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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 NTRS2020

Landsat-8 TIRS Thermal Radiometric Calibration Status

The Landsat-8 Thermal Infrared Sensor (TIRS) has been acquiring two-band thermal infrared images of the Earth’s surface since 2013. The calibration of the two-band system has been monitored using the on-board calibrator and validated with vicarious calibration performed by NASA/JPL and RIT since launch. Soon after launch, it was discovered that the instrument had a significant stray light effect that was affecting the radiometric calibration. The stray light was corrected in the processing system in 2017. Since then, it has become apparent that there was an additional radiometric error, based on the vicarious calibration results. With a failure within the primary electronic system and subsequent switch to the redundant electronic system, the TIRS instrument effectively has two separate calibration regimes. The vicarious calibration found a statistically significant calibration error, primarily a constant over time, in Band 11 on the primary electronics (Feb 11,2013 through March 5,2015) of about -0.6K at 300K. The calibration error in Band 10 was smaller though still statistically significant at about 0.2K at 300K. On the redundant side(March 5,2015 to present), the calibration error is more signal dependent than time dependent. Both bands are affected, with Band 10 having an error between 1K and -0.4K (between 273-320K) and Band 11 having an error between 0.8K and -1.44K (between 273-320K).This calibration error will be corrected within the USGS Landsat Product Generation System with the release of Landsat Collection-2 products. The Collection-2 release also includes a correction to the relative radiometric calibration of TIRS data. Striping as a result of poor detector-to-detector normalization has been increasing in the imagery since launch. The TIRS relative radiometric calibration will be updated based on internal calibrator data to remove the stripes on a quarterly basis. The visible stripes are generally at 0.1-0.2% level, though there are some detectors in each band that have changed by 1% or more. The Collection-2 release will result in much more uniform TIRS images.

Julia A Barsi↗
NASA NTRS2012

Advances in the RXTE Proportional Counter Array Calibration: Nearing the Statistical Limit

During its 16 years of service Rossi X-ray Timing Explorer (RXTE) mission has provided an extensive archive of data, which will serve as a primary source of high cadence observation of variable X-ray sources for fast timing studies. It is, therefore, very important to have the most reliable calibration of RXTE instruments. The Proportional Counter Array (PCA) is the primary instrument on-board RXTE which provides data in 2-50 keY with higher than millisecond time resolution in up to 256 energy channels. In 2009 RXTE team revised the response residual minimization method used to derive the parameters of the PCA physical model. The procedure is now based on the residual minimization between the model spectrum for Crab nebula emission and a calibration data set consisting of a number of spectra from the Crab and the on-board Am241 calibration source, uniformly covering a whole RXTE span. The new method led to a much more effective model convergence and allowed for better understanding of the behavior of the PCA energy-to-channel relationship. It greatly improved the response matrix performance. We describe the new version of the RXTE/PCA response generator PCARMF vll.7 along with the corresponding energy-to-channel conversion table (version e05v04) and their difference from the previous releases of PCA calibration. The new PCA response adequately represents the spectrum of the calibration sources and successfully predicts the energy of the narrow iron emission line in Cas-A throughout the RXTE mission.

Shaposhnikov, Nikolai↗
NASA NTRS2004

Application of a Semiparametric Statistical Method to Spacecraft Sensor and Calibration Data

A semiparametric statistical method is applied to spacecraft sensor data and spacecraft sensor residual data. The method stipulates a reference distribution and deviations from it. The problem is to estimate the reference distribution and the distorted distributions from all available data from all the sources under consideration. The problem can be solved by assuming a distortion form and independent data.

Kedem, Benjamin↗
DOE OSTI2023

Grain size estimation in fluvial gravel bars using uncrewed aerial vehicles: A comparison between methods based on imagery and topography

Abstract Grain size assessments are necessary for understanding the various geomorphological, hydrological and ecological processes that occur within rivers. Recent research has shown that the application of Structure‐from‐Motion (SfM) photogrammetry to imagery from uncrewed aerial vehicles (UAVs) shows promise for rapidly characterising grain sizes along rivers in comparison to traditional field‐based methods. Here, we evaluated the applicability of different methods for estimating grain sizes in gravel bars along a study reach in the Olentangy River in Columbus, Ohio. We collected imagery of these gravel bars with a UAV and processed those images with SfM photogrammetry software to produce three‐dimensional point clouds and orthomosaics. Our evaluation compared statistical models calibrated on topographic roughness, which was computed from the point clouds, and to those based on image texture, which was computed from the orthomosaics. Our results showed that statistical models calibrated on image texture were more accurate than those based on topographic roughness. This might be because of site‐specific patterns of grain size, shape and imbrication. Such patterns would have complicated the detection of topographic signatures associated with individual grains. Our work illustrates that UAV‐SfM approaches show potential to be used as an accessible method for characterising surface grain sizes along rivers at higher spatial and temporal resolutions than those provided by traditional methods.

