SEARCH · Search NASA

Results for “component analysis”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

NASA NTRS2004

An Empirical Cumulus Parameterization Scheme for a Global Spectral Model

Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.

Rajendran, K.↗

NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Principal component analysis (PCA) has been the center of many surrogate models used to characterize fluid flows in recent years. However, little work has been done to character- ize the uncertainty in the PCA transform itself and its effect on derived surrogate models. To explore the uncertainty, a typical interpolated surrogate model is constructed for a represen- tative aerodynamic body from computational data. The computational data is then resampled to explore the robustness of the PCA transformation. The variations of the model predictions during this resampling are analyzed to get a measure of confidence in the PCA transformation, which is then applied to the interpolated surrogate model to get uncertainty on integrated force predictions. An initial test case has been explored with promising results.

Principal Component Analysis↗

NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Computational Fluid Dynamics↗

NASA NTRS2023

Coronado Ecological Conservation: Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails

Species monitoring is essential for mitigating the impacts of plant invasion, such as radical changes in an area’s ecosystem, degraded soil health, increased wildfire severity, landslides, and increased flooding. For this project, NASA DEVELOP partnered with the National Park Service (NPS) to investigate invasive species in disturbed lands: specifically, areas affected by off-trail travel and U.S.-Mexico border construction activities. The team assessed how construction has impacted the distribution of Lehmann’s lovegrass and Russian thistle invasives throughout Coronado National Memorial, AZ from 1986-2022. Using data from Landsat 5 and 8, Sentinel-2, NAIP, and PlanetScope, the team computed NDVI, NDMI, MSAVI2, EVI, and Tasseled Cap Wetness, Brightness, and Greenness transformations as vegetation health indicators to input into various machine learning algorithms. To minimize noise, the team conducted Principal Component Analysis on vegetation indices and spectral bands before running k-means clustering and random forest classification algorithms. Between all datasets, the team found that the median area fully overtaken by invasive plants was 5.37% of the park’s total area in 2022. The NPS will use end products to help increase restoration efforts in disturbed areas with high concentrations of invasive plants, and this project can serve as a jumping off point for future invasive species monitoring. The NPS’s collection of ground data for 2022-2023, in conjunction with future data collection, will notably improve the accuracy of classification models, leading to more precise monitoring of invasive species spread over time.

Coronado National Memorial↗

NASA NTRS2002

Continuum Fitting HST QSO Spectra

The Principal Component Analysis (PCA) method which we are using to fit and describe QSO spectra relies upon the fact that QSO continuum are generally very smooth and simple except for emission and absorption lines. To see this we need high signal-to-noise (S/N) spectra of QSOs at low redshift which have relatively few absorption lines in the Lyman-a forest. We need a large number of such spectra to use as the basis set for the PCA analysis which will find the set of principal component spectra which describe the QSO family as a whole. We have found that too few HST spectra have the required S/N and hence we need to supplement them with ground based spectra of QSOs at higher redshift. We have many such spectra and we have been working to make them suitable for this analysis. We have concentrated on this topic since 12/15/01.

Tytler, David↗

NASA NTRS2021

Moisture and Temperature Influences on Nonlinear Vegetation Trends in Serengeti National Park

While long-term vegetation greening trends have appeared across large land areas over the late 20th century, uncertainty remains in identifying and attributing finer-scale vegetation changes and trends, particularly across protected areas. Serengeti National Park (SNP) is a critical East African protected area, where seasonal vegetation cycles support vast populations of grazing herbivores and a host of ecosystem dynamics. Previous work has shown how non-climate drivers (e.g. land use) shape the SNP ecosystem, but it is still unclear to what extent changing climate conditions influence SNP vegetation, particularly at finer spatial and temporal scales. We fill this research gap by evaluating long-term (1982–2016) changes in SNP leaf area index (LAI) in relation to both temperature and moisture availability using Ensemble Empirical Mode Decomposition and Principal Component Analysis with regression techniques. We find that SNP LAI trends are nonlinear, display high sub-seasonal variation, and are influenced by lagged changes in both moisture and temperature variables and their interactions. LAI during the long rains (e.g. March) exhibits a greening-to-browning trend reversal starting in the early 2000s, partly due to antecedent precipitation declines. In contrast, LAI during the short rains (e.g. November, December) displays browning-to-greening alongside increasing moisture availability. Rising temperature trends also have important, secondary interactions with moisture variables to shape these SNP vegetation trends. Our findings show complex vegetation-climate interactions occurring at important temporal and spatial scales of the SNP, and our rigorous statistical approaches detect these complex climate-vegetation trends and interactions, while guarding against spurious vegetation signals.

