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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,495 records · Page 83

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NASA NTRS1977

A hybrid technique for spacecraft attitude interpolation with arbitrary attitude data gaps

An algorithm for obtaining sufficiently accurate estimates of a system parameter Y, given a data record of the parameter in which at arbitrary times no direct observations of Y exist, and describing the variance of these estimates, is proposed. The method provides estimates at all requested times, and the variance of the estimates is maintained as close to the variance of directly measured data as the quality of input information will allow. Three classes of information are used: (1) the measured data record of Y and the statistical extension of this record into time intervals of no direct measurement, obtained by a modification of standard autocorrelation techniques; (2) an a priori indicator of the behavior of Y; and (3) one or more indirect indicators of Y based on observation of other system parameters. The application of the algorithm to the SEASAT-A attitude determination under conditions of sensor field-of-view limitations and sun occultations is presented.

Treder, A. J.↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2019

Residential retrofits at scale: opportunity identification, saving estimation, and personalized messaging based on communicating thermostat data

Basic insulation and heating system retrofits of existing homes could achieve annual energy savings of up to $4–5 billion in the USA. However, current US utility energy efficiency (EE) programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and on-line advertising that lack specificity about particular home retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments that can be inconvenient to homeowners, expensive, and of variable accuracy. Our paper discusses using communicating thermostat data to significantly increase the customer uptake of energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. We extended our previously developed gray-box building model to identify physical building parameters corresponding to the target retrofit opportunities, i.e., whole-home R value, air leakage characteristics, and heating system efficiency. Here, the estimated R values and air leakage characteristics compare favorably with the ground truth. The validated algorithms can calculate home-specific energy savings estimate for each ECM, and the algorithm outputs can be used by utility EE programs to formulate home-specific retrofit offers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1983

A sea surface height estimator using synthetic aperture radar complex imagery

A method is developed for estimating the sea surface height using the complex imagery from synthetic aperture radar (SAR). Algorithms are developed for the envelope and phase demodulation, regression, and filtering of the SAR complex imagery and are applied to simulated and actual satellite radar SEASAT-SAR data. A simulation of a simplified stationary scene is utilized in order to establish tentative sufficient conditions on large-scale SAR and sampling parameters for the accurate estimation of the scale structure's height. It is shown that the algorithm accurately estimated a long wavelength low-amplitude sea height structure present in the SEASAT-SAR data, which was determined to be consistent with observations.

Harger, R. O.↗

NASA NTRS1983

Determination of airplane model structure from flight data using splines and stepwise regression

A procedure for the determination of airplane model structure from flight data is presented. The model is based on a polynomial spline representation of the aerodynamic coefficients, and the procedure is implemented by use of a stepwise regression. First, a form of the aerodynamic force and moment coefficients amenable to the utilization of splines is developed. Next, expressions for the splines in one and two variables are introduced. Then the steps in the determination of an aerodynamic model structure and the estimation of parameters are discussed briefly. The focus is on the application to flight data of the techniques developed.

Klein, V.↗

NASA NTRSDate not supplied

CONUS and Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as wildlands. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have developed a carbon consumption database for the Continental United States (CONUS) and are developing a global carbon consumption database, both of which are directly link to distinct fuels within various fire danger categories. Fuel Characteristic Classification System (FCCS) fuels are used to parameterize biomass, and consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. Residual burned area is defined as burning in areas that have recently burned. We implemented the FCCS 30-meter CONUS fuelbed dataset during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets. The 300-meter global database builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized FCCS biomass parameters. These products can serve as valuable tools when used in conjunction with burned area data to rapidly and accurately estimate carbon consumed and released into the atmosphere.

FCCS↗

NASA NTRS1979

Multistage variable probability forest volume inventory

An inventory scheme based on the use of computer processed LANDSAT MSS data was developed. Output from the inventory scheme provides an estimate of the standing net saw timber volume of a major timber species on a selected forested area of the Navajo Nation. Such estimates are based on the values of parameters currently used for scaled sawlog conversion to mill output. The multistage variable probability sampling appears capable of producing estimates which compare favorably with those produced using conventional techniques. In addition, the reduction in time, manpower, and overall costs lend it to numerous applications.

Anderson, J. E.↗

DOE OSTI2016

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem’s gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally–e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the “state” and “control” in the model refer to its own (internal) and another subsystem’s (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model’s parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems.

Biochemistry & Molecular Biology↗

DOE OSTI2021

On the estimation of boundary layer heights: a machine learning approach

Abstract. The planetary boundary layer height (zi) is a key parameter used in atmospheric models for estimating the exchange of heat, momentum, and moisture between the surface and the free troposphere. Near-surface atmospheric and subsurface properties (such as soil temperature, relative humidity, etc.) are known to have an impact on zi. Nevertheless, precise relationships between these surface properties and zi are less well known and not easily discernible from the multi-year dataset. Machine learning approaches, such as random forest (RF), which use a multi-regression framework, help to decipher some of the physical processes linking surface-based characteristics to zi. In this study, a 4-year dataset from 2016 to 2019 at the Southern Great Plains site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with over 20 different surface meteorological measurements as inputs to a RF model. The model is trained using radiosonde-derived zi values spanning the period from 2016 through 2018 and then evaluated using data from 2019. Results from 2019 showed significantly better agreement with the radiosonde compared to estimates derived from a thresholding technique using Doppler lidars only. Noteworthy improvements in daytime zi estimates were observed using the RF model, with a 50 % improvement in mean absolute error and an R2 of greater than 85 % compared to the Tucker method zi. We also explore the effect of zi uncertainty on convective velocity scaling and present preliminary comparisons between the RF model and zi estimates derived from atmospheric models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Design criteria and eigensequence plots for satellite computed tomography

The use of the degrees of freedom for signal is proposed as a design criteria for comparing different designs for satellite and other measuring systems. It is also proposed that certain eigensequence plots be examined at the design stage along with appropriate estimates of the parameter lambda playing the role of noise to signal ratio. The degrees of freedom for signal and the eigensequence plots may be determined using prior information in the spectral domain which is presently available along with a description of the system, and simulated data for estimating lambda. This work extends the 1972 work of Weinreb and Crosby.

