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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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NASA NTRSDate not supplied

ASCoT 3: Nonlinear Principal Components Analysis and Uncertainty Quantification in Early Concept Spacecraft Flight Software Cost Estimation

For mission planners and evaluators alike, value in cost models comes from a mean or median prediction, an understanding of the uncertainty on that prediction, and an understanding of model performance. Here we apply advanced statistical and machine learning methods to spacecraft flight software cost, effort, and SLOC estimation, and present the results in the latest version of the Analogy Software Cost Tool (ASCoT). We present in- and out-of-sample performance metrics for our models, each of which incorporate some amount of epistemic uncertainty. ASCoT, hosted on the One NASA Cost Engineering (ONCE) database via the Online NASA Space Estimation Tool (ONSET), was first showcased in 2016 as a number of analogy-based models and methods (kNN and Clustering) to support early project formulation. This ASCoT update improves upon the previous analogic methods by incorporating uncertainty in the data transformations. In particular, we use a Nonlinear Principal Components Analysis (NLPCA) to deal with ordinal data.

Robotic Spacecraft↗
NASA NTRS2023

Use of Machine Learning and Principal Component Analysis to Retrieve Nitrogen Dioxide (NO 2 ) With Hyperspectral Imagers and Reduce Noise in Spectral Fitting

Nitrogen dioxide (NO 2 ) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO 2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO 2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO 2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO 2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO 2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO 2 retrievals. Nevertheless, the NO 2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO 2 retrievals. Once trained, NNs can also substantially speed up NO 2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

NO2↗
NASA NTRS1987

Effective use of principal component analysis with high resolution remote sensing data to delineate hydrothermal alteration and carbonate rocks

Methods of applying principal component (PC) analysis to high resolution remote sensing imagery were examined. Using Airborne Imaging Spectrometer (AIS) data, PC analysis was found to be useful for removing the effects of albedo and noise and for isolating the significant information on argillic alteration, zeolite, and carbonate minerals. An effective technique for using PC analysis using an input the first 16 AIS bands, 7 intermediate bands, and the last 16 AIS bands from the 32 flat field corrected bands between 2048 and 2337 nm. Most of the significant mineralogical information resided in the second PC. PC color composites and density sliced images provided a good mineralogical separation when applied to a AIS data set. Although computer intensive, the advantage of PC analysis is that it employs algorithms which already exist on most image processing systems.

Feldman, Sandra C.↗
NASA NTRS2012

A Technique Using Principal Component Analysis to Compare Seasonal Cycles of Earth Radiation from CERES and Model Computations

A method for quantitatively comparing the seasonal cycles of two global data sets is presented. The seasonal cycles of absorbed solar radiation (ASR) and outgoing longwave radiation (OLR) have been computed from an eight-year data set from the Clouds and Earth's Radiant Energy System (CERES) scanning radiometers and from a model data set produced by the NASA Goddard Space Flight Center Global Modeling and Assimilation Office. To compare the seasonal cycles from these two data sets, principal component (PC) analysis is used, where the PCs express the time variations and the corresponding empirical orthogonal functions (EOFs) describe the geographic variations. Ocean has a long thermal response time compared to land, so land and ocean are separated for the analysis. The root-mean square values for the seasonal cycles of ASR and OLR are extremely close for the two data sets. The first three PCs are quite close, showing that the time responses and magnitudes over the globe are very similar. The agreement between the two sets of PCs is quantified by computing the matrix of inner products of the two sets. For ASR over land, the first PCs of CERES and the model agree to better than 99.9%. The EOF maps are similar for most of the globe, but differ in a few places, and the agreement of the EOF maps is likewise quantified. Maps of differences between the annual cycles show regions of agreement and disagreement.

Smith, G. Louis↗
DOE OSTI2025

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest

ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1984

Matrix partitioning and EOF/principal component analysis of Antarctic Sea ice brightness temperatures

A field of measured anomalies of some physical variable relative to their time averages, is partitioned in either the space domain or the time domain. Eigenvectors and corresponding principal components of the smaller dimensioned covariance matrices associated with the partitioned data sets are calculated independently, then joined to approximate the eigenstructure of the larger covariance matrix associated with the unpartitioned data set. The accuracy of the approximation (fraction of the total variance in the field) and the magnitudes of the largest eigenvalues from the partitioned covariance matrices together determine the number of local EOF's and principal components to be joined by any particular level. The space-time distribution of Nimbus-5 ESMR sea ice measurement is analyzed.

