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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

An interpretation of a mysterious 3.0- to 4.6-kHz emission band observed on Voyager 2 near Neptune

A whistler mode interpretation is provided for the narrowband signal (f approx. 3 - 4.6 kHz, Delta f approx. 200 - 800 Hz) detected by the plasma wave instrument on Voyager 2 during its encounter with Neptune. Our analysis indicates that this signal may have been generated in a limited spatial region and that it propagated to other regions of the Neptunian magnetosphere in the nonducted whistler mode with wave normal vectors lying close to the whistler mode resonance cone. The observed frequency variation of the emission along the Voyager 2 trajectory is consistent with this interpretation. The source location is estimated to be near the magnetic equator at L approx. 4 and dipole longitude of 111 deg W (260 deg W longitude in Neptune coordinate system). The source frequency and bandwidth are estimated to be 3.6 kHz and 300 Hz, respectively. The waves most likely would have been generated by energetic electrons with 2- to 20-keV parallel energy via a gyroresonance mechanism. Our interpretation of the narrowband emissions places the following limits on the Neptunian thermal plasma density and temperature: (1) N(sub e, min) greater than 0.16 el/cu cm for 1.2 R(sub N) less than R less than 5 R(sub N), (2) N(sub e, max) = 597.5/cu cm at R - 1.3 R(sub N), (3) T(sub e, max) less than 500-1000 K at R approx. 5 R(sub N). It is also possible that the weak UV aurora observed near Neptune could have been caused by the precipitation of energetic particles by the narrowband emission as a result of wave particle interactions.

Sonwalkar, Vikas S.↗
NASA NTRS1997

The Systematic Interpretation of Cosmic Ray Data (The Transport Project)

The Transport project's primary goals were to: (1) Provide measurements of critical fragmentation cross sections; (2) Study the cross section systematics; (3) Improve the galactic cosmic ray propagation methodology; and (4) Use the new cross section measurements to improve the interpretation of cosmic ray data. To accomplish these goals a collaboration was formed consisting of researchers in the US at Louisiana State University (LSU), Lawrence Berkeley Laboratory (LBL), Goddard Space Flight Center (GSFC), the University of Minnesota (UM), New Mexico State University (NMSU), in France at the Centre d'Etudes de Saclay and in Italy at the Universita di Catania. The US institutions, lead by LSU, were responsible for measuring new cross sections using the LBL HISS facility, analysis of these measurements and their application to interpreting cosmic ray data. France developed a liquid hydrogen target that was used in the HISS experiment and participated in the data interpretation. Italy developed a Multifunctional Neutron Spectrometer (MUFFINS) for the HISS runs to measure the energy spectra, angular distributions and multiplicities of neutrons emitted during the high energy interactions. The Transport Project was originally proposed to NASA during Summer, 1988 and funding began January, 1989. Transport was renewed twice (1991, 1994) and finally concluded at LSU on September, 30, 1997. During the more than 8 years of effort we had two major experiment runs at LBL, obtained data on the interaction of twenty different beams with a liquid hydrogen target, completed the analysis of fifteen of these datasets obtaining 590 new cross section measurements, published nine journal articles as well as eighteen conference proceedings papers, and presented more than thirty conference talks.

Guzik, T. Gregory↗
NASA NTRS1999

Interpretation of Lidar and Satellite Data Sets Using a Global Photochemical Model

A primary goal of the NASA Tropospheric Chemistry Program (TCP) is to "contribute substantially to scientific understanding of human impacts on the global troposphere". In order to analyze global or regional trends and factors of the troposphere chemistry, for example, its oxidation capacity or composition, a continuous global/regional data coverage as well as model simulations are needed. The Global Tropospheric Experiment (GTE), a major component of the TCP, provides data vital to these questions via aircraft measurement of key trace chemical species in various remote regions of the world. Another component in NASA's effort are satellite projects for exploration of tropospheric chemistry and dynamics. A unique data product is the Tropospheric Ozone Residual (TOR) utilizing global tropospheric ozone data. Another key research tool are simulation studies of atmospheric chemistry and dynamics for the theoretical understanding of the atmosphere, the extrapolation of observed trends, and for sensitivity studies assessing a changing anthropogenic impact to air chemistry and climate. In the context with model simulations, field data derived from satellites or (airborne) field missions are needed for two purposes: 1. To initialize and validate model simulations, and 2., to interpret field data by comparison to model simulation results in order to analyze global or regional trends and deviations from standard tropospheric chemistry and transport conditions as defined by the simulations. Currently, there is neither a sufficient global data coverage available nor are existing well established global circulation models. The NASA LARC CTM model is currently not yet in a state to accomplish a sufficient tropospheric chemistry simulation, so that the current research under this cooperative agreement focuses on utilizing field data products for direct interpretation. They will be also available for model testing and a later interpretation with a finally utilized model.

