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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 775 records · Page 43

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NASA NTRS2016

A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit

Shoulder injury is one of the most severe risks that have the potential to impair crewmembers' performance and health in long duration space flight. Overall, 64% of crewmembers experience shoulder pain after extra-vehicular training in a space suit, and 14% of symptomatic crewmembers require surgical repair (Williams & Johnson, 2003). Suboptimal suit fit, in particular at the shoulder region, has been identified as one of the predominant risk factors. However, traditional suit fit assessments and laser scans represent only a single person's data, and thus may not be generalized across wide variations of body shapes and poses. The aim of this work is to develop a software tool based on a statistical analysis of a large dataset of crewmember body shapes. This tool can accurately predict the skin deformation and shape variations for any body size and shoulder pose for a target population, from which the geometry can be exported and evaluated against suit models in commercial CAD software. A preliminary software tool was developed by statistically analyzing 150 body shapes matched with body dimension ranges specified in the Human-Systems Integration Requirements of NASA ("baseline model"). Further, the baseline model was incorporated with shoulder joint articulation ("articulation model"), using additional subjects scanned in a variety of shoulder poses across a pre-specified range of motion. Scan data was cleaned and aligned using body landmarks. The skin deformation patterns were dimensionally reduced and the co-variation with shoulder angles was analyzed. A software tool is currently in development and will be presented in the final proceeding. This tool would allow suit engineers to parametrically generate body shapes in strategically targeted anthropometry dimensions and shoulder poses. This would also enable virtual fit assessments, with which the contact volume and clearance between the suit and body surface can be predictively quantified at reduced time and cost.

Kim, K. Han↗

NASA NTRS2018

Three Years of Atmospheric Characterization at Ka/Q-band with the NASA/POLIMI Alphasat Receiver in Milan, Italy

Since June of 2014, NASA Glenn Research Center (GRC) and the Politecnico di Milano (POLIMI) have jointly conducted a propagation campaign within the framework of the Alphasat propagation experiment through a propagation terminal at the POLIMI campus in Milan, Italy. The terminal utilizes the 20 GHz and 40 GHz beacons broadcast by the Aldo Paraboni Technology Demonstration Payload (TDP #5), and consists of dual coherent Ka- and Q-band beacon receivers. These provide a direct measurement of the signal attenuation and scintillation and are complemented by concurrent weather instrumentation that provides measurements of the atmospheric conditions at the receiver. The primary goal of these measurements is to improve model predictions of communication system performance at 40 GHz. Over three years of concurrent measurements have now been collected from the terminal, and herein we present a statistical analysis of the results thus far, as well as a summary of recent hardware upgrades to the receivers that were made in September 2017.

Radio Transmission↗

DOE OSTI2024

Elliptically-Contoured Tensor-variate Distributions with Application to Image Learning

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

The ground truth analysis of rain gauge data for the TRMM project

As a part of the Tropical Rainfall Measuring Missioin (TRMM) ground truth program to determine the diurnal variability of the area wide rain rates, the rain rate PDFs, and their effect on the area integral algorithm, rain rate data have been collected from a network of gages located in the area near the Cape Canaveral and Kennedy Space Center, in the period beginning in September 1987. In the preliminary statistical analysis, based on the rain rates derived from the eleven gages, the seasonal diurnal rainfall and network averaged rain rates are determined. The analysis was performed in two steps: determination of the hourly and daily rain accumulations and rain rates; and computation of the fraction of hourly and daily rain rates that exceed a particular threshold, and analysis of the hourly and daily rain rate PDFs for the network. The results indicate that there are diurnal and seasonal variations in the components which determine the network rain rate PDFs.

Kowalewsky, Karen J.↗

NASA NTRS1975

Shortgrass prairie spectral measurements

The spectral methods of vegetation analysis not only measure herbage biomass on a nondestructive basis but also can be adapted to aircraft and satellite devices to map the spatial distribution over an area in an efficient and economical fashion. This study reviews the ground-based in situ field spectrometry in the 0.350-0.800 micron region of the spectrum. A statistical analysis of in situ spectroreflectance data from sample plots of the shortgrass prairie shows that green biomass, chlorophyll concentration, and leaf water content are directly interrelated to that composite property of the plot which is called functioning green biomass. Spectrocorrelation data indicate the spectral regions of optimum sensitivity for a remote estimation of the green biomass, chlorophyll, and leaf water content. The near-infrared region of the spectrum shows a high positive spectrocorrelation to these three sample parameters, regardless of the amount of standing dead vegetation.