Murray, C. W., Jr.↗
NASA NTRS2015

A Fully Non-Metallic Gas Turbine Engine Enabled by Additive Manufacturing Part I: System Analysis, Component Identification, Additive Manufacturing, and Testing of Polymer Composites

The research and development activities reported in this publication were carried out under NASA Aeronautics Research Institute (NARI) funded project entitled "A Fully Nonmetallic Gas Turbine Engine Enabled by Additive Manufacturing." The objective of the project was to conduct evaluation of emerging materials and manufacturing technologies that will enable fully nonmetallic gas turbine engines. The results of the activities are described in three part report. The first part of the report contains the data and analysis of engine system trade studies, which were carried out to estimate reduction in engine emissions and fuel burn enabled due to advanced materials and manufacturing processes. A number of key engine components were identified in which advanced materials and additive manufacturing processes would provide the most significant benefits to engine operation. The technical scope of activities included an assessment of the feasibility of using additive manufacturing technologies to fabricate gas turbine engine components from polymer and ceramic matrix composites, which were accomplished by fabricating prototype engine components and testing them in simulated engine operating conditions. The manufacturing process parameters were developed and optimized for polymer and ceramic composites (described in detail in the second and third part of the report). A number of prototype components (inlet guide vane (IGV), acoustic liners, engine access door) were additively manufactured using high temperature polymer materials. Ceramic matrix composite components included turbine nozzle components. In addition, IGVs and acoustic liners were tested in simulated engine conditions in test rigs. The test results are reported and discussed in detail.

acoustic liners↗
NASA NTRS2012

NASA's Carbon Monitoring System Flux-Pilot Project: A Multi-Component Analysis System for Carbon-Cycle Research and Monitoring

The importance of greenhouse gas increases for climate motivates NASA s observing strategy for CO2 from space, including the forthcoming Orbiting Carbon Observatory (OCO-2) mission. Carbon cycle monitoring, including attribution of atmospheric concentrations to regional emissions and uptake, requires a robust modeling and analysis infrastructure to optimally extract information from the observations. NASA's Carbon-Monitoring System Flux-Pilot Project (FPP) is a prototype for such analysis, combining a set of unique tools to facilitate analysis of atmospheric CO2 along with fluxes between the atmosphere and the terrestrial biosphere or ocean. NASA's analysis system is unique, in that it combines information and expertise from the land, oceanic, and atmospheric branches of the carbon cycle and includes some estimates of uncertainty. Numerous existing space-based missions provide information of relevance to the carbon cycle. This study describes the components of the FPP framework, assessing the realism of computed fluxes, thus providing the basis for research and monitoring applications. Fluxes are computed using data-constrained terrestrial biosphere models and physical ocean models, driven by atmospheric observations and assimilating ocean-color information. Use of two estimates provides a measure of uncertainty in the fluxes. Along with inventories of other emissions, these data-derived fluxes are used in transport models to assess their consistency with atmospheric CO2 observations. Closure is achieved by using a four-dimensional data assimilation (inverse) approach that adjusts the terrestrial biosphere fluxes to make them consistent with the atmospheric CO2 observations. Results will be shown, illustrating the year-to-year variations in land biospheric and oceanic fluxes computed in the FPP. The signals of these surface-flux variations on atmospheric CO2 will be isolated using forward modeling tools, which also incorporate estimates of transport error. The results will be discussed in the context of interannual variability of observed atmospheric CO2 distributions.

Pawson, S.↗
NASA NTRS1988

A preliminary component analysis of a Mach-7 hypersonic vehicle

As research continues in the development of an aircraft capable of travelling at hypersonic flight velocities, both the propulsion and thermal management systems stand out as areas requiring innovative technological breakthroughs. For propulsion, the difficulty involves efficiently compressing and combusting hydrogen in a supersonic stream, i.e., developing a viable scramjet with thermal management, the challenge lies in development of materials and active cooling systems capable of handling the enormous heat fluxes associated with hypersonic flight. This paper focuses on these problems and presents component designs for both an active cooling system and an all-external-compression scramjet. These systems are mated to a Mach-6 passenger cruise aircraft whose aerodynamic configuration was derived from an optimization of NASA windtunnel test results. The following outlines the development of the configuration and then focuses in on the design of the two component systems.

Source record↗
DOE OSTI2024

Feedback Controllability Components Analysis (FCCA) v1.0

FCCA is a linear dimensionality reduction method that find subspaces of high-dimensional time-series data that are most feedback controllable. The key innovation is to formulate an objective function that quantifies the joint cost of state reconstruction and state regulation that can be evaluated from purely observational data. To do this, it leverages the duality between controllability and observability. We provide analytic results demonstrating the validity of the cost function. We evaluated this method in both synthetic and real neural data (from multiple organisms and brain areas).