Wong, Tyler↗
DOE OSTI2020

Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation

This study addresses the problem of calibrating utility-maximizing nested logit activity-based travel demand model-systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model-system’s parameters prior to their application in transportation planning, policy making, and operations. However, calibration of activity-based model-systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider the fundamental inter-dependencies among choice-models and do not have a systematic way to adjust model parameters. Also, other purely simulation-based approaches do not perform well in large-scale applications. In this study, we focus on utility-maximizing nested logit activity-based model-systems and calibrating aggregate statistics such as activity shares, mode shares, time-dependent & mode-specific OD flows, and time-dependent & mode-specific sensor counts. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it. The solution procedure relies on microsimulation to calculate expectations of the aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function —that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model-system and is non-myopic in considering the dependencies across its model components. The formulated optimization problem is non-convex, highly nonlinear, and potentially has multiple-minima. Lastly, we show —through a real-world application— that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model-systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nondestructive measurements of residual 235 U mass of Israeli Research Reactor-1 fuel using the Advanced Experimental Fuel Counter

In 2018, a measurement campaign took place with participants from Los Alamos National Laboratory (LANL), the Nuclear Research Centre-Negev (NRCN) and Soreq Nuclear Research Center (SNRC) at the Israeli Research Reactor-1 (IRR-1) in which 14 of the reactor’s used fuel assemblies (FAs) with varied amount of depletion were measured with the nondestructive assay instrument Advanced Experimental Fuel Counter (AEFC). Designed for safeguards purposes, the AEFC measures both neutrons emitted from the FA (passive neutrons) and fission neutrons induced by an external neutron source (in this experiment, 252 Cf). Signals recorded with the AEFC include total neutron count rates (Singles), time-correlated neutron count rates (Doubles), and total gamma-ray count rates. The 235 U content of the FAs was previously assessed by two independent methods: (1) measurement of the transparency of the FA to low-energy gamma rays from an activated rhenium source (rhenium gamma transmission, or the RGT method) and (2) calculation of the 30-year burnup history of the core using detailed three-dimensional Monte-Carlo core depletion calculations. The results from the FAs that had been measured via the RGT method were used to construct the calibration curves, which translate the AEFC count rates to 235 U mass. Then, the calibration was evaluated using AEFC measurements of six additional FAs that were not measured via the RGT method. From the results, it was determined the Doubles calibration curve was more reliable than that of the Singles and follows a simple second-order polynomial fit for the whole range of residual 235 U mass content, albeit with larger statistical uncertainty. Detailed uncertainties quantification was conducted for both the AEFC Singles and Doubles. This includes the analysis of statistical uncertainties, calibration uncertainty, and random uncertainties due to the sensitivity of the AEFC to several sources of uncertainty, namely the FA position, FA orientation, interrogation source position, and ambient pool temperature. Finally, an overall total uncertainty of 6 g of 235 U is estimated for the Singles and Doubles, which is mainly due to calibration uncertainty (for the Singles) and statistical uncertainty (for the Doubles), and which constitutes 3%–6% of the 235 U total mass in the FAs, depending on their level of depletion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1989

Calibration of quadpolarization SAR data using backscatter statistics

A new technique is described for calibration of complex multipolarization SAR imagery. Scatterer reciprocity and lack of correlation between like- and cross-polarized radar echoes for natural targets are used to remove cross-polarized contamination in the radar data channels without the use of known ground targets. If known targets are available, all data channels can be calibrated relative to one another and absolutely as well. The method is verified with airborne SAR data.