Zenker, Thomas↗
NASA NTRS2000

An Interpretation of Banded Magnetospheric Radio Emissions

Recently-published Active Magnetospheric Particle Tracer Explorer/Isothermal Remanent Magnetization (AMPTE/IRM) banded magnetospheric emissions, commonly referred to as '(n + 1/2)f(sub ce)' emissions where f(sub ce) is the electron gyrofrequency, are analyzed by treating them as analogous to sounder-stimulated ionospheric emissions. We show that both individual AMPTE/IRM spectra of magnetospheric banded emissions, and a statistically-derived spectra observed over the two-year lifetime of the mission, can be interpreted in a self-consistent manner. The analysis, which predicts all spectral peaks within 4% of the observed peaks, interprets the higher-frequency emissions as due to low group-velocity Bernstein-mode waves and the lower-frequency emissions as eigen modes of cylindrical-electromagnetic-plasma-oscillations. The demarcation between these two classes of emissions is the electron plasma frequency f(sub pe), where an emission is often observed. This f(sub pe), emission is not necessarily the strongest. None of the observed banded emissions were attributed to the upper-hybrid frequency. We present Alouette-2 and ISIS-1 plasma-resonance data, and model electron temperature (T(sub e)) values, to support the argument that the frequency-spectrum of ionospheric sounder-stimulated emissions is not strongly temperature dependent and thus that the interpretation of these emissions in the ionosphere is relevant to other plasmas (such as the magnetosphere) where N(sub e) and T(sub e) can be quite different but where the ratio f(sub pe)/f(sub ce) is identical.

Benson, Robert F.↗
NASA NTRS2002

Correlations among the Optical Properties of Cirrus-Cloud Particles: Microphysical Interpretation

Cirrus measurements obtained with a ground-based polarization Raman lidar at 67.9 deg N in January 1997 reveal a strong positive correlation between the particle optical properties, specifically depolarization ratio delta(sub par) and extinction- to-backscatter (lidar) ratio S, for delta(sub par) less than approximately 40%, and an anti-correlation for delta(sub par) greater than approximately 40%. Over the length of the measurements the particle properties vary systematically. Initially, delta (sub par) approximately equals 60% and S approximately equals 10sr are observed. Then, with decreasing delta(sub par), S first increases to approximately 27sr (delta(sub par) approximately equals 40%) before decreasing to values around 10sr again (delta(sub par) approximately equals 20%). The analysis of lidar humidity and radiosonde temperature data shows that the measured optical properties stem from scattering by dry solid ice particles, while scattering by supercooled droplets, or by wetted or subliming ice particles can be excluded. For the microphysical interpretation of the lidar measurements, ray-tracing computations of particle scattering properties have been used. The comparison with the theoretical data suggests that the observed cirrus data can be interpreted in terms of size, shape, and, under the assumption that the lidar measurements of consecutive cloud segments can be mapped on the temporal development of a single cloud parcel moving along its trajectory, growth of the cirrus particles: Near the cloud top in the early stage of cirrus development, light scattering by nearly isometric particles that have the optical characteristics of hexagonal columns (short, column-like particles) is dominant. Over time the ice particles grow, and as the cloud base height extends to lower altitudes characterized by warmer temperatures they become morphologically diverse. For large S and depolarization values of approximately 40%, the scattering contributions of column- and plate-like particles are roughly the same. In the lower ranges of the cirrus clouds, light scattering is predominantly by plate-like ice particles. This interpretation assumes random orientation of the cirrus particles. Simulations with a simple model suggest, however, that the positive correlation between S and delta(sub par) which is observed for depolarization ratios less than 40% mainly at low cloud altitudes, can be alternatively explained by horizontal alignment of a fraction of the cirrus particle population.

Reichardt, J.↗
NASA NTRS2005

THE DEFINITION AND INTERPRETATION OF TERRESTRIAL ENVIRONMENT DESIGN INPUTS FOR VEHICLE DESIGN CONSIDERATIONS

The description and interpretation of the terrestrial environment (0-90 km altitude) is an important driver of aerospace vehicle structural, control, and thermal system design. NASA is currently in the process of reviewing the meteorological information acquired over the past decade and producing an update to the 1993 Terrestrial Environment Guidelines for Aerospace Vehicle Design and Development handbook. This paper addresses the contents of this updated handbook, with special emphasis on new material being included in the areas of atmospheric thermodynamic models, wind dynamics, atmospheric composition, atmospheric electricity, cloud phenomena, atmospheric extremes, sea state, etc. In addition, the respective engineering design elements will be discussed relative to the importance and influence of terrestrial environment inputs that require consideration and interpretation for design applications. Specific lessons learned that have contributed to the advancements made in the acquisition, interpretation, application and awareness of terrestrial environment inputs for aerospace engineering applications are discussed.

