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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

An observing system simulation experiment for the Laser Atmospheric Wind Sounder (LAWS)

The present observing system-simulation experiments evaluate the potential of a Laser Atmospheric Wind Sounder (LAWS) instrument for 5-day forecasting, using a primitive-equation multilevel spectral global circulation model. A 55-deg-inclined and a 98-deg sun-synchronous orbit are examined, by adding LAWS wind profiles into a global 4D data-assimilation system, and comparing both the analyses and forecasts to a control experiment. The 4D data-assimilation system consists of a multivariate optimum interpolation analysis and a nonlinear, normal-mode intialization, using the aforementioned global circulation model.

Rohaly, G. D.↗
NASA NTRS1994

Acoustic method of damage sensing in composite materials

The use of acoustic emission and acousto-ultrasonics to characterize impact damage in composite structures is being performed on both graphite epoxy and kevlar bottles. Further development of the acoustic emission methodology to include neural net analysis and/or other multivariate techniques will enhance the capability of the technique to identify failure mechanisms during fracture. The acousto-ultrasonics technique will be investigated to determine its ability to predict regions prone to failure prior to the burst tests. The combination of the two methods will allow for simple nondestructive tests to be capable of predicting the performance of a composite structure prior to being placed in service and during service.

Workman, Gary L.↗
NASA NTRS1996

An acoustic emission and acousto-ultrasonic analysis of impact damaged composite pressure vessels

The use of acoustic emission to characterize impact damage in composite structures is being performed on composite bottles wrapped with graphite epoxy and kevlar bottles. Further development of the acoustic emission methodology will include neural net analysis and/or other multivariate techniques to enhance the capability of the technique to identify dominant failure mechanisms during fracture. The acousto-ultrasonics technique will also continue to be investigated to determine its ability to predict regions prone to failure prior to the burst tests. Characterization of the stress wave factor before, and after impact damage will be useful for inspection purposes in manufacturing processes. The combination of the two methods will also allow for simple nondestructive tests capable of predicting the performance of a composite structure prior to its being placed in service and during service.

Workman, Gary L.↗
NASA NTRS1997

Mixed H2/H-Infinity Control of a Flexible Space Structure

As theory progresses for design and analysis of robust multivariable control laws, synthesis procedures and to a larger extent, experimental verification generally lags behind. Recent developments in robust control theory have extended the H-infinity and mu-synthesis methods to incorporate H2 properties in the control synthesis. A major difficulty in implementing robust controllers is the order of the compensator and associated complexity of the computation required for synthesis, especially when order constraints are imposed. This paper presents results of system identification and robust control design for the Controls/Structures Interaction Ground Test Facility at NASA/Marshall Space Flight Center.

Whorton, Mark↗
NASA NTRS2003

Usefulness of microvolt T-wave alternans for prediction of ventricular tachyarrhythmic events in patients with dilated cardiomyopathy: results from a prospective observational study

OBJECTIVES: This study was designed to evaluate the ability of microvolt-level T-wave alternans (MTWA) to identify prospectively patients with idiopathic dilated cardiomyopathy (DCM) at risk of ventricular tachyarrhythmic events and to compare its predictive accuracy with that of conventional risk stratifiers. BACKGROUND: Patients with DCM are at increased risk of sudden death from ventricular tachyarrhythmias. At present, there are no established methods of assessing this risk. METHODS: A total of 137 patients with DCM underwent risk stratification through assessment of MTWA, left ventricular ejection fraction, baroreflex sensitivity (BRS), heart rate variability, presence of nonsustained ventricular tachycardia (VT), signal-averaged electrocardiogram, and presence of intraventricular conduction defect. The study end point was either sudden death, resuscitated ventricular fibrillation, or documented hemodynamically unstable VT. RESULTS: During an average follow-up of 14 +/- 6 months, MTWA and BRS were significant univariate predictors of ventricular tachyarrhythmic events (p < 0.035 and p < 0.015, respectively). Multivariate Cox regression analysis revealed that only MTWA was a significant predictor. CONCLUSIONS: Microvolt-level T-wave alternans is a powerful independent predictor of ventricular tachyarrhythmic events in patients with DCM.

Evaluation Studies↗
NASA NTRS2022

Warming, Increase in Precipitation, and Irrigation Enhance Greening in High Mountain Asia

High-Mountain Asia (HMA) exhibits one of the highest increases in vegetation greenness on Earth, subsequently influencing the exchange of water and energy between the land surface and the atmosphere. Given the strong interactions between the hydrosphere, the biosphere, and the cryosphere, understanding the drivers of greening in this highly complex region with significant land cover heterogeneity is essential to assess the changes in the regional water budget. Here we perform a holistic multivariate remote sensing analysis to simultaneously examine the primary components of the terrestrial water cycle from 2003 to 2020 and decipher the principal drivers of greening in HMA. We identified three drivers of greening: (1) precipitation drives greening in mid and low elevation areas covered by evergreen and mixed forests (e.g., Irrawaddy basin), (2) decreases in snow enhance greening in most of the hydrologic basins, and (3) irrigation induces greening in irrigated lands (Ganges-Brahmaputra and Indus).

Fadji Zaouna Maina↗
NASA NTRS1991

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.

