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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. 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 feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗
DOE OSTI2024

Effect of XC functionals and dispersion corrections on the DFT‐computed structural and vibrational properties of SrCl 2 –NaCl and ZrF 4 –LiF

Density functional theory (DFT) calculations were performed to examine the impact of exchange–correlation (XC) functionals and van der Waals corrections (specifically the D3 method) on the structural and vibrational properties of the SrCl 2 –NaCl and ZrF 4 –LiF salt systems. Multiple XC functionals, including the local density approximation (LDA), the generalized gradient approximation using the Perdew–Burke–Ernzerhof (PBE) model, and its modified form suitable for solids (PBEsol), the dispersion-corrected PBE-D3 and PBEsol-D3, were considered. Of these functionals, LDA was found to exhibit the highest degree of error, while PBEsol and PBE-D3 displayed the least error. Underestimated lattice parameters compared with experimental values were observed to result in higher force constants, leading to an overprediction of vibrational frequencies. Conversely, an overestimation of lattice parameters was associated with lower vibrational frequencies. The methodology presented in this study yielded results that are in good agreement with experiment, irrespective of the method (finite differences vs. density functional perturbation theory) employed for calculating infrared and Raman spectra. It was further demonstrated that for alkali halides with weak Raman scattering, utilizing a supercell constructed from primitive cells better predicts Raman features than does the use of conventional cells.

Raman↗
DOE OSTI2024

Modeling phonon-mediated quasiparticle poisoning in superconducting qubit arrays

Correlated errors caused by ionizing radiation impacting superconducting qubit chips are problematic for quantum error correction. Such impacts generate quasiparticle (QP) excitations in the qubit electrodes, which temporarily reduce qubit coherence significantly. The many energetic phonons produced by a particle impact travel efficiently throughout the device substrate and generate quasiparticles with high probability, thus causing errors on a large fraction of the qubits in an array simultaneously. Here, we describe a comprehensive strategy for the numerical simulation of the phonon and quasiparticle dynamics in the aftermath of an impact. We compare the simulations with experimental measurements of phonon-mediated QP poisoning and demonstrate that our modeling captures the spatial and temporal footprint of the QP poisoning for various configurations of phonon downconversion structures. We thus present a path forward for the operation of superconducting quantum processors in the presence of ionizing radiation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

CuXASNet: Rapid and accurate prediction of copper L-edge x-ray absorption spectra using machine learning

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu -edge x-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra from FEFF9 at the multiple scattering level of theory, and can predict the and edges for Cu sites to quantitative accuracy. To validate our approach, we compare 14 experimental spectra extracted from the literature with the predictions of CuXASNet. The agreement of CuXASNet with experiments is shown by an average mean absolute error of 0.125 and an average Spearman's correlation coefficient of 0.891, which is comparable to FEFF9's values of 0.131 and 0.898 for the same metrics. As such, CuXASNet can rapidly predict a large number of -edge XAS spectra at the same accuracy as FEFF9 simulations. This can be used as a drop-in replacement for multiple scattering codes for fast screening of candidate atomic structure models of a measured system. This model establishes a general framework for Cu XAS prediction, and can be extended to more computationally expensive levels of theory and to other transition metal edges.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Evaluation of the WRF lake module (v1.0) and its improvements at a deep reservoir

Large lakes and reservoirs play important roles in modulating regional hydrological cycles and climate; however, their representations in coupled models remain uncertain. The existing lake module in the Weather Research and Forecasting (WRF) system (hereafter WRF-Lake), although widely used, did not accurately predict temperature profiles in deep lakes mainly due to its poor lake surface property parameterizations and underestimation of heat transfer between lake layers. We therefore revised WRF-Lake by improving its (1) numerical discretization scheme; (2) surface property parameterization; (3) diffusivity parameterization for deep lakes; and (4) convection scheme, the outcome of which became WRF-rLake (i.e., revised lake model). We evaluated the off-line WRF-rLake by comparing simulated and measured water temperature at the Nuozhadu Reservoir, a deep reservoir in southwestern China. WRF-rLake performs better than its predecessor by reducing the root-mean-square error (RMSE) against observed lake surface temperatures (LSTs) from 1.4 to 1.1°C and consistently improving simulated vertical temperature profiles. We also evaluated the sensitivity of simulated water temperature and surface energy fluxes to various modeled lake processes. We found (1) large changes in surface energy balance fluxes (up to 60 W m -2 ) associated with the improved surface property parameterization and (2) that the simulated lake thermal structure depends strongly on the light extinction coefficient and vertical diffusivity. Although currently only evaluated at the Nuozhadu Reservoir, we expect that these model parameterization and structural improvements could be general and therefore recommend further testing at other deep lakes and reservoirs.

