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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. Here, this work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

Casey, Ryan T. F. [Georgia Institute of Technology↗
NASA NTRS1973

The asymptotic distribution of maxima in bivariate samples

The joint distribution (as n tends to infinity) of the maxima of a sample of n independent observations of a bivariate random variable (X,Y) is studied. A method is developed for deriving the asymptotic distribution of the maxima, assuming that X and Y possess asymptotic extreme-value distributions and that the probability element dF(x,y) can be expanded in a canonical series. Applied both to the bivariate normal distribution and to the bivariate gamma and compound correlated bivariate Poisson distributions, the method shows that maxima from all these distributions are asymptotically uncorrelated.

Campbell, J. W.↗
NASA NTRSDate not supplied

The Two-Cavity Method for Characterizing Acoustical Materials—an Interlaboratory Study

This study focuses on understanding the sound absorption characteristics of porous acoustic materials, which are determined by two key parameters independent of material thickness: characteristic impedance and propagation constant. These parameters can be characterized by testing a porous sample concept in a normal incidence impedance tube using either the two-thickness or the two-cavity method. In the two-thickness method, two samples of varying thicknesses are required. In contrast, the two-cavity method requires testing one sample at two different air cavity depths behind the porous material. This method is particularly advantageous for materials that are costly or challenging to fabricate. This interlaboratory study evaluates the variability of characteristic properties determined using the two-cavity method. Porous acoustic materials were additively manufactured and tested in the Liner Technology Facility(LTF)at NASA Langley Research Center and the Mechanics, Acoustics and Dynamics Lab (MADLab) at Michigan Technological University. The characteristic properties derived from various cavity depth combinations were used to predict the surface impedance of the sample with a rigid backing. The prediction was then compared the measured impedance spectrum. It was also found that the combinations of cavity depths can significantly influence the accuracy of the deduced properties.

acoustic liners↗
DOE OSTI2020

Primary $\gamma$-ray intensities and $\gamma$-strength functions from discrete two-step $\gamma$-ray cascades in radiative proton-capture experiments

Background: Reaction rates of radiative capture reactions can play a crucial role in the nucleosynthesis of heavy nuclei in explosive stellar environments. These reaction rates depend strongly on $\gamma$-ray decay widths in the reaction products, which are, for nonresonant capture reactions at high excitation energies, derived from the $\gamma$-ray strength function and the nuclear level density. Recently, the ratio method was applied to primary $\gamma$ rays observed from ($\textit{d, p}$) reactions and nuclear resonance fluorescence measurements to extract the dipole strength in atomic nuclei and to test the generalized Brink-Axel hypothesis. Purpose: The purpose of this work is to apply the ratio method to primary $\gamma$-ray intensities of the 63,65 Cu($\textit{p},\gamma$) reactions to extract $\gamma$-ray strength information on the nuclei 64,66 Zn. Here, the impact of spin distribution, total $\gamma$-ray decay widths, level densities, and width fluctuations on the application of the ratio method will be discussed. Additionally, by comparing the relative $\gamma$-ray strength at different excitation energies, conclusions on the validity of the generalized Brink-Axel hypothesis can be made. Method: The radiative proton capture reaction measurements have been performed at the HORUS $\gamma$-ray spectrometer of the University of Cologne at one excitation energy for each reaction. Primary $\gamma$-ray intensities have been determined by normalizing secondary $\gamma$-ray transitions in two-step cascades using their absolute branching ratio. The ratio method was applied to the measured primary $\gamma$-ray intensities as well as to previous measurements by Erlandsson et al. at different excitation energies. Results: The relative strength function curve for 64 Zn from our measurement shows no significant deviation from the previous measurement at a different excitation energy. The same is true for 66 Zn where both measurements were at almost the same excitation energy. Absolute $\gamma$-strength function values have been obtained by normalizing the relative curves to quasiparticle random phase approximation calculations because of the absence of experimental data in the respective energy region. Conclusion: The generalized Brink-Axel hypothesis, i.e., the independence of the strength function on the excitation energy, seems to hold in the studied energy region and nuclei. The method to obtain primary $\gamma$-ray intensities from two-step cascade spectra was shown to be a valuable and sensitive tool although its uncertainties are connected to the knowledge of the low-energy level scheme of the investigated nucleus. The scaling in the ratio method should be taken with care, because the relative strength is not a simple sum of $f_{E1}$ and $f_{M1}$ but a somewhat complex linear combination dependent on the excitation energy of the nucleus.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1985

A spectral haze diagnostic feature for normalizing Landsat Thematic Mapper data

The effects of atmospheric haze on Thematic Mapper data, transformed to TM Tasseled Cap features, are illustrated by means of simulation. A spectral feature by which the amount of atmospheric haze may be inferred is derived and described for both simulated and actual TM data. Results presented for two actual TM scenes illustrate the diagnostic feature's sensitivity to changes in haze level as well as its insensitivity to scene-class-related variability. The method by which such a diagnostic feature might be incorporated into a haze normalization procedure is also discussed.

