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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

NASA standard: Trend analysis techniques

Descriptive and analytical techniques for NASA trend analysis applications are presented in this standard. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. This document should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend analysis is neither a precise term nor a circumscribed methodology: it generally connotes quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this document. The basic ideas needed for qualitative and quantitative assessment of trends along with relevant examples are presented.

Source record↗
NASA NTRS1975

A study of sound generation in subsonic rotors, volume 2

Computer programs were developed for use in the analysis of sound generation by subsonic rotors. Program AIRFOIL computes the spectrum of radiated sound from a single airfoil immersed in a laminar flow field. Program ROTOR extends this to a rotating frame, and provides a model for sound generation in subsonic rotors. The program also computes tone sound generation due to steady state forces on the blades. Program TONE uses a moving source analysis to generate a time series for an array of forces moving in a circular path. The resultant time series are than Fourier transformed to render the results in spectral form. Program SDATA is a standard time series analysis package. It reads in two discrete time series and forms auto and cross covariances and normalizes these to form correlations. The program then transforms the covariances to yield auto and cross power spectra by means of a Fourier transformation.

Chalupnik, J. D.↗
NASA NTRS1985

Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer

The development of vector processing computers of the streaming array architectures has made possible a dramatic decrease in the time required for the solution of problems, i.e., for those algorithms which readily lend themselves to sequential operations on long vectors. There has concurrently been rapid growth in the applications of the techniques generally known as mathematical digital filtering, also called signal analysis, digital signal processing, time series analysis, and digital image processing. The best known applications of these techniques is to seismic data and two-dimensional images, data types consisting of very large collections of numbers. A major limitation for these cases is the size and speed of the computer available. Therefore, a move to the streaming array architecture can result in marked improvement in the data analysis techniques which may be employed.

Ioup, G. E.↗
NASA NTRS2002

On the Choice of Variable for Atmospheric Moisture Analysis

The implications of using different control variables for the analysis of moisture observations in a global atmospheric data assimilation system are investigated. A moisture analysis based on either mixing ratio or specific humidity is prone to large extrapolation errors, due to the high variability in space and time of these parameters and to the difficulties in modeling their error covariances. Using the logarithm of specific humidity does not alleviate these problems, and has the further disadvantage that very dry background estimates cannot be effectively corrected by observations. Relative humidity is a better choice from a statistical point of view, because this field is spatially and temporally more coherent and error statistics are therefore easier to obtain. If, however, the analysis is designed to preserve relative humidity in the absence of moisture observations, then the analyzed specific humidity field depends entirely on analyzed temperature changes. If the model has a cool bias in the stratosphere this will lead to an unstable accumulation of excess moisture there. A pseudo-relative humidity can be defined by scaling the mixing ratio by the background saturation mixing ratio. A univariate pseudo-relative humidity analysis will preserve the specific humidity field in the absence of moisture observations. A pseudorelative humidity analysis is shown to be equivalent to a mixing ratio analysis with flow-dependent covariances. In the presence of multivariate (temperature-moisture) observations it produces analyzed relative humidity values that are nearly identical to those produced by a relative humidity analysis. Based on a time series analysis of radiosonde observed-minus-background differences it appears to be more justifiable to neglect specific humidity-temperature correlations (in a univariate pseudo-relative humidity analysis) than to neglect relative humidity-temperature correlations (in a univariate relative humidity analysis). A pseudo-relative humidity analysis is easily implemented in an existing moisture analysis system, by simply scaling observed-minus background moisture residuals prior to solving the analysis equation, and rescaling the analyzed increments afterward.

Dee, Dick P.↗
NASA NTRS2018

Aqua Spring 2018 IAM Series Results

Each Inclination Adjust Maneuver (IAM) series requires a post-series analysis along with a long term prediction. This presentation analyzes Aqua's 2018 IAM series, some of the issues encountered, along with the long-term impact of this series performance. The long-term prediction covers Aqua's ground track and inclination progression until the next IAM series in 2019.

Aqua↗
NASA NTRS2019

Aqua Spring 2019 IAM Series Results

Each Inclination Adjust Maneuver (IAM) series requires a post-series analysis along with a long term prediction. This presentation analyzes Aqua's 2019 IAM series, some of the issues encountered, along with the long-term impact of this series performance. The long-term prediction covers Aqua's ground track and inclination progression until the next IAM series in 2020.

Aqua↗
DOE OSTI2023

Demand Estimation of Net Metered Loads for Microgrid Restoration

Microgrids can experience frequency instability while operating in the islanded mode due to a significant demand generation imbalance. Presence of net metered (NM) loads can further complicate accurate demand forecasting. For these loads only the net demand, difference of load demand and generation, is available. This paper presents the various stages observed during microgrid restoration with high penetration of NM loads. Disaggregation of demand and generation from the net metered time series is shown to be essential for accurate demand forecasting in each of these stages. A disaggregation approach is proposed using correlation in the net metered data. Time series analysis is then used to forecast the demand in each stage of the microgrid restoration process. A case study using demand and generation time series data from residential loads, shows that the proposed approach is significantly more accurate for demand forecasting than using only the net-metered time series.

demand forecasting, Microgrid, power system restor↗
NASA NTRS1985

Interactive Digital Signal Processor

Interactive Digital Signal Processor, IDSP, consists of set of time series analysis "operators" based on various algorithms commonly used for digital signal analysis. Processing of digital signal time series to extract information usually achieved by applications of number of fairly standard operations. IDSP excellent teaching tool for demonstrating application for time series operators to artificially generated signals.