58 GEOSCIENCES↗
NASA NTRS1992

Improvement of structural models using covariance analysis and nonlinear generalized least squares

The next generation of large, flexible space structures will be too light to support their own weight, requiring a system of structural supports for ground testing. The authors have proposed multiple boundary-condition testing (MBCT), using more than one support condition to reduce uncertainties associated with the supports. MBCT would revise the mass and stiffness matrix, analytically qualifying the structure for operation in space. The same procedure is applicable to other common test conditions, such as empty/loaded tanks and subsystem/system level tests. This paper examines three techniques for constructing the covariance matrix required by nonlinear generalized least squares (NGLS) to update structural models based on modal test data. The methods range from a complicated approach used to generate the simulation data (i.e., the correct answer) to a diagonal matrix based on only two constants. The results show that NGLS is very insensitive to assumptions about the covariance matrix, suggesting that a workable NGLS procedure is possible. The examples also indicate that the multiple boundary condition procedure more accurately reduces errors than individual boundary condition tests alone.

Glaser, R. J.↗
NASA NTRS1988

Modal model reduction or model reduction of large space structures in frequency domain

Large space structures are characterized by a large number of modes, grouped frequencies, and small inherent damping. Model reduction techniques in time domain may not be effective due to small damping. The model truncation method is generally used. This method can not solve the problem of grouped frequencies, and will lose all the information about the higher order modes. A new method developed in this paper, which tries to minimize the error of interested transfer functions, makes use of all the information of the original system, and achieves improvement not only from a smaller error of transfer functions but also from better frequency distribution.

Mifang, Ruan↗
NASA NTRS1995

Ball bearing vibrations amplitude modeling and test comparisons

Bearings generate disturbances that, when combined with structural gains of a momentum wheel, contribute to induced vibration in the wheel. The frequencies generated by a ball bearing are defined by the bearing's geometry and defects. The amplitudes at these frequencies are dependent upon the actual geometry variations from perfection; therefore, a geometrically perfect bearing will produce no amplitudes at the kinematic frequencies that the design generates. Because perfect geometry can only be approached, emitted vibrations do occur. The most significant vibration is at the spin frequency and can be balanced out in the build process. Other frequencies' amplitudes, however, cannot be balanced out. Momentum wheels are usually the single largest source of vibrations in a spacecraft and can contribute to pointing inaccuracies if emitted vibrations ring the structure or are in the high-gain bandwidth of a sensitive pointing control loop. It is therefore important to be able to provide an a priori knowledge of possible amplitudes that are singular in source or are a result of interacting defects that do not reveal themselves in normal frequency prediction equations. This paper will describe the computer model that provides for the incorporation of bearing geometry errors and then develops an estimation of actual amplitudes and frequencies. Test results were correlated with the model. A momentum wheel was producing an unacceptable 74 Hz amplitude. The model was used to simulate geometry errors and proved successful in identifying a cause that was verified when the parts were inspected.

Hightower, Richard A., III↗
NASA NTRS1998

Stratospheric Assimilation of Chemical Tracer Observations Using a Kalman Filter: Chi-Square Validated Results and Analysis of Variance and Correlation Dynamics - Pt. 2

A Kalman filter system designed for the assimilation of limb-sounding observations of stratospheric chemical tracers, which has four tunable covariance parameters, was developed in Part I (Menard et al. 1998) The assimilation results of CH4 observations from the Cryogenic Limb Array Etalon Sounder instrument (CLAES) and the Halogen Observation Experiment instrument (HALOE) on board of the Upper Atmosphere Research Satellite are described in this paper. A robust (chi)(sup 2) criterion, which provides a statistical validation of the forecast and observational error covariances, was used to estimate the tunable variance parameters of the system. In particular, an estimate of the model error variance was obtained. The effect of model error on the forecast error variance became critical after only three days of assimilation of CLAES observations, although it took 14 days of forecast to double the initial error variance. We further found that the model error due to numerical discretization as arising in the standard Kalman filter algorithm, is comparable in size to the physical model error due to wind and transport modeling errors together. Separate assimilations of CLAES and HALOE observations were compared to validate the state estimate away from the observed locations. A wave-breaking event that took place several thousands of kilometers away from the HALOE observation locations was well captured by the Kalman filter due to highly anisotropic forecast error correlations. The forecast error correlation in the assimilation of the CLAES observations was found to have a structure similar to that in pure forecast mode except for smaller length scales. Finally, we have conducted an analysis of the variance and correlation dynamics to determine their relative importance in chemical tracer assimilation problems. Results show that the optimality of a tracer assimilation system depends, for the most part, on having flow-dependent error correlation rather than on evolving the error variance.

