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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Aerodynamic parameter estimation via Fourier modulating function techniques

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.

Pearson, A. E.↗
NASA NTRS2013

Probabilistic Mass Growth Uncertainties

Mass has been widely used as a variable input parameter for Cost Estimating Relationships (CER) for space systems. As these space systems progress from early concept studies and drawing boards to the launch pad, their masses tend to grow substantially, hence adversely affecting a primary input to most modeling CERs. Modeling and predicting mass uncertainty, based on historical and analogous data, is therefore critical and is an integral part of modeling cost risk. This paper presents the results of a NASA on-going effort to publish mass growth datasheet for adjusting single-point Technical Baseline Estimates (TBE) of masses of space instruments as well as spacecraft, for both earth orbiting and deep space missions at various stages of a project's lifecycle. This paper will also discusses the long term strategy of NASA Headquarters in publishing similar results, using a variety of cost driving metrics, on an annual basis. This paper provides quantitative results that show decreasing mass growth uncertainties as mass estimate maturity increases. This paper's analysis is based on historical data obtained from the NASA Cost Analysis Data Requirements (CADRe) database.

Plumer, Eric↗
DOE OSTI2019

DeepCMB: Lensing Reconstruction of the CMB with Deep Neural Networks

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitati onal lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The GMRT archive atomic gas survey – II. Mass modelling and dark matter halo properties across late-type spirals

Studying the kinematics and mass modelling of galaxies from H i 21 cm data provides valuable insights into the properties of both the baryonic components and the dark matter halo in nearby galaxies. Despite many observational studies, mass modelling of galaxies remains challenging due to different limitations. For example, most of the previous studies involving mass modelling are based on rotation curves derived from 2D velocity fields from H i or H α spectroscopic observation which are often affected by beam smearing and projection effect. However, kinematic modelling done by fitting the ‘Tilted ring model’ to 3D data cube is not affected by these issues. In this study, we present and compare 3D kinematic modelling of a pilot sample of 11 galaxies from the GMRT archive atomic gas survey (GARCIA) using two different publicly available pipelines. We model the observed H i rotation curve using 3.6-μm infrared data and SDSS r-band data for stellar contribution, H i surface density profile for gas, and Navarro–Frenk–White profile for dark matter halo; and employ the Markov chain Monte Carlo optimization method for parameter estimation. Further, to validate our analysis, we revisit important scaling relations, e.g. the M gas –M star relation, M star –M halo relation, M gas –M halo relation and Baryonic Tully–Fisher relation. Here, the scaling relations from our analysis are broadly consistent with that reported in the literature. A larger sample of galaxies from GARCIA in the near future will allow studying these scaling relations in greater details.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models

We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of -0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $\$$653 and $\$$685, respectively, at a willingness-to-pay (WTP) threshold of $\$$66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters’ posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $\$$809 at the same WTP threshold. Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets

The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tropospheric and stratospheric wildfire smoke profiling with lidar: mass, surface area, CCN, and INP retrieval

We present retrievals of tropospheric and stratospheric height profiles of particle mass, volume, surface area, and number concentrations in the case of wildfire smoke layers as well as estimates of smoke-related cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations from backscatter lidar measurements on the ground and in space. Conversion factors used to convert the optical measurements into microphysical properties play a central role in the data analysis, in addition to estimates of the smoke extinction-to-backscatter ratios required to obtain smoke extinction coefficients. The set of needed conversion parameters for wildfire smoke is derived from AERONET observations of major smoke events, e.g., in western Canada in August 2017, California in September 2020, and southeastern Australia in January–February 2020 as well as from AERONET long-term observations of smoke in the Amazon region, southern Africa, and Southeast Asia. The new smoke analysis scheme is applied to CALIPSO observations of tropospheric smoke plumes over the United States in September 2020 and to ground-based lidar observation in Punta Arenas, in southern Chile, in aged Australian smoke layers in the stratosphere in January 2020. These case studies show the potential of spaceborne and ground-based lidars to document large-scale and long-lasting wildfire smoke events in detail and thus to provide valuable information for climate, cloud, and air chemistry modeling efforts performed to investigate the role of wildfire smoke in the atmospheric system.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

A curve-fitting technique for the determination of gain and saturation intensity in homogeneously-broadened gas lasers: Application to a waveguide CO2 laser

A least squares curve-fitting algorithm is derived which allows the simultaneous estimation of the small signal gain and the saturation intensity from an arbitrary number of data points relating power output to the incidence angle of an internal coupling plate. The method is used to study the dependence of the two parameters on tube pressure and discharge current in a waveguide CO2 laser having a 2 mm diameter capillary. It is found that, at pressures greater than 28 torr, rising CO2 temperature degrades the small signal gain at current levels as low as three milliamperes.

