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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 NTRS1981

Variations of solar irradiance

The active cavity radiometer experiment on the Solar Maximum Mission is providing sensitive measurements of time variations of the total solar irradiance with greater accuracy and precision than previously achieved. The mean 1 AU irradiance for the first 45 days' operation is 1368.64 W/sq m with an absolute uncertainty of less than + or 0.5%. Variations about this mean have been observed on time scales of hours to days with amplitudes up to + or - 0.04%, resolved with a statistical uncertainty as low as 0.001%. Variations within a single orbit with amplitudes as large as + or - 0.5% have been resolved with 0.005% or smaller statistical uncertainty. Although these variations do not display a systematic relationship to conventional solar activity indices over the period, correlative behavior cannot be ruled out on the basis of the present limited data set.

Willson, R. C.↗
NASA NTRS1981

Crop classification using airborne radar and LANDSAT data

Airborne radar data acquired with a 13.3 GHz scatterometer over a test-site near Colby, Kansas were used to investigate the statistical properties of the scattering coefficient of three types of vegetation cover and of bare soil. A statistical model for radar data was developed that incorporates signal-fading and natural within-field variabilities. Estimates of the within-field and between-field coefficients of variation were obtained for each cover-type and compared with similar quantities derived from LANDSAT images of the same fields. The classification accuracy provided by LANDSAT alone, radar alone, and both sensors combined was investigated. The results indicate that the addition of radar to LANDSAT improves the classification accuracy by about 10; percentage-points when the classification is performed on a pixel basis and by about 15 points when performed on a field-average basis.

Ulaby, F. T.↗
NASA NTRS1984

Improved HIRS2/MSU soundings using tropopause information

In the GLAS physical retrieval scheme, all temperature profiles are expressed as an expansion about a global mean using empirical orthogonal functions derived from a sample of radiosondes. Total ozone burden, a parameter highly correlated with tropopause height, should be useful in improving sounding accuracy. The ability to estimate tropopause information from layer mean temperatures suggests incorporating the layer mean temperatures estimated from the sounding itself to give estimates of tropopause pressure and temperature. While information completely independent of sounding and first guess data is preferable, additional information is in fact added to the system by the statistical relationships between tropopause temperature and pressure and layer mean temperature profiles. This should not be confused with use of statistical relationships between temperature profiles and satellite observations, which form the basis of a statistical retrieval system but is in no way used in the physical retrieval scheme.

Susskind, J.↗
NASA NTRS1988

Aircraft/lidar turbulence comparison

Very good agreement between remotely sensed winds using a ground-based Doppler lidar and in situ measurements with an instrumented aircraft is possible. Results show that turbulence intensities computed from time histories measured with the aircraft and time histories of the radial wind measured with lidar can be analyzed statistically to provide turbulence intensities and turbulence spectra which agree well with one another. The results further show that the second moment data, as presently compared with the NASA/MSFC algorithms, do not provide meaningful comparisons with turbulence intensities measured with the aircraft. This disagreement, however, must be investigated further in terms of the accuracy of the second moment data determined by both the lidar hardware and the algorithm for computing the second moment.

Huang, Kao-Huah↗
NASA NTRS1997

Application of Multitemporal Remotely Sensed Soil Moisture for the Estimation of Soil Physical Properties

This paper demonstrates the use of multitemporal soil moisture derived from microwave remote sensing to estimate soil physical properties. The passive microwave ESTAR instrument was employed during June 10-18, 1992, to obtain brightness temperature (TB) and surface soil moisture data in the Little Washita watershed, Oklahoma. Analyses of spatial and temporal variations of TB and soil moisture during the dry-down period revealed a direct relationship between changes in T and soil moisture and soil physical (viz. texture) and hydraulic (viz. saturated hydraulic conductivity, K(sat)) properties. Statistically significant regression relationships were developed for the ratio of percent sand to percent clay (RSC) and K(sat), in terms of change components of TB and surface soil moisture. Validation of results using field measured values and soil texture map indicated that both RSC and K(sat) can be estimated with reasonable accuracy. These findings have potential applications of microwave remote sensing to obtain quick estimates of the spatial distributions of K(sat), over large areas for input parameterization of hydrologic models.

Mattikalli, N. M.↗
NASA NTRS2010

Weather, Climate, and Society: New Demands on Science and Services

A new algorithm has been constructed to estimate the path length of lightning channels for the purpose of improving the model predictions of lightning NOx in both regional air quality and global chemistry/climate models. This algorithm was tested and applied to VHF signals detected by the North Alabama Lightning Mapping Array (NALMA). The accuracy of the algorithm was characterized by comparing algorithm output to the plots of individual discharges whose lengths were computed by hand. Several thousands of lightning flashes within 120 km of the NALMA network centroid were gathered from all four seasons, and were analyzed by the algorithm. The mean, standard deviation, and median statistics were obtained for all the flashes, the ground flashes, and the cloud flashes. Channel length distributions were also obtained for the different seasons.