Menard, Richard↗
NASA NTRS2005

Thermal Conductivity and Specific Heat Measurements of Candidate Structural Materials for the JWST Optical Bench

The James Webb Space Telescope will include an optical bench known as the integrated science instrument module (ISIM). Candidate structural materials for the ISIM must have low density, high stiffness, high thermal conductivity, and low thermal expansion coefficient at the operating temperature of 30 Kelvin. The specific heat is also important in modeling the on-orbit cooldown. We built two different systems for measuring the thermal conductivity and specific heat of samples between 4 Kelvin and 290 Kelvin. Both experiments were carefully designed to minimize potential errors due to radiative heat transfer. We chose the cooling system and instrumentation to allow long-term unattended operation. Software was developed to automate each experiment. It used an algorithm designed to ensure that each system was in thermal equilibrium before a measurement was taken. We describe the two experiments and present the data.

Canavan, E. R.↗
NASA NTRS2015

Wing Shape Sensing from Measured Strain

A new two-step theory is investigated for predicting the deflection and slope of an entire structure using strain measurements at discrete locations. In the first step, a measured strain is fitted using a piecewise least-squares curve fitting method together with the cubic spline technique. These fitted strains are integrated twice to obtain deflection data along the fibers. In the second step, computed deflection along the fibers are combined with a finite element model of the structure in order to interpolate and extrapolate the deflection and slope of the entire structure through the use of the System Equivalent Reduction and Expansion Process. The theory is first validated on a computational model, a cantilevered rectangular plate wing. The theory is then applied to test data from a cantilevered swept-plate wing model. Computed results are compared with finite element results, results using another strain-based method, and photogrammetry data. For the computational model under an aeroelastic load, maximum deflection errors in the fore and aft, lateral, and vertical directions are -3.2 percent, 0.28 percent, and 0.09 percent, respectively; and maximum slope errors in roll and pitch directions are 0.28 percent and -3.2 percent, respectively. For the experimental model, deflection results at the tip are shown to be accurate to within 3.8 percent of the photogrammetry data and are accurate to within 2.2 percent in most cases. In general, excellent matching between target and computed values are accomplished in this study. Future refinement of this theory will allow it to monitor the deflection and health of an entire aircraft in real time, allowing for aerodynamic load computation, active flexible motion control, and active induced drag reduction..

system equivalent reduction and expansion process↗
NASA NTRS1981

Structure of the solar chromosphere. III - Models of the EUV brightness components of the quiet-sun

The described investigation is concerned with the solution of the non-LTE optically thick transfer equations for hydrogen, carbon, and other constituents to determine semiempirical models for six components of the quiet solar chromosphere. For a given temperature-height distribution, the solution is obtained of the equations of statistical equilibrium, radiative transfer for lines and continua, and hydrostatic equilibrium to find the ionization and excitation conditions for each atomic constituent. The emergent spectrum is calculated, and a trial and error approach is used to adjust the temperature distribution so that the emergent spectrum is in best agreement with the observed one. The relationship between semiempirical models determined in this way and theoretical models based on radiative equilibrium is discussed by Avrett (1977). Harvard Skylab EUV observations are used to determine models for a number of quiet-sun regions.

Vernazza, J. E.↗
DOE OSTI2025

Turbulent Processes that Influence Boundary-Layer Cloud Structure (Final Technical Report)

Many models of the atmosphere simulate vertical profiles of observable fields, such as temperature, moisture, and vertical velocity. To assess the realism of such model output, it is important to evaluate the modeled profiles against observations. However, many extant evaluations consist of comparing time averaged profiles from the model and the observations. Such time averages can smooth out unrealistic features that appear in instantaneous profiles. In this way, the time averages may obscure certain model errors from detection. For instance, many models misrepresent turbulent processes, leading to profiles that are too smoothed out or too noisy. E.g., if a model has coarse vertical resolution and overly strong diffusion, then the model may be unable to produce observed sharp peaks. On the other hand, a model may have artificial approximations that produce spurious jumps in the vertical. Much can be learned by browsing through instantaneous snapshots of model output. But the volume of output produced by a global model is far too great to rely purely on visual inspection. In addition, we need a way to statistically characterize the profiles, so that anomalies can be detected more conveniently.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

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

Systematic analysis of magnetic equilibrium reconstruction with eddy currents on LTX- β

