the new, performance portable finite difference code Finch, were also demonstrated in example studies on the effect of multilayer AM microstructure predictions on hatch spacing and cell size, respectively. Additional new features are detailed and demonstrated, including the ability to perform simulations using various interfacial response function forms, execute simulations on state-of-the-art hardware, improved usability through post-processing versatility, and improved strong and weak scaling performance. The performance, physics, and versatility improvements demonstrated here will further enable large-scale studies on AM process–microstructure relationships that were not previously possible. Furthermore, the usability improvements and ability to run coupled AM process–microstructure simulations using the Finch-ExaCA workflow will facilitate broader use of this open-source software by the computational materials community.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spin–Flop and Metamagnetic Transition in Monoclinic Eu 4 Bi 6 Se 13

This study explores an investigation of the crystallographic, electronic, and magnetic properties of the europium-based bismuth selenide compound Eu 4 Bi 6 Se 13 , with particular focus on its magnetic anisotropy. This compound adopts a monoclinic crystal structure classified under the P2 1 /m space group (#11). It exhibits distinctive structural features, including substantial Eu–Se coordination numbers (6 and 8), Bi–Se ladders, and linear chains of Eu atoms that propagate along the b-axis. Electronic resistivity assessments indicate that Eu 4 Bi 6 Se 13 exhibits metallic behavior. As the magnetic field is oriented along the b-axis, magnetic characterization reveals uniaxial magnetic anisotropy, with metamagnetic transitions appearing at approximately 12 kOe and a lower field. In the field below 10 kOe, the spin-flop transition is observed with possible domain-induced hysteresis. This behavior supports the identification of metamagnetic features in field-dependent measurements attributable to the europium spins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Controlling the Structure of MoS 2 Membranes via Covalent Functionalization with Molecular Spacers

Restacked two-dimensional (2D) materials represent a new class of membranes for water-ion separations. Understanding the interplay between the 2D membrane’s structure and the constituent material’s surface chemistry to its ion sieving properties is crucial for further membrane development. In this paper, we reveal, and tune via covalent functionalization, the structure of MoS 2 -based membranes. We find features on both the ~1 nm (interlayer spacing) and ~100 nm (mesoporous voids between layers) length scales that evolve with the hydration level. The functional groups act as permanent molecular spacers, preventing local impermeability caused by irreversible restacking and promoting the uniform rehydration of the membrane. Molecular dynamics simulations show that the choice of functional group tunes the structure of water within the MoS 2 channel and consequently determines the hydrated interlayer spacing. We demonstrate that MoS 2 membranes functionalized with acetic acid have consistently ~92% rejection of Na 2 SO 4 with a flux of ~1.5 lm -2 hr -1 bar -1 .

2D channel↗
DOE OSTI2023

Characterization of fast magnetosonic waves driven by compact toroid plasma injection along a magnetic field

Magnetosonic waves are low-frequency, linearly polarized magnetohydrodynamic (MHD) waves commonly found in space, responsible for many well-known features, such as heating of the solar corona. In this work, we report observations of interesting wave signatures driven by injecting compact toroid (CT) plasmas into a static Helmholtz magnetic field at the Big Red Ball Facility at Wisconsin Plasma Physics Laboratory. By comparing the experimental results with the MHD theory, we identify that these waves are the fast magnetosonic modes propagating perpendicular to the background magnetic field. Additionally, we further investigate how the background field, preapplied poloidal magnetic flux in the CT injector, and the coarse grid placed in the chamber affect the characteristics of the waves. Since this experiment is part of an ongoing effort of creating a target plasma with tangled magnetic fields as a novel fusion fuel for magneto-inertial fusion (MIF), our current results could shed light on future possible paths of forming such a target for MIF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A next-generation liquid xenon observatory for dark matter and neutrino physics

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Plasma-arc lamp high heat flux cycling exposure of neutron irradiated tungsten materials