Mish, W. H.↗
DOE OSTI2023

Power Electronics Materials and Bonded Interfaces - Reliability and Lifetime

High temperature operation of wide bandgap devices continue to be a challenge for the power electronics packages. Thermal performance and reliability are important factors that determine the viability of a bonded interface for operation at high temperatures. In this presentation, we present the technical approach and key results from the research on sintered silver, transient liquid phase alloy, and polymeric materials. A lifetime prediction model that incorporates the thermomechanical behavior of sintered silver at 200C was developed. The copper-aluminum transient alloy completed 350 thermal cycles from -40C to 200C and little increase in the defect level was observed. In addition to material research, we initiated a time-series analysis on the scanning acoustic microscope images of eutectic solder to explore statistical forecasting methods and machine learning techniques. Initial results that report the accuracy of a few different statistical models are presented.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2008

Mapping Snow Grain Size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Lyapustin, Alexei↗
DOE OSTI2020

Big Data and AI at DoE's Legacy Sites - 20546

More than 30 years have passed since DOE started the decommissioning of nuclear weapon complexes and the clean-up of soil and groundwater. All the sites have been collecting and archiving soil and groundwater monitoring datasets; particularly contaminant concentration time-series. These datasets provide unparalleled opportunities to understand the system behavior (including more fundamental hydrological and geochemical processes, the response to various perturbations, the long-term trend and environmental decay rate towards the regulatory limit). This understanding is critical for providing multiple lines of evidences that can support site closure. In this study, we explore the machine learning (ML) and artificial intelligence (AI) applications to the long-term soil and groundwater management at DoE's legacy sites. ML can improve our understanding of the subsurface systems, which is critical for long-term monitoring and management of the sites, while AI can automate or support some of decision-making processes (e.g., anomaly detection, monitoring well placements). The particular focuses are to develop general algorithms to: (1) to identify distinct spatiotemporal patterns and to identify several groups that have similar temporal behaviors, using unsupervised clustering methods, (2) identify the different temporal scales of hydrological responses to climate perturbations by time-series analysis, and (3) reduce the number of monitoring wells by identifying the minimum sufficient number of wells to capture the heterogeneity of the groundwater contaminant plume and concentration distribution, using the Gaussian Process model. We demonstrate our methodology at the Savannah River Site F-Area. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2018

Benchmark Comparison of Cloud Analytics Methods Applied to Earth Observations

Earth Observation data are a vital resource for studying long term changes, but the large data volumes can be challenging to analyze. Time series analysis in particular is hampered by the typical thin-time-slice file organization. We examine several potential solutions inspired in large part by the data-parallel methods that have arisen with cloud computing. These solutions include various combinations of data re-organization, spatial indexing, distributed storage and pre-computation that we term "Analytics Optimized Data Stores" (AODS). We find that even simple solutions (such as a data cube) produce more than an order of magnitude improvement; the best provide two to three orders of magnitude improvement. The most performant solutions have tradeoffs in terms of generality or storage footprint, but may nonetheless be useful components in data analytics frameworks where performance is critical.

parallel processing (computers)↗
DOE OSTI2020

Into the UV: A Precise Transmission Spectrum of HAT-P-41b Using Hubble’s WFC3/UVIS G280 Grism

The ultraviolet–visible wavelength range holds critical spectral diagnostics for the chemistry and physics at work in planetary atmospheres. To date, time-series studies of exoplanets to characterize their atmospheres have relied on several combinations of modes on the Hubble Space Telescope’s STIS/COS instruments to access this wavelength regime. Here for the first time, we apply the Hubble WFC3/UVIS G280 grism mode to obtain exoplanet spectroscopy from 200 to 800 nm in a single observation. We test the G280 grism mode on the hot Jupiter HAT-P-41b over two consecutive transits to determine its viability for the characterization of exoplanet atmospheres. We obtain a broadband transit depth precision of 29–33 ppm and a precision of on average 200 ppm in 10 nm spectroscopic bins. Spectral information from the G280 grism can be extracted from both the positive and negative first-order spectra, resulting in a 60% increase in the measurable flux. Additionally, the first Hubble Space Telescope orbit can be fully utilized in the time-series analysis. We present detailed extraction and reduction methods for use by future investigations with this mode, testing multiple techniques. We find the results to be fully consistent with STIS measurements of HAT-P-41b from 310 to 800 nm, with the G280 results representing a more observationally efficient and precise spectrum. HAT-P-41b’s transmission spectrum is best fit with a model with T {sub eq} = 2091 K, high metallicity, and significant scattering and cloud opacity. With these first-of-their-kind observations, we demonstrate that WFC3/UVIS G280 is a powerful new tool to obtain UV–optical spectra of exoplanet atmospheres, adding to the UV legacy of Hubble and complementing future observations with the James Webb Space Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method

The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.

42 ENGINEERING↗
DOE OSTI2023

Estimating the Performance Loss Rate of Photovoltaic Systems Using Time Series Change Point Analysis

The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.

photovoltaics↗
NASA NTRSDate not supplied

System and Safety Analysis with SysAI A Statistical Learning Framework

This is a tutorial on how to use the SYSAI (System Analysis using Statistical AI), a flexible statistical learning framework for the V&V and analysis of complex and high-dimensional Aerospace systems with DNN and AI components. SYSAI provides functionality for a variety of analyses and V&V tasks, including statistical data analysis, high dimensional safety-envelope and time-series analysis, property checking, as well as intelligent test-case generation. The tutorial will demonstrate SYSAI with our industrial partner’s Autonomous Centerline Tracking system, which uses a DNN to enable autonomous aircraft taxiing as an example. Video & Tutorial

Statistical V&V for Complex safety-critical system↗