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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Case for and Against a Gadolinium Bias in SCALE: Round 2

The “Opening Arguments” for and against a gadolinium bias in SCALE were presented at the American Nuclear Society Annual Meeting in Philadelphia, Pennsylvania, in June, 2018. Some critical experiments included in the Oak Ridge National Laboratory Verified, Archived Library of Inputs and Data (VALID) indicate a significant bias as a function of gadolinium concentration. Other experiments indicate that no significant bias exists. The work presented here develops a larger suite of gadolinium-bearing benchmark models to further examine code, data, and benchmark performance. The new benchmark models have been reviewed for accuracy, but documentation and review for addition to the VALID library have not been completed. The problematic benchmarks included in VALID are HEU-SOL-THERM-014 and -016. These are two evaluations from a series of experiments from the Institute for Physics and Power Engineering (IPPE), Russia, documented in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The entire set of evaluations also includes HEU-SOL-THERM-015, -017, -018, -019, and -025. Each evaluation contains a different uranium concentration, and different configurations within each evaluation include different gadolinium concentrations. These 7 evaluations contain a total of 52 configurations and form the largest subset of experiments considered, and they allow for a more complete assessment of the performance of these benchmarks than has historically been possible using just the HEU-SOL-THERM-014 and -016 results. Additional solution experiments are considered, including MIX-SOL-THERM-006 and -007 and PU-SOL-THERM-034. MIX-SOL-THERM-007 is in the VALID library, whereas MIX-SOL-THERM-006 and PU-SOL-THERM-034 are not. The results for MIX-SOL THERM-007 have not shown a gadolinium trend. These mixed- and plutonium-fueled solutions include 28 configurations. Some experiments including solid fuel and solid gadolinium are also included. These experiments include highly enriched uranium (HEU) foils moderated with polyethylene in HEU-MET-THERM-010, -016, and -034, and low enriched uranium (LEU) pin arrays with gadolinia absorber rods in LEU-COMP THERM-036 and -043. A total 32 cases with solid fuel are included. The IPPE solution benchmarks show a fairly high degree of variability, but no clear trend as a function of gadolinium concentration can be observed. The mixed- and plutonium-fueled solutions show less variability than the HEU solutions and also no trend relative to gadolinium concentration. The solid-fueled experiments also show no trend as a function of gadolinium content. The entire set of benchmarks shows no clear trend on gadolinium content or the energy of the average neutron lethargy causing fission.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2018

Polarization Decomposition and Temperature Bias Resolution for SMAP Passive Soil Moisture Retrieval Using Time Series Brightness Temperature Observations

In passive microwave remote sensing of soil moisture, the tau-omega (τ-ω) model has often been used to provide soil moisture estimates at a spatial scale representative of the satellite footprint dimensions. For modeling simplicity, model parameters such as the single scattering albedo (ω) and vegetation opacity (τ) that go into the geophysical inversion process are often assumed to be independent of polarizations. Although this absence of polarization dependence can often be justified in special cases as in low-frequency remote sensing or under dense vegetation conditions, it is not a robust assumption in general. Additional model parameterization errors arising from this assumption are possible, leading to degradation in soil moisture estimation accuracy. In this paper, we propose a time series approach to try to resolve the polarization dependence of several τ-ω model parameters as well as the temperature bias arising from the ancillary temperature data. The Version 4 of the Soil Moisture Active Passive (SMAP) Level 1B brightness temperature time series observations were used to illustrate the mechanics of this approach, with an emphasis on a comparison between resulting satellite soil moisture retrievals and in situ data collected at several core validation sites. It was found that this time series approach resulted in significant reduction of the dry bias exhibited in the current SMAP passive soil moisture data products, while retaining the same performance in other metrics of the current baseline passive soil moisture retrieval algorithm.