Thick plate, unalloyed W was neutron irradiated in the High Flux Isotope Reactor (HFIR) at 550 °C to a fast fluence of 1.24 × 10 25 n m -2 E > 0.1 MeV (~0.24 dpa). Unirradiated and irradiated specimens of the material were high heat flux (HHF) tested in the Plasma Arc Lamp (PAL) facility. The PAL uses a high-power photon source to provide a broad and even heat distribution on the sample surface. To simulate on/off cycling of normal operating plasma, the samples were exposed to approximately 800 cycles at 4.73 MW m -2 absorbed heat flux (incident heat fluxes of 10.95 MW m -2 ). Additionally, after PAL exposure, slight changes were observed on the surfaces of the samples with SEM. The samples showed some annealing in the near surface polished region, but they were all below the damage threshold for cracking or other destructive features. The PAL has a large parameter space for future testing. The use of the HFIR and PAL to sequentially expose neutron irradiated samples to HHF will be a powerful tool for understanding materials behavior in a fusion-like environment.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multi-machine validation of plasma initiation modelling and prospects for future devices: Predicting plasma initiation using only hardware design and control room input data

This paper reports on the generic prediction capability of full electromagnetic plasma initiation modelling with DYON, which was carried out for the first time in fusion research by the joint modelling of the International Tokamak Physics Activity—Integrating Operation Scenario group. The following devices were included in the experiment database: VEST (spherical torus, copper coils, Stainless steel wall, R/a = 0.3 m/0.2 m, V v = 3.7 m 3 ), MAST-U (spherical torus, copper coils, C wall, R/a = 0.7 m/0.5 m, V v = 55 m 3 ), EAST (conventional tokamak, superconducting coils, metallic wall, R/a = 1.85 m/0.5 m, V v = 38 m 3 ), DIII-D (conventional tokamak, copper coils, C wall, R/a = 1.67 m/0.65 m, V v = 35 m 3 ), and KSTAR (conventional tokamak, superconducting coils, C wall, R/a = 1.8 m/0.5 m, V v = 55 m 3 ). Despite the different hardware features of the devices, the required operating spaces of the loop voltage induction and prefill gas pressure for inductive plasma initiation in each device were successfully reproduced by the predictive simulations with DYON using only the individual hardware design and the control room input data for each discharge. This successful validation across multiple machines demonstrates that the full electromagnetic DYON modelling can capture the essential physics of inductive plasma initiation. The simulation settings commonly employed for all modelling and the modifications necessary to account for the discrepancies between individual devices are reported. Predictions for ITER based on the multi-machine validation indicate that a wide range of prefill gas pressures exists for the Townsend breakdown and the plasma burn-through (0.01–1.5 mPa).

DYON↗
DOE OSTI2023

A detailed study of interpretability of deep neural network based top taggers

Abstract Recent developments in the methods of explainable artificial intelligence (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input–output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton–proton collisions at the Large Hadron Collider. We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how correlations among features impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as neural activation pattern diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. These studies not only facilitate a methodological approach to interpreting models but also unveil new insights about what these models learn. Incorporating these observations into augmented model design, we propose the particle flow interaction network model and demonstrate how interpretability-inspired model augmentation can improve top tagging performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Scrambling and quantum chaos indicators from long-time properties of operator distributions

Scrambling is a key concept in the analysis of nonequilibrium properties of quantum many-body systems. Most studies focus on its characterization via out-of-time-ordered correlation (OTOC) functions, particularly through the early-time decay of the OTOC. However, scrambling is a complex process which involves operator spreading and operator entanglement, and a full characterization requires one to access more refined information on the operator dynamics at several timescales. In this work we analyze operator scrambling by expanding the target operator in a complete basis and studying the structure of the expansion coefficients treated as a coarse-grained probability distribution in the space of operators. Here we study different features of this distribution, such as its mean, variance, and participation ratio, for the Ising model with longitudinal and transverse fields, kicked collective spin models, and random circuit models. We show that the long-time properties of the operator distribution display common features across these cases and discuss how these properties can be used as a proxy for the onset of quantum chaos. Finally, we discuss the connection with OTOCs and analyze the cost of probing the operator distribution experimentally using these correlation functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