Klein, Jeffrey D.↗
NASA NTRS2020

Calibration of the 11x11-Foot Transonic Wind Tunnel at NASA Ames Research Center

A static pipe calibration characterizing the streamwise static pressure distribution was conducted at the 11-By 11-Foot Transonic Wind Tunnel at NASA Ames Research Center. This data is used to determine the local Mach number within the test section and evaluate buoyancy corrections to axial force measurements. The 60 foot long, 6 inch diameter pipe contained 444 static pressure taps spanning the test section and nozzle regions of the tunnel. The forward end of the pipe extends into the settling chamber and is held by four cables mounting to the tunnel shell, and the aft end is fixed on the institutional model support system. A hydraulic cylinder at the aft end of the pipe provides tension on the system to reduce vibration and to keep the pipe as level as possible throughout the test section. The previous calibration was improved upon by using pressure scanners with greater accuracy, ensuring a uniform pressure tube length for each tap to control pneumatic lag, optically tracking any streamwise movement of the pipe, and more tightly controlling the tunnel condition set points. Typically this calibration is conducted with the pipe on tunnel centerline and 33 inches below centerline for sting-mounted models and semi-span (i.e. floor-mounted) models respectively, however schedule demands permitted only the centerline calibration. The semi-span calibration is planned to be completed in the summer of 2020. Immediately following the static pipe calibration, a shorter, 9 foot static pipe used as the calibration check standard was installed to obtain its first post-calibration pressure dataset. This short static pipe consists of 148 static pressure taps distributed along the pipe section and one total pressure tap at the end of an ogive nose. Performing the calibration test and its check standard back-to-back allows this dataset to establish a reliable baseline for future calibration check standard testing. Over time the use of a calibration check standard offers the ability to assess the stability of the calibration through statistical process control in an efficient and costeffective manner, thereby potentially increasing the time required between full tunnel calibrations.

White, Bethany↗
DOE OSTI2021

Calibration of the Diffusivity Predictions of Centipede Using Approximate Bayesian Computation and Applications in Nyx (Engineering Scale) and Xolotl-MARMOT (Meso-Scale) Simulations

Fission gas evolution and release in UO 2 nuclear fuel are important fuel performance metrics and occur in several distinct stages: 1) nucleation, growth and resolution of intra-granular bubbles, 2) diffusion to grain boundaries and 3) nucleation and growth of bubbles at grain boundaries, which eventually form a connected network (percolation) enabling release of gas from grain boundaries through connections to triple junctions, grain edges or free surfaces. The NE-SciDAC project is developing several computational tools to model this problem, which are connected in a hierarchical multi-scale framework. The information transfer in the multi-scale framework is a critical step that, in addition to best-estimates, should include uncertainty quantification. Despite taking a first-principles multi-scale approach, there is a need to perform parameter calibration to ensure consistency with available experimental data. In the present study, uncertainty quantification (UQ) and parameter calibration is demonstrated for one of the lower length scale codes in the multi-scale framework (Centipede) and then the results, including instances of the propagated uncertainties, are used in other codes within the framework, specifically Nyx and Xolotl-MARMOT. We calibrated the model parameters in Centipede, a computer code used to predict diffusivities of uranium (U) and xenon (Xe) in the context of the simulation of fission gas in uranium oxide (UO 2 ) nuclear fuel. The Centipede code depends on 183 parameters, all of which are subject to uncertainty. The three data sets used in our calibration effort are taken from the literature. This data is available as a set of measurements, including measurement errors. Our goal is to calibrate a statistical model that predicts both the value of the measurement and the uncertainty associated with the measurement. We perform a Bayesian calibration of the model parameters using a dedicated approximate Bayesian computation (ABC) likelihood function. To avoid excessive computational costs, we replace the expensive Centipede simulation code by a higher-order surrogate model, constructed using only the 9 most important parameters. These important parameters are identified by a preliminary global sensitivity analysis (GSA) study. Among the important parameters are T0 (the temperature at which UO 2 is perfectly stoichiometric) and Hf_pO2 (the temperature dependence of the oxygen (O) partial pressure) that should be considered as operating conditions to be estimated along with the other parameters. We consider two different cases: one where we define one set of these operating conditions for all data sets, and one where we define distinct operating condition parameters for each data set. The Xe diffusivities predicted by the latter case show distinct features that could not be observed in the former. Next, we use the diffusivity predictions by Centipede as input to Nyx, a reduced order fuel performance code focused on gas behavior alone, in order to estimate quantities associated with inter-granular bubble formation at conditions specified by the experiments. Finally, the diffusivities obtained from the calibrated Centipede runs were used in coupled Xolotl-MARMOT simulations of intra- and inter-granular gas evolution. The results are compared to simulations using the baseline diffusivities from Turnbull et al.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Bias Correction and Statistical Downscaling of Future Solar Irradiance Projections Using the NSRDB

Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.