Kumar, Ankit↗
NASA NTRS1981

Some aspects of analytical chemistry as applied to water quality assurance techniques for reclaimed water: The potential use of X-ray fluorescence spectrometry for automated on-line fast real-time simultaneous multi-component analysis of inorganic pollutants in reclaimed water

The potential use of isotopically excited energy dispersive X-ray fluorescence (XRF) spectrometry for automated on line fast real time (5 to 15 minutes) simultaneous multicomponent (up to 20) trace (1 to 10 parts per billion) analysis of inorganic pollutants in reclaimed water was examined. Three anionic elements (chromium 6, arsenic and selenium) were studied. The inherent lack of sensitivity of XRF spectrometry for these elements mandates use of a preconcentration technique and various methods were examined, including: several direct and indirect evaporation methods; ion exchange membranes; selective and nonselective precipitation; and complexation processes. It is shown tha XRF spectrometry itself is well suited for automated on line quality assurance, and can provide a nondestructive (and thus sample storage and repeat analysis capabilities) and particularly convenient analytical method. Further, the use of an isotopically excited energy dispersive unit (50 mCi Cd-109 source) coupled with a suitable preconcentration process can provide sufficient sensitivity to achieve the current mandated minimum levels of detection without the need for high power X-ray generating tubes.

Ling, A. C.↗
NASA NTRS1983

A closed-loop principal component analysis of a tetrahedral truss

A tetrahedral truss, representative of a fairly broad class of large space structures, is considered. A method, measuring the dynamical importance of each elastic mode when the structure is under feedback control, is presented. The main conclusion is that the closed-loop importance of each vibration mode depends on the robustness requirements. Also, there appears to be a conflict between precise control of a given node of the structure and stability margin, these two requirements leading to contradictory classifications of the modes by order of importance.

Jonckheere, E. A.↗
NASA NTRS1993

Passive microwave remote sensing of thin sea ice using principal component analysis

The possibility of developing a satellite-based sea-ice-concentration algorithm which solves for the presence of thinner ice is explored on the basis of time sequences of surface-based measurements of passive MW emission from growing saline ice reported by Wensnahan et al. (1993). It is shown that two classes of thinner ice can be distinguished from mixtures of open water, first-year ice, and multiyear ice. Newly formed ice appears to be optically thick at 37 and 90 GHz and has a relatively dry surface. The thin ice spectrum occurs when the ice is greater than 4 cm thick and appears to result from the accumulation of brine at the ice surface. Thin ice has a relatively stable spectrum characterized by high brightness temperatures, a near-zero spectral gradient at vertical polarization, and a large difference between vertical and horizontal polarizations. It is concluded that thin ice can be detected using satellite data.

Wensnahan, Mark↗
NASA NTRS1998

Principal Component Analysis for Biosignature Detection in Extraterrestrial Samples

Analysis of extraterrestrial samples for organic signatures of past or present life presents several problems. Chief among these is distinguishing bonafide extraterrestrial organic material from terrestrial contamination, either carried on a spacecraft or present in the terrestrial environment to which the sample is exposed. A related problem is separating biologically derived molecules from those produced by abiotic syntheses in the interstellar medium, on meteorite parent bodies, or in planetary atmospheres and oceans.

McDonald, G. D.↗
NASA NTRS2010

Principal Components Analysis of Reflectance Spectra Returned by the Mars Exploration Rover Opportunity

The Mars Exploration Rover Opportunity has spent over six years exploring the Martian surface near its landing site at Meridiani Planum. Meridiani bedrock observed by the rover is largely characterized by sulfate-rich sandstones and hematite spherules, recording evidence of ancient aqueous environments [1]. The region is a deflationary surface, allowing hematite spherules, fragments of bedrock, and "cobbles" of foreign origin to collect loosely on the surface. These cobbles may be meteorites (e.g., Barberton, Heat Shield Rock, Santa Catarina) [2], or rock fragments of exotic composition derived from adjacent terranes or from the subsurface and delivered to Meridiani Planum as impact ejecta [3]. The cobbles provide a way to better understand Martian meteorites and the lithologic diversity of Meridiani Planum by examining the various rock types located there. In the summer of 2007, a global dust storm on Mars effectively disabled Opportunity's Miniature Thermal Emission Spectrometer (Mini-TES), which served as the Athena Science Team s primary tool for remotely identifying rocks of interest on a tactical timescale for efficient rover planning. While efforts are ongoing to recover use of the Mini-TES, the team is currently limited to identifying rocks of interest by visual inspection of images returned from Opportunity's Panoramic Camera (Pancam). This study builds off of previous efforts to characterize cobbles at Meridiani Planum using a database of reflectance spectra extracted from Pancam 13-Filter (13F) images [3]. We analyzed the variability of rock spectra in this database and identified physical characteristics of Martian rocks that could potentially account for the observed variance. By understanding such trends, we may be able to distinguish between rock types at Meridiani Planum and regain the capability to remotely identify locally unique rocks.

Mercer, C. M.↗