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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Flight-path estimation in passive low-altitude flight by visual cues

A series of experiments was conducted, in which subjects had to estimate the flight path while passively being flown in straight or in curved motion over several types of nominally flat, textured terrain. Three computer-generated terrain types were investigated: (1) a random 'pole' field, (2) a flat field consisting of random rectangular patches, and (3) a field of random parallelepipeds. Experimental parameters were the velocity-to-height (V/h) ratio, the viewing distance, and the terrain type. Furthermore, the effect of obscuring parts of the visual field was investigated. Assumptions were made about the basic visual-field information by analyzing the pattern of line-of-sight (LOS) rate vectors in the visual field. The experimental results support these assumptions and show that, for both a straight as well as a curved flight path, the estimation accuracy and estimation times improve with the V/h ratio. Error scores for the curved flight path are found to be about 3 deg in visual angle higher than for the straight flight path, and the sensitivity to the V/h ratio is found to be considerably larger. For the straight motion, the flight path could be estimated successfully from local areas in the far field. Curved flight-path estimates have to rely on the entire LOS rate pattern.

Grunwald, Arthur J.↗
DOE OSTI2016

TU-H-207A-08: Estimating Radiation Dose From Low-Dose Lung Cancer Screening CT Exams Using Tube Current Modulation

Purpose: The purpose of this work is to estimate effective and lung doses from a low-dose lung cancer screening CT protocol using Tube Current Modulation (TCM) across patient models of different sizes. Methods: Monte Carlo simulation methods were used to estimate effective and lung doses from a low-dose lung cancer screening protocol for a 64-slice CT (Sensation 64, Siemens Healthcare) that used TCM. Scanning parameters were from the AAPM protocols. Ten GSF voxelized patient models were used and had all radiosensitive organs identified to facilitate estimating both organ and effective doses. Predicted TCM schemes for each patient model were generated using a validated method wherein tissue attenuation characteristics and scanner limitations were used to determine the TCM output as a function of table position and source angle. The water equivalent diameter (WED) was determined by estimating the attenuation at the center of the scan volume for each patient model. Monte Carlo simulations were performed using the unique TCM scheme for each patient model. Lung doses were tallied and effective doses were estimated using ICRP 103 tissue weighting factors. Effective and lung dose values were normalized by scanspecific 32 cm CTDIvol values based upon the average tube current across the entire simulated scan. Absolute and normalized doses were reported as a function of WED for each patient. Results: For all ten patients modeled, the effective dose using TCM protocols was below 1.5 mSv. Smaller sized patient models experienced lower absolute doses compared to larger sized patients. Normalized effective and lung doses showed some dependence on patient size (R2 = 0.77 and 0.78, respectively). Conclusion: Effective doses for a low-dose lung screening protocol using TCM were below 1.5 mSv for all patient models used in this study. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Integration of a Concentrating Solar Steam Topping Turbine to an Existing Geothermal Binary Power Plant (CRADA CRD-17-700 Final Report)

U.S. Geothermal Inc. is a publicly traded leading renewable energy company focused on the development, production, and sale of electricity from geothermal energy with operating generation facilities at Raft River, Idaho; San Emidio, Nevada; and Neal Hot Springs, Oregon. (U.S. Geothermal was acquired by Ormat Technologies, Inc. since the execution of this project). This project will evaluate the integration of a concentrating solar steam topping cycle with a geothermal bottoming binary cycle. This hybrid plant configuration will enable high efficiency conversion of the solar heat to electrical power, while simultaneously increasing the power output of the bottoming cycle. Such a design has the potential to reduce the cost of electrical power from concentrating solar power (CSP) and geothermal plants, and a viable design could be deployed at existing geothermal plants that have experienced resource productivity decline as well as in greenfield projects. This analysis will examine US Geothermal’s Raft River power plant for implementation of this concept. INL and NREL have previously collaborated in the analysis of geo-solar hybrid power plants. These two labs will again work together to model the solar field, steam-topping, and geothermal bottoming cycles in order to investigate the performance of the proposed cycle. This analysis will provide US Geothermal with technical information including equipment specifications and estimated power generation necessary to advance the concept toward implementation. A successful project outcome has two parts: One would be for US Geothermal to have a process flow diagram (heat and mass balance), and performance parameters for the major pieces of equipment. US Geothermal could then use this information evaluate the techno-economic feasibility of the hybrid power cycle, and serve as the basis for subsequent detailed equipment design, purchase, and installation of the cycle. The second would be for the developed solution to be published by DOE for use by other geothermal developers both to retrofit underperforming plants and to lower the LCOE of new developments.