analytical models↗
DOE OSTI2026

Development of a 95-Year Solar Dataset for Resource Adequacy Studies

Long-term high-resolution solar data provides enhanced understanding of variability of solar generation and enhances our ability to develop strategies for a resilient and reliable electric grid under high deployment of solar energy. Therefore, it is important to develop long-term synthetic datasets that can provide multiple occurrences of various severe weather scenarios that are expected to test the limits of resource adequacy under scenarios contain various energy generation sources. Examples of such scenarios could be long periods of high temperatures when demand for electricity is high or periods where high winds could lead to a shut-down of transmission lines for long periods of time to ensure fire safety. NREL has developed the first version of such a dataset covering a 95-year period covering 2006-2100 at a 4km hourly resolution. This dataset contains all variables necessary to calculate solar generation. During development of this dataset, we focused on creating unbiased, high-resolution solar irradiance through statistical downscaling methods, using Regional Climate Model (RCM) simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) as input. The National Solar Radiation Database (NSRDB) containing over 25 years of observations was used to calibrate the statistical downscaling models. This presentation will outline the primary steps in developing this dataset, including (1) regridding RCM data to a common grid at 20-km resolution, (2) correcting RCM biases with NSRDB, (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar and ancillary data. Additionally, we will present an evaluation of the downscaled data against the NSRDB across various zones in the CONUS. Lastly, we will present a user guide for accessing the datasets.

14 SOLAR ENERGY↗
NASA NTRS2007

Estimation Filter for Alignment of the Spitzer Space Telescope

A document presents a summary of an onboard estimation algorithm now being used to calibrate the alignment of the Spitzer Space Telescope (formerly known as the Space Infrared Telescope Facility). The algorithm, denoted the S2P calibration filter, recursively generates estimates of the alignment angles between a telescope reference frame and a star-tracker reference frame. At several discrete times during the day, the filter accepts, as input, attitude estimates from the star tracker and observations taken by the Pointing Control Reference Sensor (a sensor in the field of view of the telescope). The output of the filter is a calibrated quaternion that represents the best current mean-square estimate of the alignment angles between the telescope and the star tracker. The S2P calibration filter incorporates a Kalman filter that tracks six states - two for each of three orthogonal coordinate axes. Although, in principle, one state per axis is sufficient, the use of two states per axis makes it possible to model both short- and long-term behaviors. Specifically, the filter properly models transient learning, characteristic times and bounds of thermomechanical drift, and long-term steady-state statistics, whether calibration measurements are taken frequently or infrequently. These properties ensure that the S2P filter performance is optimal over a broad range of flight conditions, and can be confidently run autonomously over several years of in-flight operation without human intervention.

Bayard, David↗
DOE OSTI2020

Bayesian Approach to Estimation of Water Table Elevations Using Historical Rasters as Prior Information 2019 - 20430

In cases of complex but only partially known geology and a lack of spatial control in observation well locations, water table elevation estimation is very challenging. In some cases, auxiliary information, such as observations of the movement of tracers, operation of injection and extraction wells, and calibration of groundwater models against the historical elevation data, can be combined with expert judgement to estimate flow directions in areas of sparse data and to aid in the production of more reliable contour maps (and associated rasters) than could be produced by relying on sparse well elevation data alone. Given a historical sequence of these raster maps, the question arises how to automate, to the extent possible, the process of producing new raster maps to reflect data from previous times, the current data and the operation of expert judgement. One solution is to adopt a Bayesian point of view and to regard the historical well elevation data, auxiliary information and historical raster maps as prior information. The well elevations for water table wells, as well as those for injection/extraction wells and the data associated with other relevant variables, can be viewed as predictors for the raster surface. From this prior information, we can, conditional on the values of these predictors for a new time period, compute an expected value map and a standard deviation map for the new raster. These then can be taken to specify a prior predictive distribution for the pixels in the new raster map. Then we condition the pixels, corresponding to water level observation wells within the raster, on the observed values in those wells (which in general will differ from the regression estimate) for the new time period. Given the smoothness of the water table surface, we then smooth the surface of deviations from the mean surface, based on the variograms of the historical rasters, and add this smoothed surface to the regression mean surface. The error structure of the produced raster map is defined by the regression error structure and the error due to smoothing based on the estimated variograms. This methodology has been developed and is being further refined for groundwater monitoring and remediation at LANL. It is a very flexible method that can also be applied with a variety of other predictors applied to model the water level wells in the area of interest over the historical record. The smoothness of the spatial process and its possible evolution over time can then be estimated from the residuals from this regression. This can be augmented by expert hydrogeological opinion based on site topography and hydrogeology. (authors)

54 ENVIRONMENTAL SCIENCES↗