Magnetic equilibrium reconstruction in the Lithium Tokamak Experiment-Beta (LTX-β) is complicated by strong eddy currents and toroidal asymmetries arising from its segmented, close-fitting conducting shell. In the earlier experiment LTX, these three-dimensional (3D) conductor effects significantly distorted diagnostic signals and challenged conventional axisymmetric reconstruction methods. In this work, we demonstrate that accurate plasma equilibria can be recovered on LTX-β by incorporating a small number of dominant eddy current modes derived from realistic conductor models. Using the open-source $\tt{TokaMaker}$ Grad–Shafranov solver, we reconstruct equilibria across a systematically selected set of LTX-β discharges and validate them against the legacy $\tt{PSI-Tri}$ hybrid 2D-3D code. Our results show that fully 2D $\tt{TokaMaker}$ reconstructions achieve significantly improved agreement with flux loop and Mirnov probe measurements, reducing total chi-squared fitting errors, especially during startup. Finally, we develop new hybrid 2D-3D $\tt{TokaMaker}$ reconstructions by integrating the $\tt{ThinCurr}$ 3D eddy current model, which we optimize for significant further reduction of chi-squared in most scenarios. These findings underscore the importance of realistic wall-current modeling in short-pulse tokamaks, and establish a physics-based reconstruction framework that is extensible to devices with complex passive structures and 3D wall interactions.

Grad–Shafranov solver↗
NASA NTRS1984

The p-wave upper mantle structure beneath an active spreading centre - The Gulf of California

Over 1400 seismograms of earthquakes in Mexico are analyzed and data sets for the travel time, apparent phase velocity, and relative amplitude information are utilized to produce a tightly constrained, detailed model for depths to 900 km beneath an active oceanic ridge region, the Gulf of California. The data are combined by first inverting the travel times, perturbing that model to fit the p-delta data, and then performing trial and error synthetic seismogram modelling to fit the short-period waveforms. The final model satisfies all three data sets. The ridge model is similar to existing upper mantle models for shield, tectonic-continental, and arc-trench regimes below 400 km, but differs significantly in the upper 350 km. Ridge model velocities are very low in this depth range; the model 'catches up' with the others with a very large velocity gradient from 225 to 390 km.

Walck, M. C.↗
NASA NTRS1998

Experiment 1: Orbital Processing of High-Quality Zn-Alloyed CdTe Compound Semiconductors

Zinc-alloyed cadmium telluride (Zn:CdTe) crystals were grown in unit gravity and in microgravity for comparative analysis. Two crystals were grown on USML-1/STS-50 in 1992, and two additional crystals were grown on USML-2/STS-73 in 1995. The Crystal Growth Furnace (CGF) in the seeded Bridgman-Stockbarger crystal growth geometry was utilized on both missions. Crystals grown on USML-1/STS-50 were found to have solidified with partial wall contact due to the near-absence of the hydrostatic pressure in microgravity, a residual g-vector that was not axial, and the nonwetting sample/ampoule wetting conditions. Crystals grown on USML-2/STS-73 included: a sample/ampoule identical to the USML-1/STS-73 sample/ampoule, with the addition of a restraining spring to simulate hydrostatic pressure internally, and a tapered ampoule which accomplished 2.2 cm of crystal growth without wall contact. Infrared transmission of all ground and flight samples was found to be 63-66 percent, very close to the theoretical 66 percent, suggesting good stoichiometric control. Infrared microscopy confirmed that the primary precipitates were Te and their size (1-10 microns) and density suggested that the flight- and ground-base samples experienced similar thermal histories. Longitudinal macrosegregation, calculated using scaling analysis, was predicted to be low. Nearly diffusion controlled growth was achieved even in unit gravity and macrosegregation data could be fit with a diffusion controlled model. Radial segregation was monitored and was found to vary with fraction solidified, particularly through the shoulder region, where the sample cross section was varying significantly. It was also disturbed in the flight samples in regions where asymmetric wall contact was noted. In regions where a steady state was established, the radial segregation was invariant within our experimental measurement error. Flight samples were found to be much higher in structural perfection than samples processed in unit gravity under identical growth conditions. In regions where solidification had occurred without wall contact, the free surfaces evidenced virtually no twinning, although twins appeared in the flight samples in regions of wall contact and were pervasive in the ground samples. These results were confirmed using optical microscopy and synchrotron x-ray white beam topography. Full-width half-maximum rocking curve widths (FWHM), recorded in arc-seconds, were significantly reduced from 20 a-s (1 g) to 9 a-s (micro-g) for the best regions of the crystals. The 9 a-s (FWHM) rocking curve value in the unconfined flight samples equals the best value reported terrestrially for this material. The ground samples exhibited a fully developed (111)[110] dislocation mosaic structure, whereas dislocations within the flight samples were discrete and no mosaic structure was evident. The defect density was quantitatively reduced from 75,000 (1 g) to 800 (micro-g) +/-50 percent. Dislocation etch pit density results were confirmed using transmission synchrotron white beam and monochromated beam topography. The low defect density is thought to have resulted from the near absence of hydrostatic pressure, which allowed the molten boule to solidify with little or no wall contact. This minimized the transfer of hoop stresses during solidification and post-solidification processing.

Larson, D. J., Jr.↗
NASA NTRS2019

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Brazilian Amazon↗