Moisture↗

NASA NTRS1997

RL10A-3-3A Rocket Engine Modeling Project

Two RL10A-3-3A rocket engines comprise the main propulsion system for the Centaur upper stage vehicle. Centaur is used with bod Titan and Atlas launch vehicles, carrying military and civilian payloads from high altitudes into orbit and beyond. The RL10 has delivered highly reliable service for the past 30 years. Recently, however, there have been two in-flight failures which have refocused attention on the RL10. This heightened interest has sparked a desire for an independent RL10 modeling capability within NASA and th Air Force. Pratt & Whitney, which presently has the most detailed model of the RL10, also sees merit in having an independent model which could be used as a cross-check with their own simulations. The Space Propulsion Technology Division (SPTD) at the NASA Lewis Research Center has developed a computer model of the RL10A-3-3A. A project team was formed, consisting of experts in the areas of turbomachinery, combustion, and heat transfer. The overall goal of the project was to provide a model of the entire RL10 rocket engine for government use. In the course of the project, the major engine components have been modeled using a combination of simple correlations and detailed component analysis tools (computer codes). The results of these component analyses were verified with data provided by Pratt & Whitney. Select modeling results and test data curves were then integrated to form the RL10 engine system model The purpose of this report is to introduce the reader to the RL10 rocket engine and to describe the engine system model. The RL10 engine and its application to U.S. launch vehicles are described first, followed by a summary of the SPTD project organization, goals, and accomplishments. Simulated output from the system model are shown in comparison with test and flight data for start transient, steady state, and shut-down transient operations. Detailed descriptions of all component analyses, including those not selected for integration with the system model, are included as appendices.

Binder, Michael↗

NASA NTRS1996

NASA Research Announcement Phase 1 Report and Phase 2 Proposal for the Development of a Power Assisted Space Suit Glove Assembly

In July of 1996, ILC Dover was awarded Phase 1 of a contract for NASA to develop a prototype Power Assisted Space Suit glove to enhance the performance of astronauts during Extra-Vehicular Activity (EVA). This report summarizes the work performed to date on Phase 1, and details the work to be conducted on Phase 2 of the program. Phase 1 of the program consisted of research and review of related technical sources, concept brainstorming, baseline design development, modeling and analysis, component mock-up testing, and test data analysis. ILC worked in conjunction with the University of Maryland's Space Systems Laboratory (SSL) to develop the power assisted glove. Phase 2 activities will focus on the design maturation and the manufacture of a working prototype system. The prototype will be tested and evaluated in conjunction with existing space suit glove technology to determine the performance enhancement anticipated with the implementation of the power assisted joint technology in space suit gloves.

Cadogan, Dave↗

NASA NTRS2020

On Development of Modern Software Interface to Glenn Research Center’s Communication Analysis Suite

NASA’s Space Communications and Navigation (SCaN) program analyzes space communication channels involving satellites in any Earth orbit and deep space for a multitude of operations. Specifically, the SCaN program will power the future of lunar communications in the next decade and beyond. A web based user interface of the static link analysis component of the Glenn Research Center’s Communication Analysis Suite (GCAS) was developed. It is operated through a newly designed front-end application with an easy to use and intuitive web-based user interface that provides accurate satellite communications link analysis capabilities.

link analysis↗

NASA NTRS2010

Extracting Independent Local Oscillatory Geophysical Signals by Geodetic Tropospheric Delay

Zenith Tropospheric Delay (ZTD) due to water vapor derived from space geodetic techniques and numerical weather prediction simulated-reanalysis data exhibits non-linear and non-stationary properties akin to those in the crucial geophysical signals of interest to the research community. These time series, once decomposed into additive (and stochastic) components, have information about the long term global change (the trend) and other interpretable (quasi-) periodic components such as seasonal cycles and noise. Such stochastic component(s) could be a function that exhibits at most one extremum within a data span or a monotonic function within a certain temporal span. In this contribution, we examine the use of the combined Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA): the EEMD-ICA algorithm to extract the independent local oscillatory stochastic components in the tropospheric delay derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) over six geodetic sites (HartRAO, Hobart26, Wettzell, Gilcreek, Westford, and Tsukub32). The proposed methodology allows independent geophysical processes to be extracted and assessed. Analysis of the quality index of the Independent Components (ICs) derived for each cluster of local oscillatory components (also called the Intrinsic Mode Functions (IMFs)) for all the geodetic stations considered in the study demonstrate that they are strongly site dependent. Such strong dependency seems to suggest that the localized geophysical signals embedded in the ZTD over the geodetic sites are not correlated. Further, from the viewpoint of non-linear dynamical systems, four geophysical signals the Quasi-Biennial Oscillation (QBO) index derived from the NCEP/NCAR reanalysis, the Southern Oscillation Index (SOI) anomaly from NCEP, the SIDC monthly Sun Spot Number (SSN), and the Length of Day (LoD) are linked to the extracted signal components from ZTD. Results from the synchronization analysis show that ZTD and the geophysical signals exhibit (albeit subtle) site dependent phase synchronization index.