Crist, E. P.↗
NASA NTRS1992

Minimization of deviations of gear real tooth surfaces determined by coordinate measurements

The deviations of a gear's real tooth surface from the theoretical surface are determined by coordinate measurements at the grid of the surface. A method was developed to transform the deviations from Cartesian coordinates to those along the normal at the measurement locations. Equations are derived that relate the first order deviations with the adjustment to the manufacturing machine-tool settings. The deviations of the entire surface are minimized. The minimization is achieved by application of the least-square method for an overdetermined system of linear equations. The proposed method is illustrated with a numerical example for hypoid gear and pinion.

Litvin, F. L.↗
NASA NTRS2005

Formation of Nanophase Iron in Lunar Soil Simulant for Use in ISRU Studies

For the prospective return of humans to the Moon and the extensive amount of premonitory studies necessary, large quantities of lunar soil simulants are required, for a myriad of purposes from construction/engineering purposes all the way to medical testing of its effects from ingestion by humans. And there is only a limited and precious quantity of lunar soil available on Earth (i.e., Apollo soils) - therefore, the immediate need for lunar soil simulants. Since the Apollo era, there have been several simulants; of these JSC-1 (Johnson Space Center) and MLS-1 (Minnesota Lunar Simulant) have been the most widely used. JSC-1 was produced from glassy volcanic tuff in order to approximate lunar soil geotechnical properties; whereas, MLS-1 approximates the chemistry of Apollo 11 high-Ti soil, 10084. Stocks of both simulants are depleted, but JSC-1 has recently gone back into production. The lunar soil simulant workshop, held at Marshall Space Flight Center in January 2005, identified the need to make new simulants for the special properties of lunar soil, such as nanophase iron (np-Fe(sup 0). Hill et al. (2005, this volume) showed the important role of microscale Fe(sup 0) in microwave processing of the lunar soil simulants JSC-1 and MLS-1. Lunar soil is formed by space weathering of lunar rocks (e.g., micrometeorite impact, cosmic particle bombardment). Glass generated during micrometeorite impact cements rock and mineral fragments together to form aggregates called agglutinates, and also produces vapor that is deposited and coats soil grains. Taylor et al. (2001) showed that the relative amount of impact glass in lunar soil increases with decreasing grain size and is the most abundant component in lunar dust (less than 20 micrometer fraction). Notably, the magnetic susceptibility of lunar soil also increases with the decreasing grain size, as a function of the amount of nanophase-sized Fe(sup 0) in impact-melt generated glass. Keller et al. (1997, 1999) also discovered the presence of abundant np-Fe(sup 0) particles in the glass patinas coating most soil particles. Therefore, the correlation of glass content and magnetic susceptibility can be explained by the presence of the np-Feo particles in glass: small particles contain relatively more np-Fe(sup 0) as glass coatings because the surface area versus mass ratio of the grain size is so increased. The magnetic properties of lunar soil are important in dust mitigation on the Moon (Taylor et al. 2005). Thus material simulating this property is important for testing mitigation methods using electromagnetic field. This np- Fe(sup 0) also produces a unique energy coupling to normal microwaves, such as present in kitchen microwave ovens. Effectively, a portion of lunar soil placed in a normal 2.45 GHz oven will melt at greater than 1200 C before your tea will boil at 100 C, a startling and new discovery reported by Taylor and Meek (2004, 2005). Several methods have been investigated in attempts to make nanophase-sized Feo dispersed within silicate glass; like in the lunar glass. We have been successful in synthesizing such a product and continue to improve on our recipe. We have performed extensive experimentation on this subject to date. Ultimately it will probably be necessary to add this np-Fe(sup 0) bearing silicate glass to lunar soil stimulant, like JSC-1, to actually produce the desired magnetic and microwave coupling properties for use in appropriate ISRU experimentation.

