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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Single Vector Calibration System for Multi-Axis Load Cells and Method for Calibrating a Multi-Axis Load Cell

A single vector calibration system is provided which facilitates the calibration of multi-axis load cells, including wind tunnel force balances. The single vector system provides the capability to calibrate a multi-axis load cell using a single directional load, for example loading solely in the gravitational direction. The system manipulates the load cell in three-dimensional space, while keeping the uni-directional calibration load aligned. The use of a single vector calibration load reduces the set-up time for the multi-axis load combinations needed to generate a complete calibration mathematical model. The system also reduces load application inaccuracies caused by the conventional requirement to generate multiple force vectors. The simplicity of the system reduces calibration time and cost, while simultaneously increasing calibration accuracy.

Parker, Peter A.↗
NASA NTRS2003

Space Environment Factors Affecting the Performance of International Space Station Materials: The First Two Years of Flight Operations

In this paper, the natural and induced space environment factors affecting materials performance on ISS are described in some detail. The emphasis will be on ISS flight experience and the more significant design and development issues of the last two years. The intent is to identify and document the set of space environment factors, affecting materials, that are producing the largest impacts on the ISS flight hardware verification and acceptance process and on ISS flight operations. Orbital inclination (S1.6 ) and altitude (nominal3S0 km to 400 km altitude) determine the set of natural environment factors affecting the functional life of materials and subsystems on ISS. ISS operates in the F2 region of Earth's ionosphere in well-defined fluxes of atomic oxygen, other ionospheric plasma species, and solar UV, VUV, and x-ray radiation, as well as galactic cosmic rays, trapped radiation, and solar cosmic rays (1,2). The high latitude orbital environment also exposes external surfaces to significantly less well-defined or predictable fluxes of higher energy trapped electrons and auroral electrons (3 ,4). The micrometeoroid and orbital debris environment is an important determinant of spacecraft design and operations in any orbital inclination. Environment factors induced by ISS flight operations include ram-wake effects, magnetic induction voltages arising from flight through Earth's magnetic field, hypergolic thruster plume impingement from proximity operations of visiting vehicles, materials outgassing, venting and dumping of fluids, ISS thruster operations, as well as specific electrical power system interactions with the ionospheric plasma (S-7). ISS must fly in a very limited number of approved flight attitudes leading to location specific environmental exposures and extreme local thermal environments (8). ISS is a large vehicle and produces a deep wake structure from which both ionospheric plasma and neutrals (atomic oxygen) are largely excluded (9-11). At high latitude, the ISS wake may produce a spacecraft charging environment similar to that experienced by the DMSP and Freja satellites (800 to 100 km altitude polar orbits), especially during geo-magnetic disturbances (12-14). ISS is also subject to magnetic induction voltages (VxB L) on conducting structure, a result of high velocity flight through Earth's magnetic field. The magnitude of the magnetic induction voltage varies with location on ISS, as well as the relative orientation of the vehicle velocity vector and planetary magnetic field vector, leading to maximum induction voltages at high latitude (15). The space environment factors, natural and induced, that have had the largest impact on pre-launch ISS flight hardware verification and flight operations during the first two years of ISS flight operations are listed below and grouped according to the physical and chemical processes driving their interaction with ISS materials.

Koontz, Steven L.↗
NASA NTRS2023

SelenITA: A Dual Point Lunar Mission to Characterize the Near Surface Dust and Electromagnetic Plasma Environment

SelenITA is a dual CubeSat mission that will provide the first multi-point dust, plasma, and magnetic field measurements in lunar orbit. This mission will advance the understanding of the electromagnetic space environment at the Moon in support of the Artemis program, exploration, and the geosciences. Here we present the science rationale motivating the mission. The candidate mission science objectives include: (1) constrain the origins of crustal magnetic fields; (2) determine the nature of plasma interactions with crustal magnetic fields; (3) characterize plasma waves and turbulence at the Moon; (4) characterize the lunar surface electric potential in varying plasma environments; (5) constrain the composition, thermal state, and structure of the lunar upper mantle and crust; (6) characterize the ionizing radiation in lunar orbit; and lastly, (7) determine the density of the dust exosphere as a function of latitude, longitude, and altitude, including the lunar polar space environment. The measurement requirements include simultaneous two-point observations of the 3-component vector magnetic field, plasma distribution (flux, energy, density, temperature), and single-point observations of energetic particles (protons, electrons, gamma rays), and dust. These measurements are important because it helps us understand how future astronauts, robots, and space hardware will live and work on the lunar surface.

space weather↗
NASA NTRS2023

SelenITA: A Dual Point Lunar Mission to Characterize the Near Surface Dust and Electromagnetic Plasma Environment