Botai, O. J.↗

NASA NTRS1988

Probabilistic structural analysis of aerospace components using NESSUS

Probabilistic structural analysis of a Space Shuttle main engine turbopump blade is conducted using the computer code NESSUS (numerical evaluation of stochastic structures under stress). The goal of the analysis is to derive probabilistic characteristics of blade response given probabilistic descriptions of uncertainties in blade geometry, material properties, and temperature and pressure distributions. Probability densities are derived for critical blade responses. Risk assessment and failure life analysis is conducted assuming different failure models.

Shiao, Michael C.↗

NASA NTRS1988

Probabilistic structural analysis of aerospace components using NESSUS

Shiao, Michael C.↗

NASA NTRS1983

Progress toward the analysis of supersonic inlet flows

A zonal flow analysis procedure was developed to predict the flow through the supersonic diffuser of an axisymmetric mixed compression inlet at angle-of-attack. In this analysis, the inlet flow is divided into three types of regions, each with different dominant flow phenomena. These are the inviscid supersonic core, boundary layer, and shock/boundary layer interaction flows. An appropriate analysis was selected or developed for the three-dimensional flow in each type of region. Procedures were developed to interface these analyses for the overall inlet flow analysis. This analysis was applied to an inlet operating at M = 2.58 at several angle-of-attack conditions. Comparisons are presented between computed and measured flow properties for the inlet and for the component analysis flows. Extensions of the present procedure to include the terminal shock and subsonic diffuser flows are recommended. Desirable experiments for evaluation of the inlet analysis procedure or the component analyses and to support improved modeling or extension of the inlet analysis are defined and recommended.

Paynter, G. C.↗

NASA NTRS1972

Design of microstrip components by computer

Development of computer programs for component analysis and design aids used in production of microstrip components is discussed. System includes designs for couplers, filters, circulators, transformers, power splitters, diode switches, and attenuators.

Cisco, T. C.↗

NASA NTRS1990

An unmanned probe to Pluto

Now that Voyager II has completed its grand tour of the solar system, all the planets in the solar system, with the exception of Pluto, have been studied. Even now, missions to return to Mercury, Venus, Mars Jupiter, and Saturn are currently flying or are planned. However, a mission to explore Pluto is not, at the present time, being considered seriously. The design problem presented to the students was very general, i.e., design an unmanned mission to Pluto with a launch window constraint of the years 2000 to 2010. All other characteristics of the mission, such as mission type (flyby, orbiter, lander, penetrator), scientific objectives and payload, and the propulsion system were to be determined by the design teams. The design studies exposed several general problems to be solved. Due to the extreme distance to Pluto (and a corresponding travel time in the range of 10 to 25 years), the spacecraft had to be lighter and more robust than current spacecraft designs. In addition, advanced propulsion concepts had to be considered. These included the new generation of launch vehicles and upper stages and nuclear electric propulsion. The probe design offered an abundance of synthesis and analysis problems. These included sizing trade studies, selection of subsystem components, analysis of spacecraft dynamics, stability and control, structural design and material selection, trajectory design, and selection of scientific equipment. Since the characteristics of the mission, excluding the launch window, were to be determined by the design teams, the solutions varied widely.

Source record↗

NASA NTRS2003

Electromagnetic Interactions GenERalized (EIGER): Applications at Johnson Space Center

This slide presentation reviews the software framework for the analysis and design of complex electromagnetic systems. The system called Electromagnetic Interactions GenERalized (EIGER) is a hybrid finite element solution for wave equations and deru=ives boundary element solutions for integral equation formulations. Written in Fortran 90 Eiger uses object-oriented design methods to abstract key analysis components. EIGER has been applied to higher order modeling for analysis of antennas to assist in the design of the antennas of a mini-AERCam. EIGER might also be used to model the Ion Cyclotron Resonant heating stage for the VAriable Specific Impulse Magnetoplasma Rocket (VASIMR).

Dobbins, Justin A.↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