Liu, Yang↗
NASA NTRS1975

Further developments in the prediction of oscillatory aerodynamics in mixed transonic flow

This paper presents improvements made to the oscillatory transonic aerodynamic kernel function method given in AIAA Paper No. 74-359. The improved method is fully developed for flows with continuously varying local Mach number and oscillating normal shocks. The boundary conditions developed for oscillating shocks require that a line doublet be added at the foot of the shock which represents the loads induced by shock movement. Numerical results show good agreement with experiment and significant deviation from linear aerodynamic theory. The method is also used to predict wind-tunnel wall interference effects.-

Cunningham, A. M., Jr.↗
NASA NTRS1958

Stability of Cylindrical and Conical Shells of Circular Cross Section, with Simultaneous Action of Axial Compression and External Normal Pressure

We consider in this report the determination of the upper limit of critical loads in the case of simultaneous action of a compressive force, uniformly distributed over plane cross sections, and of isotropic external normal pressure on cylindrical or conical shells of circular cross section. As a starting point we use the differential equations for neutral equilibrium of conical shells which have been used for the solution of the problem of stability of conical shells under torsion and under axial compression; upon solution of the problem it is possible to satisfy all boundary conditions, in contrast to the report where no attention is paid to the fulfillment of the boundary conditions, and to the report where only part of the boundary conditions are satisfied by solution of the problem according to Galerkin's method. Approximate formulas are used for the determination of the critical external normal pressure with simultaneous action of longituninal compression. Let us note that the formulas suggested in reference 5 are not well founded and may lead, in a number of cases, to a substantial mistake in the magnitude of the critical load.

Mushtari, K. M.↗
NASA NTRS1984

Maximum Normalized Rate as a Flying Qualities Parameter

Discrete attitude commands have become a standard task for flying qualities evaluation and control system testing. Much pilot opinion data is now available for ground-based and in-flight simulations, but adequate performance measures and prediction methods have not been established. The Step Target Tracking Prediction method, introduced in 1978, correlated time-on-target and rms tracking data with NT-33 in-flight longitudinal simulations, but did not employ parameters easily measured in manned flight and simulation. Recent application of the Step Target Tracking Prediction method to lateral flying qualities analysis has led to a new measure of performance. This quantity, called Maximum Normalized Rate (MNR), reflects the greatest attitude rate a pilot can employ during a discrete maneuver without excessive overshoot and oscillation. MNR correlates NT-33 lateral pilot opinion ratings well, and is easily measured during flight test or simulation. Futhermore, the Step Target MNR method can be used to analyze large amplitude problems concerning rate limiting and nonlinear aerodynamics.

Onstott, E. D.↗
NASA NTRS1984

Maximum Normalized Rate as a Flying Qualities Parameter

Discrete attitude commands have become a standard task for flying qualities evaluation and control system testing. Much pilot opinion data is now available for ground-based and in-flight simulations, but adequate performance measures and prediction methods have not been established. The Step Target Tracking Prediction method, introduced in 1978, correlated time-on-target and rms tracking data with NT-33 in-flight longitudinal simulations, but did not employ parameters easily measured in manned flight and simulation. Recent application of the Step Target Tracking Prediction method to lateral flying qualities analysis has led to a new measure of performance. This quantity, called Maximum Normalized Rate (MNR), reflects the greatest attitude rate a pilot can employ during a discrete maneuver without excessive overshoot and oscillation. MNR correlates NT-33 lateral pilot opinion ratings well, and is easily measured during night test or simulation. Furthermore, the Step Target MNR method can be used to analyze large amplitude problems concerning rate limiting and nonlinear aerodynamics.

Onstott, E. D.↗
NASA NTRS1978

Pulse-echo probe of rock permeability near oil wells

Processing method involves sequential insonifications of borehole wall at number of different frequencies. Return signals are normalized in amplitude, and root-mean-square (rms) value of each signal is determined. Values can be processed to yield information on size and number density of microfractures at various depths in rock matrix by using averaging methods developed for pulse-echo technique.

Narasimhan, K. Y.↗
DOE OSTI2020

Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning

Accurate prediction of the optimal catalytic temperature ( T opt ) of enzymes is vital in biotechnology, as enzymes with high T opt values are desired for enhanced reaction rates. Recently, a machine learning method (temperature optima for microorganisms and enzymes, TOME) for predicting T opt was developed. TOME was trained on a normally distributed data set with a median T opt of 37 °C and less than 5% of T opt values above 85 °C, limiting the method’s predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the mean squared error on T opt values greater than 85 °C is nearly an order of magnitude higher than the error on values between 30 and 50 °C. Here, we apply ensemble learning and resampling strategies that tackle the data imbalance to significantly decrease the error on high T opt values (>85 °C) by 60% and increase the overall R 2 value from 0.527 to 0.632. The revised method, temperature optima for enzymes with resampling (TOMER), and the resampling strategies applied in this work are freely available to other researchers as Python packages on GitHub.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Elastic Changepoint Detection for Globally-indexed Functional Time Series Data with Climate Applications