SelenITA is a dual CubeSat mission that will provide the first multi-point dust, plasma, and magnetic field measurements in lunar orbit. This mission will advance the understanding of the electromagnetic space environment at the Moon in support of the Artemis program, exploration, and the geosciences. Here we present the science rationale motivating the mission. The candidate mission science objectives include: (1) constrain the origins of crustal magnetic fields; (2) determine the nature of plasma interactions with crustal magnetic fields; (3) characterize plasma waves and turbulence at the Moon; (4) characterize the lunar surface electric potential in varying plasma environments; (5) constrain the composition, thermal state, and structure of the lunar upper mantle and crust; (6) characterize the ionizing radiation in lunar orbit; and lastly, (7) determine the density of the dust exosphere as a function of latitude, longitude, and altitude, including the lunar polar space environment. The measurement requirements include simultaneous two-point observations of the 3-component vector magnetic field, plasma distribution (flux, energy, density, temperature), and single-point observations of energetic particles (protons, electrons, gamma rays), and dust. These measurements are important because it helps us understand how future astronauts, robots, and space hardware will live and work on the lunar surface.

space weather↗
NASA NTRS1986

The influence of velocity variability on the determination of wind profiles

High sensitivity radars allow the determination of velocity estimates at time resolutions down to one minute or better. Because of the variability introduced to the mean wind due to turbulence and waves, the high resolution profiles may not be too useful for forecasting applications, although they yield the most realistic estimate of the instantaneous wind profile. Profiles of wind speed and direction, vertical velocity and echo power, which were deduced in real-time on 23 August 1981 with the spaced antenna drift mode of the SOUSY-VHF-Radar are shown. Whereas these profiles were measured within 1 minute, the operating routine allowed the selection of variable (longer) measuring periods, and one has to search for the optimum duration of the data averaging period. A high time resolution wind vector diagram is given which gives an idea of the temporal variability. The data were obtained with the spaced antenna technique, which allows a good estimate of the horizontal wind without having to correct for the vertical velocity component. The wind vectors specifically indicate a quasi-periodic variation in direction. This is assumed to be due to gravity waves since the vertical velocity also shows periodical variations with the same period. The consistency of these spaced-antenna VHF radar results along with the radiosonde data convinced researchers that the method is quite suitable for wind profiling applications.

Rottger, J.↗
NASA NTRS1989

Topology of three-dimensional, variable density flows

This paper is concerned with the interpretation of unsteady, variable-density flow fields. The topology of the flow is determined by finding critical points and identifying the character of local solution trajectories. The time evolution of the flow is studied by following the paths of the critical points in the three-dimensional space of invariants of the local deformations tensor. The methodology can be applied to any smooth vector field and its associated gradient tensor including the vorticity and pressure gradient fields. This approach provides a framework for describing the geometry of complex flow patterns. Concisely summarizing that geometry in the space of invariants of the local gradient tensor may be a useful way of gaining insight into time-dependent processes described by large computational data bases. Applications to the descriptions of a flickering diffusion flame and a compressible wake are discussed.

Cantwell, Brian↗
NASA NTRS1992

SAMPEX science pointing with velocity avoidance

The Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) science pointing mode is presented with the additional constraint of velocity avoidance. This constraint has been added in light of the orbital debris and micrometeoroid fluxes that have been revealed by the Long Duration Exposure Facility (LDEF) recovered in January 1990. These fluxes are 50-100 times higher than the flux tables that were used in the September 1988 proposal to NASA for the SAMPEX mission. The SAMPEX Heavey Ion Large Telescope (HILT) sensor includes a flow-through isobutane proportional counter that is susceptible to penetration by orbital debris and micrometeoroids. Thus, keeping the HILT sensor pointed away from the velocity vector, the direction of maximum flux, will compensate for the higher than expected fluxes. Using an orbital debris model and a micrometeoroid model developed at the Johnson Space Center (JSC), and a SAMPEX dynamic simulator developed by the Guidance and Control Branch at the Goddard Space Flight Center (GSFC), an 'optimal' minimum ram angle (the angle between the HILT boresight and the velocity vector) of 90 degrees has been determined. It is optimal in the sense of minimizing the science pointing performance degradation while providing approximately an 89 percent chance of survival for the HILT sensor over a three year period.

Frakes, Joseph P.↗
NASA NTRS2024

Earth Independent Medical Operations (EIMO) DATASCOPE Technical Interchange Meeting 21st August 2023: Background and Summary of Discussion

An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

Artificial Intelligence↗
NASA NTRS2005

Rapid Temporal Changes of Boundary Layer Winds

The statistical distribution of the magnitude of the vector wind change over 0.25, 0.5, 1 and 2-h periods based on data from November 1999 through August 2001 is presented. The distributions of the 2-h u and v component wind changes are also presented for comparison. The wind changes at altitudes from 500 to 3000 m were measured using the Eastern Range network of five 915 MHz Doppler radar wind profilers. Quality controlled profiles were produced every 15 minutes for up to sixty gates, each representing 101 m in altitude over the range from 130 m to 6089 m. Five levels, each constituting three consecutive gates, were selected for analysis because of their significance to aerodynamic loads during the Space Shuttle ascent roll maneuver. The distribution of the magnitude of the vector wind change is found to be lognormal consistent with earlier work in the mid-troposphere. The parameters of the distribution vary with time lag, season and altitude. The component wind changes are symmetrically distributed with near-zero means, but the kurtosis coefficient is larger than that of a Gaussian distribution.