Source record↗
NASA NTRS2015

Lunar Impact Flash Locations from NASA's Lunar Impact Monitoring Program

Meteoroids are small, natural bodies traveling through space, fragments from comets, asteroids, and impact debris from planets. Unlike the Earth, which has an atmosphere that slows, ablates, and disintegrates most meteoroids before they reach the ground, the Moon has little-to-no atmosphere to prevent meteoroids from impacting the lunar surface. Upon impact, the meteoroid's kinetic energy is partitioned into crater excavation, seismic wave production, and the generation of a debris plume. A flash of light associated with the plume is detectable by instruments on Earth. Following the initial observation of a probable Taurid impact flash on the Moon in November 2005,1 the NASA Meteoroid Environment Office (MEO) began a routine monitoring program to observe the Moon for meteoroid impact flashes in early 2006, resulting in the observation of over 330 impacts to date. The main objective of the MEO is to characterize the meteoroid environment for application to spacecraft engineering and operations. The Lunar Impact Monitoring Program provides information about the meteoroid flux in near-Earth space in a size range-tens of grams to a few kilograms-difficult to measure with statistical significance by other means. A bright impact flash detected by the program in March 2013 brought into focus the importance of determining the impact flash location. Prior to this time, the location was estimated to the nearest half-degree by visually comparing the impact imagery to maps of the Moon. Better accuracy was not needed because meteoroid flux calculations did not require high-accuracy impact locations. But such a bright event was thought to have produced a fresh crater detectable from lunar orbit by the NASA spacecraft Lunar Reconnaissance Orbiter (LRO). The idea of linking the observation of an impact flash with its crater was an appealing one, as it would validate NASA photometric calculations and crater scaling laws developed from hypervelocity gun testing. This idea was dependent upon LRO finding a fresh impact crater associated with one of the impact flashes recorded by Earth-based instruments, either the bright event of March 2013 or any other in the database of impact observations. To find the crater, LRO needed an accurate area to search. This Technical Memorandum (TM) describes the geolocation technique developed to accurately determine the impact flash location, and by association, the location of the crater, thought to lie directly beneath the brightest portion of the flash. The workflow and software tools used to geolocate the impact flashes are described in detail, along with sources of error and uncertainty and a case study applying the workflow to the bright impact flash in March 2013. Following the successful geolocation of the March 2013 flash, the technique was applied to all impact flashes detected by the MEO between November 7, 2005, and January 3, 2014.

Moser, D. E.↗
NASA NTRS1980

Label Identification from Statistical Tabulation (LIST) temporal extendability study

The author has identified the following significant results. The most apparent contributors to the problem of poor temporal extension of LIST are the drastic changes in the brightness keys and an inadequate set of AI responses in Phase 3. The brightness trajectories change drastically from Phase 3 to the transition year (TY). Removing brightness channels from the discriminant does not completely correct the lack of extendability. Removing brightness increases the accuracy of the extension from Phase 3 to TY from 57.7 percent to 64.18 percent. The removal of the Al keys increases accuracy to 65.76 percent. Although the latter increase appears insignificant when compared to the first, the removal of only the Al keys increased accuracy to 63.58 percent. Proper weighting of the responses explains 73.8 percent of the ground truth labels but only 56.7 percent of the Al labels. By contrast, the TY responses which were weighted to explain the TY ground truth labels fared equally well, explaining 73.6 percent of those labels and 87.1 percent of the Al labels.

Dennis, T. B.↗
DOE OSTI2022

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Atmospheric effects on cluster analyses

Ground reflected radiance, from which information is extracted through techniques of cluster analyses for remote sensing application, is altered by the atmosphere when it reaches the satellite. Therefore it is essential to understand the effects of the atmosphere on Landsat measurements, cluster characteristics and analysis accuracy. A doubling model is employed to compute the effective reflectivity, observed from the satellite, as a function of ground reflectivity, solar zenith angle and aerosol optical thickness for standard atmosphere. The relation between the effective reflectivity and ground reflectivity is approximately linear. It is shown that for a horizontally homogeneous atmosphere, the classification statistics from a maximum likelihood classifier remains unchanged under these transforms. If inhomogeneity is present, the divergence between clusters is reduced, and correlation between spectral bands increases. Radiance reflected by the background area surrounding the target may also reach the satellite. The influence of background reflectivity on effective reflectivity is discussed.

Kiang, R. K.↗
NASA NTRS1994

The two-way time synchronization system via a satellite voice channel

A newly developed two-way time synchronization system is described in this paper. The system uses one voice channel at a SCPC satellite digital communication earth station, whose bandwidth is only 45 kHz, thus saving satellite resources greatly. The system is composed of one master station and one or several, up to sixty-two, secondary stations. The master and secondary stations are equipped with the same equipment, including a set of timing equipment, a synthetic data terminal for time synchronizing, and a interface unit between the data terminal and the satellite earth station. The synthetic data terminal for time synchronization also has an IRIG-B code generator and a translator. The data terminal of master station is the key part of whole system. The system synchronization process is full automatic, which is controlled by the master station. Employing an autoscanning technique and conversational mode, the system accomplishes the following tasks: linking up liaison with each secondary station in turn, establishing a coarse time synchronization, calibrating date (years, months, days) and time of day (hours, minutes, seconds), precisely measuring the time difference between local station and the opposite station, exchanging measurement data, statistically processing the data, rejecting error terms, printing the data, calculating the clock difference and correcting the phase, thus realizing real-time synchronization from one point to multiple points. We also designed an adaptive phase circuit to eliminate the phase ambiguity of the PSK demodulator. The experiments have shown that the time synchronization accuracy is better than 2 mu S. The system has been put into regular operation.