Changepoint detection is a vital tool in the application of climate data analysis. Numerous types of climate observation data are most properly represented by functional time series, implying a need for accurate changepoint detection methods applicable to functional time series data. Such data taken at a global scale often contain both spatial heterogeneity and dependence as well as phase (time) misalignment. In this report, we present methods which can detect spatially-dependent changepoints while allowing different estimates of change time and change strength depending on location. Additionally, we provide extensions to this spatially-predicted model which controls for phase variability among observations. Our methods provide the ability to detect a single change, or control for epidemic changes (where a “return-to-normal” change is more likely to be detected than the initial change). We showcase results analyzing the June 1991 eruption of Mt. Pinatubo, where our methods demonstrate the ability to accurately detect both single and epidemic changepoints even in the presence of strong seasonal variability. We find that our spatially-predicted model improves the detection of relevant changepoints versus methods which do not take spatial information into account, and we find that controlling for phase variability helps to control the false discovery rate during the detection process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos With Image Segmentation and Convolutional Neural Network

Resistance spot welding (RSW) is a widely adopted joining technique in automotive industry. Recent advancement in sensing technology makes it possible to collect thermal videos of the weld nugget during RSW using an infrared (IR) camera. The effective and timely analysis of such thermal videos has the potential of enabling in situ nondestructive evaluation (NDE) of the weld nugget by predicting nugget thickness and diameter. Deep learning (DL) has demonstrated to be effective in analyzing imaging data in many applications. However, the thermal videos in RSW present unique data-level challenges that compromise the effectiveness of most pre-trained DL models. We propose a novel image segmentation method for handling the RSW thermal videos to improve the prediction performance of DL models in RSW. The proposed method transforms raw thermal videos into spatial-temporal instances in four steps: video-wise normalization, removal of uninformative images, watershed segmentation, and spatial-temporal instance construction. The extracted spatial-temporal instances serve as the input data for training a DL-based NDE model. The proposed method is able to extract high-quality data with spatial-temporal correlations in the thermal videos, while being robust to the impact of unknown surface emissivity. Overall, our case studies demonstrate that the proposed method achieves better prediction of nugget thickness and diameter than predicting without the transformation.

42 ENGINEERING↗
NASA NTRS2004

Quantitative Rainbow Schlieren Deflectometry as a Temperature Diagnostic for Spherical Flames

Numerical analysis and experimental results are presented to define a method for quantitatively measuring the temperature distribution of a spherical diffusion flame using Rainbow Schlieren Deflectometry in microgravity. First, a numerical analysis is completed to show the method can suitably determine temperature in the presence of spatially varying species composition. Also, a numerical forward-backward inversion calculation is presented to illustrate the types of calculations and deflections to be encountered. Lastly, a normal gravity demonstration of temperature measurement in an axisymmetric laminar, diffusion flame using Rainbow Schlieren deflectometry is presented. The method employed in this paper illustrates the necessary steps for the preliminary design of a Schlieren system. The largest deflections for the normal gravity flame considered in this paper are 7.4 x 10(-4) radians which can be accurately measured with 2 meter focal length collimating and decollimating optics. The experimental uncertainty of deflection is less than 5 x 10(-5) radians.

Feikema, Douglas A.↗
DOE OSTI2024

Defining blood hematology reference values in female pig-tailed macaques ( Macaca nemestrina ) using the Isolation Forest algorithm

Background: Pig-tailed macaques (PTMs) are commonly used as preclinical models to assess antiretroviral drugs for HIV prevention research. Drug toxicities and disease pathologies are often preceded by changes in blood hematology. To better assess the safety profile of pharmaceuticals, we defined normal ranges of hematological values in PTMs using an Isolation Forest (iForest) algorithm. Methods: Eighteen female PTMs were evaluated. Blood was collected 1–24 times per animal for a total of 159 samples. Complete blood counts were performed, and iForest was used to analyze the hematology data to detect outliers. Results: Median, IQR, and ranges were calculated for 13 hematology parameters. From all samples, 22 outliers were detected. These outliers were excluded from the reference index. Conclusions: Using iForest, we defined a normal range for hematology parameters in female PTMs. This reference index can be a valuable tool for future studies evaluating drug toxicities in PTMs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Furthermore, methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