15 GEOTHERMAL ENERGY↗
NASA NTRS1980

Seasat. Volume 4: Attitude determination

The Seasat project was a feasibility demonstration of the use of orbital remote sensing for global ocean observation. The satellite was launched in June 1978 and was operated successfully until October 1978. A massive electrical failure occurred in the power system, terminating the mission prematurely. The actual implementation of the Seasat Attitude Determination system and the contents of the attitude data files generated by that system are documented. The deviations from plan caused by the anomalous Sun interference with horizon sensors, inflight calibration of Sun sensor head 2 alignment and horizon sensor biomass, estimation of yaw interpolation parameters, Sun and horizon sensor error sources, and yaw interpolation accuracy are included. Examples are given of flight attitude data from all modes of the Orbital Attitude Control System, of the ground processing effects on attitude data, and of cold cloud effects on pitch, and roll data.

Treder, A. J.↗
NASA NTRS1981

Sunspots and solar variability

The analyses of Willson et al. (1981) and Hudson et al. (1981) are extended in correlating the total solar irradiance monitor (ACRIM) data with the routine synoptic sunspot data. At the simplest level, this correlation reveals clearly that the so-called 'missing flux' is truly missing in the sense that large young active regions do produce at net diminution of the irradiance when their spots cross the central meridian. It is pointed out that the irradiance deficit must of course be made up, either promptly or on intermediate time scales; this is because the surface effects cannot perturb the energy generation processes in the interior. In the approach taken here, simple models of the reemission are constructed, the total reemission is scaled to the estimated sunspot deficit, and an attempt is made to measure the parameters of the models by a statistical comparison with ACRIM data.

Hudson, H. S.↗
NASA NTRS2016

A Comparison of Metamodeling Techniques via Numerical Experiments

This paper presents a comparative analysis of a few metamodeling techniques using numerical experiments for the single input-single output case. These experiments enable comparing the models' predictions with the phenomenon they are aiming to describe as more data is made available. These techniques include (i) prediction intervals associated with a least squares parameter estimate, (ii) Bayesian credible intervals, (iii) Gaussian process models, and (iv) interval predictor models. Aspects being compared are computational complexity, accuracy (i.e., the degree to which the resulting prediction conforms to the actual Data Generating Mechanism), reliability (i.e., the probability that new observations will fall inside the predicted interval), sensitivity to outliers, extrapolation properties, ease of use, and asymptotic behavior. The numerical experiments describe typical application scenarios that challenge the underlying assumptions supporting most metamodeling techniques.

Crespo, Luis G.↗
DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗
NASA NTRS2017

CME Velocity and Acceleration Error Estimates Using the Bootstrap Method

The bootstrap method is used to determine errors of basic attributes of coronal mass ejections (CMEs) visually identified in images obtained by the Solar and Heliospheric Observatory (SOHO) mission's Large Angle and Spectrometric Coronagraph (LASCO) instruments. The basic parameters of CMEs are stored, among others, in a database known as the SOHO/LASCO CME catalog and are widely employed for many research studies. The basic attributes of CMEs (e.g. velocity and acceleration) are obtained from manually generated height-time plots. The subjective nature of manual measurements introduces random errors that are difficult to quantify. In many studies the impact of such measurement errors is overlooked. In this study we present a new possibility to estimate measurements errors in the basic attributes of CMEs. This approach is a computer-intensive method because it requires repeating the original data analysis procedure several times using replicate datasets. This is also commonly called the bootstrap method in the literature. We show that the bootstrap approach can be used to estimate the errors of the basic attributes of CMEs having moderately large numbers of height-time measurements. The velocity errors are in the vast majority small and depend mostly on the number of height-time points measured for a particular event. In the case of acceleration, the errors are significant, and for more than half of all CMEs, they are larger than the acceleration itself.

Michalek, Grzegorz↗
NASA NTRS2010

Data Validation in the Kepler Science Operations Center Pipeline

We present an overview of the Data Validation (DV) software component and its context within the Kepler Science Operations Center (SOC) pipeline and overall Kepler Science mission. The SOC pipeline performs a transiting planet search on the corrected light curves for over 150,000 targets across the focal plane array. We discuss the DV strategy for automated validation of Threshold Crossing Events (TCEs) generated in the transiting planet search. For each TCE, a transiting planet model is fitted to the target light curve. A multiple planet search is conducted by repeating the transiting planet search on the residual light curve after the model flux has been removed; if an additional detection occurs, a planet model is fitted to the new TCE. A suite of automated tests are performed after all planet candidates have been identified. We describe a centroid motion test to determine the significance of the motion of the target photocenter during transit and to estimate the coordinates of the transit source within the photometric aperture; a series of eclipsing binary discrimination tests on the parameters of the planet model fits to all transits and the sequences of odd and even transits; and a statistical bootstrap to assess the likelihood that the TCE would have been generated purely by chance given the target light curve with all transits removed. Keywords: photometry, data validation, Kepler, Earth-size planets