NASA Program CELSS↗
DOE OSTI2024

Application of Partial Least Squares Approaches to Pyroprocessing ER Data

Multivariate approaches show promise for application to process monitoring for safeguards of pyroprocessing. Past MPACT work explored the application of Principal Component Analysis (PCA) to detect off-normal conditions in pyroprocessing electrorefiner (ER) data from in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL) known as the Scalable Pyrochemical Recycling testbed (SPyRe) ER. PCA, however, does not consider the output variables. In FY24, multivariate analysis was extended from PCA to Partial Least Squares (PLS) analysis. PLS maximizes the variance between both the input signals and output variables. In the case of this work, PLS was applied in two different manners: Predictive PLS and Discriminant PLS. Predictive PLS maximizes the covariance between the process variables of the ER and the measured U concentration from in-situ voltammetry. Discriminant PLS maximizes the covariance between the process variables and a set of training process “states” such as known off-normal conditions. By projecting into the latent variable space in PLS, the process variables can be regressed onto the outputs and predictions can be made for new data sets. In this work, by applying predictive PLS, a penalized non-linear PLS approach was able to make predictions of concentration based on test and training data and detect when operations were off-normal. However, the predictive PLS does not classify the signals to which off-normal operations are attributable. Discriminant PLS can be used to classify off-normal operations but is inadequate to properly classify specific off-normal classes like power supply faults when the Discriminant PLS model is only specifically trained to detect that off-normal class. When all faults are trained against the observation data, all three operational classes are accurately classified and distinguished. Thus, future application of latent variable techniques should not select any given method, but should use a mixture of PCA, Predictive PLS, and Discriminant PLS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1989

New multivariable capabilities of the INCA program

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

Bauer, Frank H.↗
NASA NTRS1978

A sensitivity analysis for the F100 turbofan engine using the multivariable Nyquist array

In the feedback control design of multivariable systems, closed loop performance evaluations must include the dynamic behavior of variables unavailable to the feedback controller. For the multivariable Nyquist array method, a set of sensitivity functions are proposed to simplify the adjustment of compensator parameters when the dynamic response of the unmeasurable output variables is unacceptable. A sensitivity study to improve thrust and turbine temperature responses for the Pratt-Whitney F100 turbofan engine demonstrates the utility of the proposed method.

Leininger, G. G.↗
NASA NTRS1980

Interfaces between statistical analysis packages and the ESRI geographic information system

Interfaces between ESRI's geographic information system (GIS) data files and real valued data files written to facilitate statistical analysis and display of spatially referenced multivariable data are described. An example of data analysis which utilized the GIS and the statistical analysis system is presented to illustrate the utility of combining the analytic capability of a statistical package with the data management and display features of the GIS.

Masuoka, E.↗
DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗
DOE OSTI2021

Identification of Metal Stresses in Arabidopsis thaliana Using Hyperspectral Reflectance Imaging

Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana : salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.

multivariate curve resolution↗
DOE OSTI2025

Chemometrics and visible diffuse reflectance spectroscopy to classify plutonium dioxide

Diffuse reflectance (DR) spectra in the Vis-NIR (∼380–1050 nm) region were acquired for a series of PuO 2 samples with a spot size of about 10 × 10 μm. Two batches of six PuO 2 samples, synthesized approximately 7.5 months apart, were prepared using both Pu(III) and Pu(IV) oxalate precursors at three distinct calcination temperatures (450, 650, and 950 °C). This yielded a total of 12 PuO 2 samples and 433 DR spectra. The DR spectrum of PuO 2 contained numerous peaks in the visible region, and characteristic features were identified with respect to calcination temperature and chemistry. A distinct peak multiplet near 615 nm was observed for samples prepared at low calcination temperatures, and a peak near 660 nm was observed for higher calcination temperatures. A multivariate classification strategy based on principal component analysis (PCA) was developed to distinguish PuO 2 calcination temperatures of 450, 650, and 950 °C with 100 % accuracy. Classification results also indicate the potential to distinguish chemical processing history (i.e., Pu(III) or Pu(IV)) based on the spectra with 72 % accuracy based on k-nearest neighbors applied to the PCA scores. Partial least squares discriminant analysis was used to identify variation among batches with 88 % accuracy and found that peaks near 669, 681, 811, and 970 nm were the most useful for predicting the batch identity. Here, this work demonstrates how micro-diffuse reflectance spectroscopy and chemometrics can be used to classify PuO 2 processing history based on Vis-NIR spectral features. Combining the chemometric approach with mapping sequences could provide a rapid, nondestructive approach to classify Pu oxide materials for environmental, forensics, and nonproliferation applications.

Actinide↗
NASA NTRS1984

Multivariate optimum interpolation of surface pressure and winds over oceans

The observations of surface pressure are quite sparse over oceanic areas. An effort to improve the analysis of surface pressure over oceans through the development of a multivariate surface analysis scheme which makes use of surface pressure and wind data is discussed. Although the present research used ship winds, future versions of this analysis scheme could utilize winds from additional sources, such as satellite scatterometer data.

Bloom, S. C.↗
DOE OSTI2021

The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

Lian, Zhichuang↗