time series↗
NASA NTRS2006

Estimation of Stability and Control Derivatives of an F-15

A technique for real-time estimation of stability and control derivatives (derivatives of moment coefficients with respect to control-surface deflection angles) was used to support a flight demonstration of a concept of an indirect-adaptive intelligent flight control system (IFCS). Traditionally, parameter identification, including estimation of stability and control derivatives, is done post-flight. However, for the indirect-adaptive IFCS concept, parameter identification is required during flight so that the system can modify control laws for a damaged aircraft. The flight demonstration was carried out on a highly modified F-15 airplane (see Figure 1). The main objective was to estimate the stability and control derivatives of the airplane in nearly real time. A secondary goal was to develop a system to automatically assess the quality of the results, so as to be able to tell a learning neural network which data to use. Parameter estimation was performed by use of Fourier-transform regression (FTR) a technique developed at NASA Langley Research Center. FTR is an equation- error technique that operates in the frequency domain. Data are put into the frequency domain by use of a recursive Fourier transform for a discrete frequency set. This calculation simplifies many subsequent calculations, removes biases, and automatically filters out data beyond the chosen frequency range. FTR as applied here was tailored to work with pilot inputs, which produce correlated surface positions that prevent accurate parameter estimates, by replacing half the derivatives with predicted values. FTR was also set up to work only on a recent window of data, to accommodate changes in flight condition. A system of confidence measures was developed to identify quality-parameter estimates that a learning neural network could use. This system judged the estimates primarily on the basis of their estimated variances and of the level of aircraft response. The resulting FTR system was implemented in the Simulink software system and auto-coded in the C programming language for use on the Airborne Research Test System (ARTS II) computer installed in the F-15 airplane. The Simulink model was also used in a control room that utilizes the Ring Buffered Network Bus hardware and software, making it possible to evaluate test points during flights. In-flight parameter estimation was done for piloted and automated maneuvers, primarily at three test conditions. Figure 2 shows results for pitching moment due to symmetric stabilator actuations for a series of three pitch doublet maneuvers (in a doublet maneuver, a command to change attitude in a given direction by a given amount is followed immediately by a command to change attitude in the opposite direction by the same amount). A time window of 5 seconds was used. The portions of the curves shown in red are those that passed the confidence tests. The technique showed good convergence for most derivatives for both kinds of maneuvers - typically within a few seconds. The confidence tests were marginally successful, and it would be necessary to refine them for use in an IFCS.

Smith, Mark↗
DOE OSTI2025

Nuclear data sensitivity and uncertainty study of copper-reflected integral experiments [Slides]

This presentation touches on reducing uncertainties in intermediate-energy actinide nuclear data and this continues to be a high priority for many applications. The goal of PARADIGM (PARallel Approach of Differential and InteGral Measurements): accelerate efforts to reduce biases and uncertainties in nuclear data through improvements to the nuclear data pipeline. This presentation includes integral experiments and a summarization of existing copper nuclear data.

Cu63↗
NASA NTRS1984

A statistical evaluation and comparison of VISSR Atmospheric Sounder (VAS) data and corresponding rawinsonde measurements

The mesoscale accuracy of GOES Visible/IR Spin-Scan-Radiometer (VISSR) Atmospheric Sounder (VAS) temperature profiles and mean-precipitable-water measurements obtained over central Texas on March 6, 1982, is evaluated on the basis of comparing them with three-hourly rawinsonde data (up to 100 mbar) of similar spatial resolution for the same area and time. The VAS data comprise three sets: physical retrievals by the method of Smith (1970), modified physical retrievals by the method of Smith (1983), and regression soundings (incorporating some rawinsonde data) by the method of Lee et al. (1983). The data are presented graphically, and all three VAS data sets are found to have similar temperature biases, which vary from hot to cold with altitude and are apparently related to major inversions. Systematic moisture biases are seen in the physical and modified physical data sets, while the regression soundings are relatively unbiased but do not accurately reproduce moisture gradients seen in the rawinsonde data.

Jedlovec, G. J.↗
DOE OSTI2021

Application of Machine Learning Algorithms to Identify Problematic Nuclear Data

In this work we aim to show that Machine learning algorithms are promising tools for the identification of nuclear data that contribute to increased errors in transport simulations. We demonstrate this through an application of a machine learning algorithm (Random Forest) to the Whisper/MCNP6 criticality validation library to identify nuclear data that are associated with an increase of the bias (simulated - experimental $k_{eff}$) in the calculations. Specifically, the $k_{eff}$ sensitivity profiles (w.r.t. nuclear data) of 233 U solution benchmarks are used to predict the bias and Shapley Additive Explanations (SHAP) are used to explain how the sensitivities are related to the predicted bias. The SHAP values can be interpreted as sensitivity coefficients of the machine learning model to the $k_{eff}$ sensitivities which are used to make predictions of bias. Using the SHAP values we can identify specific subsets of nuclear data which have the highest probability of influencing bias. We demonstrate the utility of this method by showing how SHAP values were used to identify an inconsistency in the 19 F inelastic scattering nuclear data. The methodology presented here is not limited to transport problems and can be applied to other simulations if there are experimental measurements to compare against, simulations of those experimental measurements, and the ability to calculate sensitivities of the model output with respect to the data inputs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Measured Long-Term Solar Irradiance for Climate Studies

Solar radiation is primarily measured using high-quality radiometers (e.g., pyranometers and pyrheliometers). These instruments need to be calibrated regularly (every two years at a minimum). They are also susceptible to various sources of uncertainties. Therefore, rigorous data quality assessment is required to obtain high-confidence data from these radiometers. This is especially true when the data is used to understand climatic trends and/or extreme weather events. In this study, we attempted to create a continuous and reliable dataset by correcting the underlying data which we believe contains bias due to reference pyranometers swap procedures. These biases can be significant which can be up to two percentage points thereby influencing the interpretation of climate changes and/or extreme events. This study elaborates on the biases and methods to correct those biases.

data integrity↗
NASA NTRS1985

Blended SST fields combined SMMR/ship data (summary of technique)