Johnson, Dale L.↗
NASA NTRS2009

Column Experiments to Interpret Weathering in Columbia Hills

Phosphate mobility has been postulated as an indicator of early aqueous activity on Mars. In addition, rock surfaces analyzed by the Mars Exploration Rover Spirit are consistent with the loss of a phosphate- containing mineral To interpret phosphate alteration behavior on Mars, we performed column dissolution experiments leaching the primary phases Durango fluorapatite, San Carlos olivine, and basalt glass (Stapafjell Volcano, courtesy of S. Gislason, University of Iceland) [3,4]) with acidic solutions. These phases were chosen to represent quickly dissolving phases likely present in Columbia Hills. Column dissolution experiments are closer to natural dissolution conditions than batch experiments, although they can be difficult to interpret. Acidic solutions were used because the leached layers on the surfaces of these rocks have been interpreted as resulting from acid solutions [5].

Hausrath, E. M.↗
NASA NTRS2016

Limb Correction of Infrared Imagery in Cloudy Regions for the Improved Interpretation of RGB Composites

Red-Green-Blue (RGB) composites (EUMETSAT User Services 2009) combine information from several channels into a single composite image. RGB composites contain the same information as the original channels, but presents the information in a more efficient manner. However, RGB composites derived from infrared imagery of both polar-orbiting and geostationary sensors are adversely affected by the limb effect, which interferes with the qualitative interpretation of RGB composites at large viewing zenith angles. The limb effect, or limb-cooling, is a result of an increase in optical path length of the absorbing atmosphere as viewing zenith angle increases (Goldberg et al. 2001; Joyce et al. 2001; Liu and Weng 2007). As a result, greater atmospheric absorption occurs at the limb, causing the sensor to observe anomalously cooler brightness temperatures. Figure 1 illustrates this effect. In general, limb-cooling results in a 4-11 K decrease in measured brightness temperature (Liu and Weng 2007) depending on the infrared band. For example, water vapor and ozone absorption channels display much larger limb-cooling than infrared window channels. Consequently, RGB composites created from infrared imagery not corrected for limb effects can only be reliably interpreted close to nadir, which reduces the spatial coverage of the available imagery. Elmer (2015) developed a reliable, operational limb correction technique for clear regions. However, many RGB composites are intended to be used and interpreted in cloudy regions, so a limb correction methodology valid for both clear and cloudy regions is needed. This paper presents a limb correction technique valid for both clear and cloudy regions, which is described in Section 2. Section 3 presents several RGB case studies demonstrating the improved functionality of limb-corrected RGBs in both clear and cloudy regions, and Section 4 summarizes and presents the key conclusions of this work.

RGB↗
NASA NTRS2019

Static Analysis Using Abstract Interpretation

Lecture about abstract interpretation. This lecture starts with a brief introduction to validation and verification using formal methods. It then demonstrates IKOS (Inference Kernel for Open Static Analyzers), a static analyzer for C/C++ based on Abstract Interpretation. Then, it describes in details the theory of Abstract Interpretation, a mathematical framework to over-approximate the reachable states of a program.

Arthaud, Maxime↗
NASA NTRSDate not supplied

Interpretation and Formalization of the Right-of-Way Rules

This paper presents an interpretation and mathematical definition of the right-of-way rules as stated in USA, Title 14 of the Code of Federal Regulations, Part 91, Section 91.113 (14 CFR 91.113). In an encounter between two aircraft, the right-of-way rules in 14 CFR 91.113 defines which aircraft, if any, has the right-of-way and which aircraft must maneuver to stay well clear of the other aircraft. The objective of the work presented in this paper is to give an unambiguous interpretation of the rules. From the interpretation, a precise mathematical formulation is created that can be used for analysis and proof of properties. The mathematical formulation has been defined in the Prototype Verification System (PVS) and properties of well formedness and core properties of the formalization have been mechanically proved. Additional objectives are to use the mathematical formulation to code a rule compliant virtual pilot that can be used in simulation experiments and to possibly use the rule compliant virtual pilot in autonomous operations.

right-of-way↗
NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗
NASA NTRSDate not supplied

Interpretation and Formalization of the Right-of-Way Rules

This paper presents an interpretation and mathematical definition of the right-of-way rules as stated in USA, Title 14 of the Code of Federal Regulations, Part 91, Section 91.113 (14 CFR 91.113). In an encounter between two aircraft, the right-of-way rules defines which aircraft, if any, has the right-of-way and which AQ2 aircraft must maneuver to stay well clear of the other aircraft. The objective of the work presented in this paper is to give an unambiguous interpretation of the rules. From the interpretation, a precise mathematical formulation is created that can be used for analysis and proof of properties. The mathematical formulation has been defined in the Prototype Verification System (PVS) and properties of well formedness and core properties of the formalization have been mechanically proved. This mathematical formulation can be implemented digitally, so that right-of-way rules can be used in simulation or in future autonomous operations.