Wu, Hayley↗
DOE OSTI2023

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Efficient and Collision-Free Anticipative Cruise Control in Randomly Mixed Strings

Connected intelligent vehicle following can improve safety and efficiency compared to today's road transport, but real traffic in the near future will not provide an ideal setting for its deployment. Unconnected human-driven vehicles will follow variable behavior patterns that combine with differences in dynamic capability to create heterogeneous scenarios. In this paper, connected automated vehicles employ model predictive control for following in traffic that may include both heavy and passenger vehicles at quasi-random positions. Adding further realism, some quasi-randomly mixed vehicles are completely unconnected and equipped with the reactive intelligent driver model using pseudorandom parameters. A mixed-integer quadratic programming formulation adapts the predictive algorithm to diverse powertrain operating point constraints. The preceding vehicle's control input is estimated from velocity and brake light observations and used to probabilistically generate a preview for the ego vehicle. Here, a terminal constraint designed using particle kinematics prevents collisions due to shortsightedness. Results are simulated at various heavy and predictive vehicle concentrations in the presence of packet loss. Linear fuel economy improvements between 1.4% and 1.9% per 10% increase in predictive vehicle penetration rate are shown.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

gcamland v1.0 – An R Package for Modelling Land Use and Land Cover Change

gcamland is an open source R package that was built to allocate land across a variety of types based on changes in agricultural yield and commodity price. The land allocation algorithm is based on the one included in the Global Change Assessment Model (GCAM). gcamland includes the ability to run in a historical mode, enabling model validation and parameter estimation, or in a future mode, simulating changes in land use/land cover in the future. For both modes, gcamland can run a single simulation or a large ensemble of simulations with different parameters. When ensembles are generated in the historical mode, gcamland calculates the likelihood of a given parameter set by comparing to observational data. gcamland is publicly available via GitHub and has can be adjusted to represent alternative scenarios or configured to different regions and land types.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Polarimetric SAR Interferometry Evaluation in Mangroves

TanDEM-X (TDX) enables to generate an interferometric coherence without temporal decorrelation effect that is the most critical factor for a successful Pol-InSAR inversion, as have recently been used for forest parameter retrieval. This paper presents mangrove forest height estimation only using single-pass/single-baseline/dual-polarization TDX data by means of new dual-Pol-InSAR inversion technique. To overcome a lack of one polarization in a conventional Pol- InSAR inversion (i.e. an underdetermined problem), the ground phase in the Pol-InSAR model is directly estimated from TDX interferograms assuming flat underlying topography in mangrove forest. The inversion result is validated against lidar measurement data (NASA's G-LiHT data).

Mangrove↗
DOE OSTI2022

Bayesian estimation of the $S$ factor and thermonuclear reaction rate for 16 O(p, γ) 17 F

The 16 O(p, γ) 17 F reaction is the slowest hydrogen-burning process in the CNO mass region. Its thermonuclear rate sensitively impacts predictions of oxygen isotopic ratios in a number of astrophysical sites, including AGB stars. The reaction has been measured several times at low bombarding energies using a variety of techniques. The most recent evaluated experimental rates have a reported uncertainty of about 7.5% below 1 GK. However, the previous rate estimate represents a best guess only and was not based on rigorous statistical methods. We apply a Bayesian model to fit all reliable 16 O(p, γ) 17 F cross section data, and take into account independent contributions of statistical and systematic uncertainties. The nuclear reaction model employed is a single-particle potential model involving a Woods-Saxon potential for generating the radial bound state wave function. The model has three physical parameters, the radius and diffuseness of the Woods-Saxon potential, and the asymptotic normalization coefficients (ANCs) of the final bound state in 17 F. Here, we find that performing the Bayesian S -factor fit using ANCs as scaling parameters has a distinct advantage over adopting spectroscopic factors instead. Based on these results, we present the first statistically rigorous estimation of experimental 16 O(p, γ) 17 F reaction rates, with uncertainties (±4.2%) of about half the previously reported values.