Wahba, G.↗

NASA NTRS1985

Design criteria and eigensequence plots for satellite-computed tomography

Wahba, G.↗

NASA NTRS2020

Optical Navigation for Autonomous Approach of Small Unknown Bodies

State of the practice in navigation around small celestial bodies heavily relies on ground sup- port and human skill, in particular, for perception-based operations such as optical navigation and mapping. This leads to longer duration and more complex mission operations and sub- sequently higher cost. Furthermore, it imposes limitations for certain missions such as fast fly-bys or multi-agent operations. In this work, we present an autonomous navigation strat- egy suitable for approaching small unexplored bodies. During the approach, we estimate the body’s physical properties as well as the spacecraft’s relative trajectory and associated un- certainties. The autonomous navigation strategy, which is solely based on optical measure- ments, begins as soon as the body becomes resolved in the navigation camera and terminates at the start of proximity operations, when the spacecraft makes its first trajectory correction to stay in the vicinity of the body. Our strategy uses multiple image-processing algorithms: light-curve analysis for estimating the target body’s rotation rate, Shape-from-Silhouette for reconstructing the 3D shape and estimating its rotation pole, and feature tracking tailored to Small-Body images for estimating relative navigation parameters. We used the Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) developed by the Jet Propulsion Laboratory to evaluate the feasibility of this multi-phase navigation strategy using simulated images of an approach trajectory. We used the Rosetta mission data to generate photorealistic images to characterise the performance of this approach. This work is based on the assumptions that the spacecraft attitude is known, the body is a principal-axis rotator, a-priori estimates of ephemerides and scale are available, and the body is observed from a zero sun phase only during initial approach. Preliminary results show orbit determination performance that is on par with the human navigation from the Rosetta mission; albeit with a 1% bias in spacecraft-target radial distance estimate. The bias error is likely due to the robustness and accuracy of the visual tracking under dynamic lighting conditions and per- spective changes, which decrease accuracy.

Villa, Jacopo↗

NASA NTRS1976

Bayes estimation on parameters of the single-class classifier

Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.

Lin, G. C.↗

NASA NTRS1999

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Tripp, John S.↗

NASA NTRS1997

Analysis of Particle Image Velocimetry (PIV) Data for Acoustic Velocity Measurements

Acoustic velocity measurements were taken using Particle Image Velocimetry (PIV) in a Normal Incidence Tube configuration at various frequency, phase, and amplitude levels. This report presents the results of the PIV analysis and data reduction portions of the test and details the processing that was done. Estimates of lower measurement sensitivity levels were determined based on PIV image quality, correlation, and noise level parameters used in the test. Comparison of measurements with linear acoustic theory are presented. The onset of nonlinear, harmonic frequency acoustic levels were also studied for various decibel and frequency levels ranging from 90 to 132 dB and 500 to 3000 Hz, respectively.

Blackshire, James L.↗

NASA NTRS2012

Method and apparatus for autonomous, in-receiver prediction of GNSS ephemerides

Methods and apparatus for autonomous in-receiver prediction of orbit and clock states of Global Navigation Satellite Systems (GNSS) are described. Only the GNSS broadcast message is used, without need for periodic externally-communicated information. Earth orientation information is extracted from the GNSS broadcast ephemeris. With the accurate estimation of the Earth orientation parameters it is possible to propagate the best-fit GNSS orbits forward in time in an inertial reference frame. Using the estimated Earth orientation parameters, the predicted orbits are then transformed into Earth-Centered-Earth-Fixed (ECEF) coordinates to be used to assist the GNSS receiver in the acquisition of the signals. GNSS satellite clock states are also extracted from the broadcast ephemeris and a parameterized model of clock behavior is fit to that data. The estimated modeled clocks are then propagated forward in time to enable, together with the predicted orbits, quicker GNSS signal acquisition.

Bar-Sever, Yoaz E.↗

NASA NTRS1991

Global ocean tide mapping using TOPEX/Poseidon altimetry

The investigation's main goals are to produce accurate tidal maps of the main diurnal, semidiurnal, and long-period tidal components in the world's deep oceans. This will be done by the application of statistical estimation techniques to long time series of altimeter data provided by the TOPEX/POSEIDON mission, with additional information provided by satellite tracking data. In the prelaunch phase, we will use in our simulations and preliminary work data supplied by previous oceanographic missions, such as Seasat and Geosat. These results will be of scientific interest in themselves. The investigation will also be concerned with the estimation of new values, and their uncertainties, for tidal currents and for the physical parameters appearing in the Laplace tidal equations, such as bottom friction coefficients and eddy viscosity coefficients. This will be done by incorporating the altimetry-derived charts of vertical tides as boundary conditions in the integration of those equations. The methodology of the tidal representation will include the use of appropriate series expansions such as ocean-basin normal modes and spherical harmonics. The results of the investigation will be space-determined tidal models of coverage and accuracy superior to that of the present numerical models of the ocean tides, with the concomitant benefits to oceanography and associated disciplinary fields.

Sanchez, Braulio V.↗

NASA NTRS2022

L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions, Catchment Land Surface Model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM compared to field data, and forward L-band TB simulations derived from in situ SM and temperature performed better than those derived from CLSM estimates relative to SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multi-angular SMOS TB data and constraints of CLSM SM and temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo and surface roughness resulted in realistic values.

L-Band↗