Tucker, C. J.↗

NASA NTRS2012

Bayesian Analysis for Risk Assessment of Selected Medical Events in Support of the Integrated Medical Model Effort

The Exploration Medical Capability project is creating a catalog of risk assessments using the Integrated Medical Model (IMM). The IMM is a software-based system intended to assist mission planners in preparing for spaceflight missions by helping them to make informed decisions about medical preparations and supplies needed for combating and treating various medical events using Probabilistic Risk Assessment. The objective is to use statistical analyses to inform the IMM decision tool with estimated probabilities of medical events occurring during an exploration mission. Because data regarding astronaut health are limited, Bayesian statistical analysis is used. Bayesian inference combines prior knowledge, such as data from the general U.S. population, the U.S. Submarine Force, or the analog astronaut population located at the NASA Johnson Space Center, with observed data for the medical condition of interest. The posterior results reflect the best evidence for specific medical events occurring in flight. Bayes theorem provides a formal mechanism for combining available observed data with data from similar studies to support the quantification process. The IMM team performed Bayesian updates on the following medical events: angina, appendicitis, atrial fibrillation, atrial flutter, dental abscess, dental caries, dental periodontal disease, gallstone disease, herpes zoster, renal stones, seizure, and stroke.

Gilkey, Kelly M.↗

NASA NTRS1981

Design and control strategies for CELSS - Integrating mechanistic paradigms and biological complexities

Systems analysis and control theory consideration are given to simulations of both individual components and total systems, in order to develop a reliable control strategy for a Controlled Ecological Life Support System (CELSS) which includes complex biological components. Because of the numerous nonlinearities and tight coupling within the biological component, classical control theory may be inadequate and the statistical analysis of factorial experiments more useful. The range in control characteristics of particular species may simplify the overall task by providing an appropriate balance of stability and controllability to match species function in the overall design. The ultimate goal of this research is the coordination of biological and mechanical subsystems in order to achieve a self-supporting environment.

Moore, B., III↗

NASA NTRS2018

Three Years of Atmospheric Characterization at Ka/Q-band with the NASA/POLIMI Alphasat Receiver in Milan, Italy

Radio Transmission↗

DOE OSTI2021

A hybrid data–model approach to map soil thickness in mountain hillslopes

Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

58 GEOSCIENCES↗

DOE OSTI2026

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures

Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.

Bicontinuous microstructure↗

DOE OSTI2024

Monte–Carlo ray-tracing studies of multiplexed prismatic graphite analyzers for the cold-neutron triple-axis spectrometer at the High Flux Isotope Reactor

A modern cold triple-axis spectrometer to study quantum condensed matter systems is planned for the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. Here, we describe the conceptual principles and design of a secondary spectrometer using a multiplexed, prismatic analyzer system relying on graphite crystals and inspired by the successful implementation of the Continuous Angle Multiple Energy Analysis (CAMEA) spectrometers at the Paul Scherrer Institute. This project is currently known as MANTA for Multi-Analyzer Neutron Triple-Axis. Here, we report Monte-Carlo ray-tracing simulations on a simple but realistic sample scattering kernel to further illustrate the prismatic analyzer concept’s workings, calibration, and performance. Then, we introduce a new statistical analysis approach based on the prismatic analyzer concept to improve the number of final energies measured on the spectrometer. We also study possible evolutions in the CAMEA design relevant for MANTA.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Major to trace element imaging and analysis of iron age glasses using stage scanning in the analytical dual beam microscope (tandem)

Dark and clear silicate glasses formed during an iron age vitrification event ≈ 1500 years ago at the Broborg hillfort near Uppsala, Sweden have been analyzed using a scanning electron microscope equipped with a micro-X-ray fluorescence (μXRF) spectrometer. Correlated µXRF and electron beam-induced energy dispersive spectrometry (EDS) X-ray maps were collected via stage-scanning at constant velocity. This coupled procedure represents a new approach for the cultural heritage community to conduct analytical studies of archaeometric specimens composed of metal, ceramic, or mixed inorganic/organic materials, where major and trace element compositions are registered in space for areas up to the centimeter-length scale at micrometer-scale resolution. Overview images were used to select areas for EDS beam scan maps correlated with multispectral cathodoluminescence (CL) imaging and co-located quantitative EDS and μXRF point analysis. Fe, Ca, Mg, Ti, P, Mn, Zr, Zn, and Y are enriched in the dark glass, while Si, Al, K, Na, Ba, Sr, Rb, and Ga are enriched in the clear glass. Unmelted material is comprised predominately of quartz (SiO 2 ) along with trace apatite (Ca 5 (PO 4 ) 3 [Cl,OH]) and zircon (ZrSiO 4 ). Multivariate statistical analysis was used to measure the area fractions of high variance components while lower variance components represented phase mixtures. Differences between calculated melt viscosities for the glass compositions are consistent with field and laboratory observations. Coupled large area EDS and μXRF imaging shows significant promise for informed selection of higher spatial resolution and higher sensitivity follow-up studies, e.g., those performed using synchrotron analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology

Process mining for conformance analysis focuses on comparing a reference process model against a data-driven process model that is generated via log files from information technology systems. While this approach is helpful when there is an existing process model in an organization, it leaves the question of what to do in the absence of a complete reference process model unanswered. In this paper, we present a comparative assessment approach that combines process mining, process mapping for dimensionality reduction, and statistical analysis. Our goal is to find similarities and dissimilarities in data-driven process models among U.S. Veterans Health Administration (VHA) facilities to assess process conformance among different healthcare facilities, which can help assess the standardization of care. We illustrate our approach by applying it to two clinical radiology order process models generated by two similar facilities. Our results demonstrate statistical similarities in the standardization of care among those two facilities.

Klasky, Hilda↗

NASA NTRS1988

An analysis of the Kalman filter in the Gamma Ray Observatory (GRO) onboard attitude determination subsystem

The Gamma Ray Observatory (GRO) spacecraft needs a highly accurate attitude knowledge to achieve its mission objectives. Utilizing the fixed-head star trackers (FHSTs) for observations and gyroscopes for attitude propagation, the discrete Kalman Filter processes the attitude data to obtain an onboard accuracy of 86 arc seconds (3 sigma). A combination of linear analysis and simulations using the GRO Software Simulator (GROSS) are employed to investigate the Kalman filter for stability and the effects of corrupted observations (misalignment, noise), incomplete dynamic modeling, and nonlinear errors on Kalman filter. In the simulations, on-board attitude is compared with true attitude, the sensitivity of attitude error to model errors is graphed, and a statistical analysis is performed on the residuals of the Kalman Filter. In this paper, the modeling and sensor errors that degrade the Kalman filter solution beyond mission requirements are studied, and methods are offered to identify the source of these errors.

Snow, Frank↗

NASA NTRS2001

The Effects of Long-Term Feeding of Rodent Food Bars on Lipid Peroxidation And Antioxidant Enzyme Levels In Fisher Rats

A specialized rodent food bar diet has been developed and utilized successfully for short-duration shuttle missions. Recent tests conducted in preparation for experiments aboard the International Space Station (ISS) indicated that long-term food bar feeding for three months induced hyperlipidemia in rats. This study examined oxidative stress status in livers of these same animals. Spectrophotometric analysis of 79 Fischer rat livers (40 female and 39 male) for lipid peroxidation (LPO) and superoxide dismutase (SOD) was conducted using Bioxytech LPO-587(TM) assay kit and SOD-525(Tm) assay kit, respectively. The treatment groups consisted of 20 male CHOW and 19 male FOOD BAR rats and 20 female CHOW and 20 female FOOD BAR rats. Statistical analysis to compare differences between groups was performed by standard analysis of variance procedures. The male FOOD BAR group LPO mean (3.6 +/- 0.2 mmol/g) was significantly (p less than or equal to 0.05) greater than that of the male CHOW group (2.1 +/-0.1 mmol/g). Moreover the female FOOD BAR group LPO mean (2.9 +/-0.1 mmol/g) was also significantly greater than the female CHOW group mean (2.2 +/-0.1 mmol/g). The mean values for SOD in both male and female groups showed no significant differences between CHOW and FOOD BAR groups. These results show that LPO levels were significantly higher in both the male and female FOOD BAR groups compared to CHOW groups and that there was no concomitant increase in SOD levels across the group. In addition, males showed a greater difference than females in terms of LPO levels. These findings suggest a need for further investigation into the use of the current food bar formulation for long-term experiments such as those planned for the ISS.

Ramirez, Joel↗

NASA NTRS1998

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Cervix Neoplasms/pathology↗

NASA NTRS1976

Search for correlation between geomagnetic disturbances and mortality

A search is conducted for a possible correlation between solar activity and myocardial infarction and stroke in the United States. A statistical analysis is performed using data on geomagnetic activity and the daily U.S. mortality due to coronary heart disease and stroke for the years 1962 through 1966. None of the results are found to yield any evidence of a correlation. It is concluded that correlations claimed by Soviet workers between geomagnetic activity and the incidence of various human diseases are probably not statistically significant or probably are not due to a causal relation between geomagnetic activity and disease.

Lipa, B. J.↗

DOE OSTI2023

Local Bayesian Dirichlet mixing of imperfect models

Abstract To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.

97 MATHEMATICS AND COMPUTING↗