Merceret, Francis J.↗
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
DOE OSTI2024

Three-dimensional continuum point cloud method for large deformation and its verification

This study presents a strong form based meshfree collocation method, which is named Continuum Point Cloud Method, to solve nonlinear field equations derived from classical mechanics for deformed bodies in three-dimensional Euclidean space. The method and its implementation are benchmarked against a nonlinear vector field using manufactured solutions. The analysis of mechanical fields firstly focuses on the study of St. Venant Kirchhoff and compressible neo-Hookean materials. Results for various initial boundary value problems are presented, including benchmark cases involving unidirectional tension and simple shear. Subsequently, the study concludes with an analysis of a displacement-controlled simulation of a compressible neo-Hookean material, specifically a bar that is pulled to 50% of its original length and rotated 90°. The pure tension case yields a 1.5% error in displacement between computed and expected values and a combined tension and torsion loading case provides further insight into material behavior under complex loading conditions. The resulting normal axial and transverse stress-strain curves are also presented. Lastly, the consistency and robustness of the proposed nonlinear numerical schemes are successfully demonstrated through various numerical experiments.

Compressible neo-Hookean materials↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The 3D Lyman- α forest power spectrum from eBOSS DR16

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-α (Ly α) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of ~205 000 quasar spectra in the redshift range 2 ≤ z ≤ 4 at an effective redshift z = 2.334. We propose a pair-count spectral estimator in configuration space, weighting each pair by exp( i k ∙ r), for wave vector k and pixel pair separation r, effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly α skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly α skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range $(0.02 ≤ k ≤ 0.35)$ h Mpc -1 at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly α forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

The HSA is a statistics based anomaly detection model. The model performs unsupervised anomaly detection, based on a datapoint's density and similarity within a dataset. Density and similarity data are encoded into an affinity matrix. The affinity matrix is evolved to summarize the data's structure on greater topographical scales within the data's function space. The set of evolved affinity matrices and an anomaly score vector are passed to a user defined penalized objective function. The penalized objective function of anomaly scores is then minimized. Data points where the absolute value of the z-scores of anomaly scores greater than a specified threshold are predicted as anomalies. A novel multi-filter feature has also been implemented. To reduce false positive rates, the multi-filter records the indexes of the HSA predictions. A new dataset and data loader are instantiated consisting of all the initial HSA predictions and non-anomalous data points in a 10% and 90% split respectively. The HSA is then run through this data set and a count of number of times a data point is predicted is kept. In this way the initial predictions may be compared with data spanning the entire dataset. After the multi-filter is complete, all datapoints will have an associated anomaly score, as well as a multi-filter prediction count to further filter the anomalous predictions.

Rogers, DempseyD [Idaho National Laboratory (INL),↗
NASA NTRS1973

The infinum principle

A complete theory of necessary and sufficient conditions is discussed for a control to be superior with respect to a nonscalar-valued performance criterion. The latter maps into a finite dimensional, integrally closed directed, partially ordered linear space. The applicability of the theory to the analysis of dynamic vector estimation problems and to a class of uncertain optimal control problems is demonstrated.

Geering, H. P.↗
NASA NTRS1974

The infimum principle

This paper reports a reasonably complete theory of necessary and sufficient conditions for a control to be superior with respect to a nonscalar-valued performance criterion. The latter maps into a finite-dimensional integrally closed directed partially ordered linear space. The applicability of the theory to the analysis of dynamic vector estimation problems and to a class of uncertain optimal control problems is demonstrated.

Geering, H. P.↗
NASA NTRS1978

Quasi-optimal decoding of linear block codes using soft decision detection

A simple but effective decoding procedure, applicable to any (n,k) linear block code with symbols from GF(q), is described. The technique involves a transformation of the parity check equations which focuses the code's correction power on the soft symbol set while still retaining the capability to correct one symbol error from outside this set. The soft symbol set is defined to be the n-k least reliably detected code symbol positions whose parity check row-spaces are linearly independent. The process generates a number of error vector screening candidates, each a solution to the parity check equations, and the maximum-likelihood candidate is accepted.

Greene, E. P.↗
NASA NTRS1987

Control of SCOLE

A relatively low order model is used to control SCOLE. Drastic truncation of the discretized model is proposed by means of a modal expansion. An open loop eigenvalue problem is illustrated as is truncated modal equations, modal state equations, actual output vector and modal Kalman filter. Also illustrated is independent modal-space control.

Meirovitch, L.↗