Degnan, J. J.↗
NASA NTRS1983

Trajectory-Estimation Error Analysis

Orbital and Geodetic Parameter Estimation Error Analysis Program (ORAN) is Bayesian least-squares simulation program for orbital trajectories. ORAN does not process data; rather, computes accuracy of results of data reduction if measurements are processed by minimum-variance data-reduction program. Actual data used to provide time when given measurement was available and estimated noise on that measurement.

Putney, B.↗
NASA NTRS1985

Solar wind variations and geomagnetic storms - A study of individual storms based on high time resolution ISEE 3 data

Two independent methods are employed to determine the relationship between the parameter epsilon and total energy dissipation rate of the magnetosphere U sub T by selecting disturbed periods from the same data d set used by Baker et al. (1983). Specifically, four storms are examined in detail, since the accuracy of estimating U sub T is significantly improved during disturbed periods. The first method assumes that U sub T = M sub A exp.2- alpha(epsilon) where M sub A is the Alfven Mach number and alpha varies with time. The second method considers a linear, time-invariant dynamic system with epsilon as input and U sub T as output. This means that U sub T = W(asterisk)epsilon where asterisk is the convolution and W is a transfer function characteristic of the system. It is found that alpha values fluctuate mainly between 0 and -0.25. The transfer function analysis indicates that W often resembles a delta-function or a narrow rectangular impulse. Both results give the same implication (namely that U sub T is approximately equal to epsilon) and thus are consistent with the view that the magnetosphere is primarily a directly driven system during disturbed periods.

Akasofu, S.-I.↗
NASA NTRS1990

Image reconstruction of IRAS survey scans

The IRAS survey data can be used successfully to produce images of extended objects. The major difficulties, viz. non-uniform sampling, different response functions for each detector, and varying signal-to-noise levels for each detector for each scan, were resolved. The results of three different image construction techniques are compared: co-addition, constrained least squares, and maximum entropy. The maximum entropy result is superior. An image of the galaxy M51 with an average spatial resolution of 45 arc seconds is presented, using 60 micron survey data. This exceeds the telescope diffraction limit of 1 minute of arc, at this wavelength. Data fusion is a proposed method for combining data from different instruments, with different spacial resolutions, at different wavelengths. Data estimates of the physical parameters, temperature, density and composition, can be made from the data without prior image (re-)construction. An increase in the accuracy of these parameters is expected as the result of this more systematic approach.

Bontekoe, Tj. Romke↗
NASA NTRS1992

Correlated flux densities from VLBI observations with the DSN

Correlated flux densities of extragalactic radio sources in the very long baseline interferometry (VLBI) astrometric catalog are required for the VLBI tracking of Galileo, Mars Observer, and future missions. A system to produce correlated and total flux density catalogs was developed to meet these requirements. A correlated flux density catalog of 274 sources, accurate to about 20 percent, was derived from more than 5000 DSN VLBI observations at 2.3 GHz (S-band) and 8.4 GHz (X-band) using 43 VLBI radio reference frame experiments during the period 1989-1992. Various consistency checks were carried out to ensure the accuracy of the correlated flux densities. All observations were made on the California-Spain and California-Australia DSN baselines using the Mark 3 wideband data acquisition system. A total flux density catalog, accurate to about 20 percent, with data on 150 sources, was also created. Together, these catalogs can be used to predict source strengths to assist in the scheduling of VLBI tracking passes. In addition, for those sources with sufficient observations, a rough estimate of source structure parameters can be made.