6 ≤ A ≤ 19↗
DOE OSTI2019

Investigation of assumptions and approximations in the virtual fields method for a viscoplastic material model

The Virtual Fields Method (VFM) is an inverse technique used for parameter estimation and calibration of constitutive models. Many assumptions and approximations—such as plane stress, incompressible plasticity, and spatial and temporal derivative calculations—are required to use VFM with full-field deformation data, for example, from Digital Image Correlation (DIC). Here, this work presents a comprehensive discussion of the effects of these assumptions and approximations on parameters identified by VFM for a viscoplastic material model for 304L stainless steel. We generated synthetic data from a Finite-Element Analysis (FEA) in order to have a reference solution with a known material model and known model parameters, and we investigated four cases in which successively more assumptions and approximations were included in the data. We found that VFM is tolerant to small deviations from the plane stress condition in a small region of the sample, and that the incompressible plasticity assumption can be used to estimate thickness changes with little error. A local polynomial fit to the displacement data was successfully employed to compute the spatial displacement gradients. The choice of temporal derivative approximation (i.e., backwards difference versus central difference) was found to have a significant influence on the computed rate of deformation and on the VFM results for the rate-dependent model used in this work. Finally, the noise introduced into the displacement data from a stereo-DIC simulator was found to have negligible influence on the VFM results. Evaluating the effects of assumptions and approximations using synthetic data is a critical first step for verifying and validating VFM for specific applications. Finally, the results of this work provide the foundation for confidently using VFM for experimental data.

304L stainless steel↗
DOE OSTI2017

Shocks and Tides Quantified in the “Sausage” Cluster, CIZA J2242.8+5301 Using N-body/Hydrodynamical Simulations

The colliding cluster, CIZA J2242.8+5301, displays a spectacular, almost 2 Mpc long shock front with a radio based Mach number M≃5, that is puzzlingly large compared to the X-ray estimate of M≃2.5. The extent to which the X-ray temperature jump is diluted by cooler unshocked gas projected through the cluster currently lacks quantification. Here we apply our self-consistent N-body/hydrodynamical code (based on FLASH) to model this binary cluster encounter. We can account for the location of the shock front and also the elongated X-ray emission by tidal stretching of the gas and dark matter between the two cluster centers. The required total mass is 8.9×10{sup 14} M{sub ⊙} with a 1.3:1 mass ratio favoring the southern cluster component. The relative velocity we derive is ≃2500 km s{sup −1} initially between the two main cluster components, with an impact parameter of 120 kpc. This solution implies that the shock temperature jump derived from the low angular resolution X-ray satellite Suzaku is underestimated by a factor of two, due to cool gas in projection, bringing the observed X-ray and radio estimates into agreement. Finally, we use our model to generate Compton-y maps to estimate the thermal Sunyaev–Zel’dovich (SZ) effect. At 30 GHz, this amounts to ΔS{sub n}=−0.072 mJy/arcmin{sup 2} and ΔS{sub s}=−0.075 mJy/arcmin{sup 2} at the locations of the northern and southern shock fronts respectively. Our model estimate agrees with previous empirical estimates that have inferred the measured radio spectra of the radio relics can be significantly affected by the SZ effect, with implications for charged particle acceleration models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Second harmonic generation for estimating state of charge of lithium-ion batteries

This study applied the nonlinear ultrasonic method, second harmonic generation, to precisely estimate the state of charge (SoC) in lithium-ion batteries. The second harmonic of the longitudinal wave is generated on a pouch cell battery at 5 MHz with a through-transmission setup. The relative nonlinear parameter β' is determined by analyzing the amplitudes at the fundamental and second harmonic frequencies. To enhance the nonlinear parameter's measurement accuracy, multiple excitation amplitudes are employed. Two separate charge/discharge tests (four-cycle and eight-cycle) are conducted on the battery at a rate of C/10. The nonlinear parameter is measured periodically during the charge/discharge process, and temperature compensation is applied to the measurement. The correlation curves between the nonlinear parameter and the actual SoC align well for the four-cycle and eight-cycle tests, and a robust linear relationship is observed for both correlation curves. A linear model and a second-order polynomial model are applied to fit the correlation using all data points from both tests. The two models are employed to validate the SoC prediction on a second battery by using a four-cycle test. Furthermore, the results indicate that both models can predict the SoC with an accuracy of approximately 3%, whereas the polynomial model demonstrates smaller errors in the regions near 0% and 100% SoC. Therefore, the nonlinear parameter β', measured through the second harmonic generation, can effectively predict lithium-ion battery SoC with an accuracy of less than 3%.

25 ENERGY STORAGE↗
DOE OSTI2018

Physical mechanism of electron bunch generation by an ultrarelativistic-intensity laser pulse passing through a sharp plasma boundary

The physical mechanism of the interaction process of a laser pulse of ultrarelativistic intensity with semi-bounded plasma having a sharp boundary is studied in one-dimensional geometry. It is shown that electron bunches are generated by the laser pulse due to the multiflow motion of plasma electrons with crossing trajectories. An analytical relation that allows an electron bunch charge to be estimated as a function of plasma characteristics and electron trajectory parameters is derived and confirmed by the results of numerical simulations. (paper)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