Heng-Qiu, Zheng↗
NASA NTRS1999

Implications of MOLA Global Roughness, Statistics, and Topography

New insights are emerging as the ongoing high-quality measurements of the Martian surface topography by Mars Orbiter Laser Altimeter (MOLA) on board the Mars Global Surveyor (MGS) spacecraft increase in coverage, resolution, and diversity. For the first time, a global characterization of the statistical properties of topography is possible. The data were collected during the aerobreaking hiatus, science phasing, and mapping orbits of MGS, and have a resolution of 300-400 m along track, a range resolution of 37.5 cm, a range precision of 1-10 m for surface slopes up to 30 deg., and an absolute accuracy of topography of 13 m. The spacecraft's orbit inclination dictates that nadir observations have latitude coverage of about 87.1S to 87.1N; the addition of observations obtained during a period of off-nadir pointing over the north pole extended coverage to 90N. Additional information is contained in the original extended abstract.

Aharonson, O.↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
NASA NTRS2001

Accuracy Assessment of Response Surface Approximations for Supersonic Turbine Design

There is a growing trend to employ CFD tools to supply the necessary information for design optimization of fluid dynamics components/systems. Such results are prone to uncertainties due to reasons including discretization. errors, incomplete convergence of computational procedures, and errors associated with physical models such as turbulence closures. Based on this type of information, gradient-based optimization algorithms often suffer from the noisy calculations, which can seriously compromise the outcome. Similar problems arise from the experimental measurements. Global optimization techniques, such as those based on the response surface (RS) concept are becoming popular in part because they can overcome some of these barriers. However, there are also fundamental issues related to such global optimization technique such as RS. For example, in high dimensional design spaces, typically only a small number of function evaluations are available due to computational and experimental costs. On the other hand, complex features of the design variables do not allow one to model the global characteristics of the design space with simple quadratic polynomials. Consequently a main challenge is to reduce the size of the region where we fit the RS, or make it more accurate in the regions where the optimum is likely to reside. Response Surface techniques using either polynomials or and Neural Network (NN) methods offer designers alternatives to conduct design optimization. The RS technique employs statistical and numerical techniques to establish the relationship between design variables and objective/constraint functions, typically using polynomials. In this study, we aim at addressing issues related to the following questions: (1) How to identify outliers associated with a given RS representation and improve the RS model via appropriate treatments? (2) How to focus on selected design data so that RS can give better performance in regions critical to design optimization? (3) How to combine NN and polynomial techniques for improving the accuracy of the RS model?

Papila, Nilay↗
DOE OSTI2020

A statistical model to predict ignition probability

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition system requires to build ignition probability maps, which is difficult and costly with either experiment or numerical simulation as both require many tests. Here, this work proposes a model to predict the ignition probability map, knowing only flow statistics in non-reacting conditions, i.e., with only one test. The originality of the model is to construct statistics of the flame kernel trajectory, which are then combined with local flow indicators to evaluate the ignition probability at the considered sparking location. Application to a swirled burner operated in premixed, non-premixed and spray combustion modes illustrates the model concepts and demonstrates its ability to recover the experimental ignition map with good accuracy.

42 ENGINEERING↗
DOE OSTI2020

A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Reliable, non-intrusive, short-term (of up to 12 hours ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. Here, we propose a novel low-complexity non-intrusive approach that improves the predictive accuracy of the state-of-the-art by up to ~10%. The backbone of our approach is a K-nearest neighbours search method, that exploits the demand pattern of the most similar historical days, and incorporates appropriate time-series pre-processing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Geometry-complete perceptron networks for 3D molecular graphs

Abstract Motivation The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional machine learning. Within this spirit, in this work, we introduce GCPNet, a new chirality-aware SE(3)-equivariant graph neural network designed for representation learning of 3D biomolecular graphs. We show that GCPNet, unlike previous representation learning methods for 3D biomolecules, is widely applicable to a variety of invariant or equivariant node-level, edge-level, and graph-level tasks on biomolecular structures while being able to (1) learn important chiral properties of 3D molecules and (2) detect external force fields. Results Across four distinct molecular-geometric tasks, we demonstrate that GCPNet’s predictions (1) for protein–ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5%, greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. Availability and implementation The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1973

Performance of statistical energy analysis

Statistical energy analysis (SEA) methods have been developed for high frequency modal analyses on random vibration environments. These SEA methods are evaluated by comparing analytical predictions to test results. Simple test methods are developed for establishing SEA parameter values. Techniques are presented, based on the comparison of the predictions with test values, for estimating SEA accuracy as a function of frequency for a general structure.

Davis, R. F.↗