Coker, R. F.↗
NASA NTRS1994

A new stochastic systems approach to structural integrity

This paper develops improved stochastic models for the description of a large variety of fatigue crack growth phenomena that occur in components of considerable importance to the functionality and reliability of complex engineering structures. In essence, the models are based on the McGill-Markov and Closure-Lognormal stochastic processes. Not only do these models have the capability of predicting the statistical dispersion of crack growth rates, they also, by incorporating the concept of crack closure, have the capability of transferring stochastic crack growth properties measured under ideal laboratory conditions to situations of industrial significance, such as those occurring under adverse loading and/or environmental conditions. The primary data required in order to be in a position to estimate the pertinent parameters of these stochastic models are obtained from a statistically significant number of replicate tests. In this paper, both the theory and the experimental technique are illustrated using a Ti-6Al-4V alloy. Finally, important structural integrity, reliability, availability and maintainability concepts are developed and illustrated.

Provan, James W.↗
NASA NTRS1995

In-flight demonstration of a Real-Time Flush Airdata Sensing (RT-FADS) system

A prototype real-time flush airdata sensing (RT-FADS) system has been developed and flight tested at the NASA Dryden Flight Research Center. This system uses a matrix of pressure orifices on the vehicle nose to estimate airdata parameters in real time using nonlinear regression. The algorithm is robust to sensor failures and noise in the measured pressures. The RT-FADS system has been calibrated using inertial trajectory measurements that were bootstrapped for atmospheric conditions using meteorological data. Mach numbers as high as 1.6 and angles of attack greater than 45 deg have been tested. The system performance has been evaluated by comparing the RT-FADS to the ship system airdata computer measurements to give a quantitative evaluation relative to an accepted measurement standard. Nominal agreements of approximately 0.003 in Mach number and 0.20 deg in angle of attack and angle of sideslip have been achieved.

Whitmore, Stephen A.↗
NASA NTRS2004

Processing of High Resolution, Multiparametric Radar Data for the Airborne Dual-Frequency Precipitation Radar APR-2

Following the successful Precipitation Radar (PR) of the Tropical Rainfall Measuring Mission, a new airborne, 14/35 GHz rain profiling radar, known as Airborne Precipitation Radar - 2 (APR-2), has been developed as a prototype for an advanced, dual-frequency spaceborne radar for a future spaceborne precipitation measurement mission. . This airborne instrument is capable of making simultaneous measurements of rainfall parameters, including co-pol and cross-pol rain reflectivities and vertical Doppler velocities, at 14 and 35 GHz. furthermore, it also features several advanced technologies for performance improvement, including real-time data processing, low-sidelobe dual-frequency pulse compression, and dual-frequency scanning antenna. Since August 2001, APR-2 has been deployed on the NASA P3 and DC8 aircrafts in four experiments including CAMEX-4 and the Wakasa Bay Experiment. Raw radar data are first processed to obtain reflectivity, LDR (linear depolarization ratio), and Doppler velocity measurements. The dataset is then processed iteratively to accurately estimate the true aircraft navigation parameters and to classify the surface return. These intermediate products are then used to refine reflectivity and LDR calibrations (by analyzing clear air ocean surface returns), and to correct Doppler measurements for the aircraft motion. Finally, the the melting layer of precipitation is detected and its boundaries and characteristics are identifIed at the APR-2 range resolution of 30m. The resulting 3D dataset will be used for validation of other airborne and spaceborne instruments, development of multiparametric rain/snow retrieval algorithms and melting layer characterization and statistics.

airborne radar↗
DOE OSTI2017

Feature Selection for Monitoring Erosive Cavitation on a Hydroturbine

This paper presents a method for comparing and evaluating cavitation detection features - the first step towards estimating remaining useful life (RUL) of hydroturbine runners that are impacted by erosive cavitation. The method can be used to quickly compare features created from cavitation survey data collected on any type of hydroturbine, sensor type, sensor location, and cavitation sensitivity parameter (CSP). Although manual evaluation and knowledge of hydroturbine cavitation is still required for our feature selection method, the use of principal component analysis greatly reduces the number of plots that require evaluation. We present a case study based on a cavitation survey data collected on a Francis hydroturbine located at a hydroelectric plant and demonstrate the selection of the most advantageous sensor type, sensor location, and CSP to use on this hydroturbine for long-term monitoring of erosive cavitation. Our method provides hydroturbine operators and researchers with a clear and effective means to determine preferred sensors, sensor placements, and CSPs while also laying the groundwork for determining RUL in the future.

13 HYDRO ENERGY↗
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.↗