Right-of-Way↗
NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗
DOE OSTI2020

Panel Session 110B: US DOE High-Level Radioactive Waste (HLW) Interpretation

This panel focused on the US DOE High-Level radioactive Waste (HLW) definition interpretation. Discussion integrated topics such as the issuance of the HLW interpretation Federal Registration Notice, the status of the draft Environmental Assessment that analyzes the treatment and commercial disposal of up to 10,000-gallons of SRS Defense Waste Processing Facility recycle wastewater in South Carolina, and the path forward. Panelists with presentations: Status on the Environmental Assessment for the Commercial Disposal of Defense Waste Processing Facility Recycle Wastewater from SRS (Theresa Kliczewski); Enhancing Stakeholder Engagement on the HLW interpretation (Kara Colton, Rick McLeod); Technical Aspects of Potential Disposal of SRS DWPF Recycle Wastewater (Kent Rosenberger)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often prioritizes maximizing reconstruction fidelity while overlooking interpretability, an essential aspect for deriving neuroscientific insight. To address this gap, we propose MoRE-Brain, a neuro-inspired framework designed for high-fidelity, adaptable, and interpretable visual reconstruction. MoRE-Brain uniquely employs a hierarchical Mixture-of-Experts architecture where distinct experts process fMRI signals from functionally related voxel groups, mimicking specialized brain networks. The experts are first trained to encode fMRI into the frozen CLIP space. A finetuned diffusion model then synthesizes images, guided by expert outputs through a novel dual-stage routing mechanism that dynamically weighs expert contributions across the diffusion process. MoRE-Brain offers three main advancements: First, it introduces a novel Mixture-of-Experts architecture grounded in brain network principles for neuro-decoding. Second, it achieves efficient cross-subject generalization by sharing core expert networks while adapting only subject-specific routers. Third, it provides enhanced mechanistic insight, as the explicit routing reveals precisely how different modeled brain regions shape the semantic and spatial attributes of the reconstructed image. Extensive experiments validate MoRE-Brain’s high reconstruction fidelity, with bottleneck analyses further demonstrating its effective utilization of fMRI signals, distinguishing genuine neural decoding from over-reliance on generative priors. Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2025

SPARTA: A flux adjustment methodology to interpret complex experiments

For the accurate determination of reactivity from a detector count rate, correction of spatial effects is of prime importance. This spatial correction is often provided using simulation methodologies, but this may introduce a bias if the result of the experiment is also used as input data for the simulation. Here, this work presents a flux adjustment methodology able to infer experimental reactivity and correction of spatial effects without the need for a simulation. It can process the signal from a complex experiment such as a heat balance measurement in the TREAT reactor, where control rods are continuously adjusted to maintain a constant power. In the present work, this methodology successfully computed the reactivity and the local spatial variation of the flux of a generated signal. It also proved to be robust against noise and errors on kinetic parameters and provides a credible interpretation of a heat balance experiment in TREAT. Efficiency of flux adjustment methods for complex experiment enable a better experiment interpretation less reliant on nuclear data evaluation.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

14 SOLAR ENERGY↗
DOE OSTI2026

Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces

This paper develops an interpretable, non-intrusive reduced-order modeling technique using regularized kernel interpolation. Existing non-intrusive approaches approximate the dynamics of a reduced-order model (ROM) by solving a data-driven least-squares regression problem for low-dimensional matrix operators. Our approach instead leverages regularized kernel interpolation, which yields an optimal approximation of the ROM dynamics from a user-defined reproducing kernel Hilbert space. We show that our kernel-based approach can produce interpretable ROMs whose structure mirrors full-order model structure by embedding judiciously chosen feature maps into the kernel. The approach is flexible and allows a combination of informed structure through feature maps and closure terms via more general nonlinear terms in the kernel. We also derive a computable a posteriori error bound that combines standard error estimates for intrusive projection-based ROMs and kernel interpolants. In conclusion, the approach is demonstrated in several numerical experiments that include comparisons to operator inference using both proper orthogonal decomposition and quadratic manifold dimension reduction.

Data-driven model reduction↗