All remote sensors have bias problems which vary in space and time. The infrared measurements are very sparse in cloudy areas: the SMMR has coverage limitations due to the interference of land areas and only half time operation. If one simply averaged the data from these various sources, the combination of the biases and varying sampling would introduce artifacts into the analyzed product which would reflect the sampling and which could be quite misleading. The remote sensors provide reasonable estimates of gradients of SST over length scales greater than the sensor resolution but smaller than hemispheric. Ships can be considered unbiased but are poorly distributed on small scales except in the densest shipping lanes. An analysis scheme based on a suggestion by Holl was developed which exploits just this approximately complementarity to provide an improved SST analysis. The resulting accuracy appears to be in the required approx. 0.5 C range for the particular case of using SMMR and ship data to produce two degree latitude by two degrees longitude monthly analyses.

Wilheit, T. T.↗
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.

17 WIND ENERGY↗
NASA NTRS1984

Electromagnetic bias of 36-GHz radar altimeter measurements of MSL

The data reduction techniques used to determine the magnitude of electromagnetic (EM) bias in radar altimeter measurements of mean sea level (MSL) area described. Particular attention is given to the bias reduction scheme developed specifically for the Surface Contour Radar (SCR) instrument of the Ocean Topography Experiment (TOPEX). The SCR makes it possible to determine the amount of the backscattered power due to EM reflectance per unit area by measuring both the return power and elevation. Variations of backscattered power for different sea states are determined as a function of displacement of the MSL. On the basis of the recent SCR observations from aircraft, a standard error due to EM bias is predicted for MSL measurements performed with a satellite altimeter radar operating at a frequency of 36 GHz. The obtained standard error was 1 percent for regions with waves 1.9-5.5 meters in height.

Walsh, E. J.↗
NASA NTRS2008

Using AIRS and IASI Data to Evaluate Absolute Radiometric Accuracy and Stability for Climate Applications

The creation of multi-decadal data sets for climate research requires better than 100 mK absolute calibration accuracy for the full range of spectral temperatures encountered under global conditions. Validation that this accuracy is achieved by the operational hyperspectral sounders from polar orbit is facilitated by comparing data from two instruments. Extreme radiometric calibration stability is critical to allow a long time series of noisy, but presumably long-term accurate truth measurements to be used for the validation of absolute accuracy at the 100 mK level. We use the RTGSST in the tropical oceans as ground truth. The difference between the AIRS derived sst2616 and the RTGSST based on six years of data shows a systematic cold bias of about 250 mK, but better than 4 mK/year stability. The double difference between AIRS and the RTGSST and IASI and the RTGSST with less than one year of data already allows statements at the 100 mK absolute level. It shows a 60 mK difference between the AIRS and the IASI calibration at 2616 cm-(sup 1) and 300 K, with a statistically insignificant 20 mK shift in six months.

EOS Aqua↗
NASA NTRS2000

Acoustic Absorption Characteristics of an Orifice With a Mean Bias Flow

The objective of the study reported here was to acquire acoustic and flow data for numerical validation of impedance models that simulate bias flow through perforates. The impedance model is being developed by researchers at High Technology Corporation. This report documents normal incidence impedance measurements a singular circular orifice with mean flow passing through it. All measurements are made within a 1.12 inch (28.5 mm) diameter impedance tube. The mean flow is introduced upstream of the orifice (with the flow and incident sound wave travelling in the same direction) with an anechoic termination downstream of the orifice. Velocity profiles are obtained upstream of the orifice to characterize the inflow boundary conditions. Velocity in the center of the orifice is also obtained. All velocity measurements are made with a hot wire anemometer and subsequent checked with mass flow measurements made concurrently. All impedance measurements are made using the Two-Microphone Method. Although we have left the analysis of the data to the developers of the impedance models that simulate bias flow through perforate, our initial examination indicates that our results follow the trends consistent with published theory on impedance of perforates with a steady bias flow.

Ahuja, K. K.↗
NASA NTRS1994

Comparison of upper tropospheric water vapor from GOES, Raman lidar, and Cross-chain Loran Atmospheric Sounding System measurements

Observations of upper tropospheric relative humidity obtained from Raman lidar and Cross-chain Loran Atmospheric Sounding System (CLASS) sonde instruments obtained during the First ISCCP Regional Experiment (FIRE) Cirrus-II field program are compared with satellite measurements from the GOES 6.7-micron channel. The 6.7-micron channel is sensitive to water vapor integrated over a broad layer in the upper troposphere (roughly 500-200 mbar). Instantaneous measurements of the upper tropospheric relative humidity from GOES are shown to agree to within roughly 6% of the nearest lidar observations and 9% of the nearest CLASS observations. The CLASS data exhibit a slight yet systematic dry bias in upper tropospheric humidity, a result which is consistent with previous radiosonde intercomparisons. Temporal stratification of the CLASS data indicates that the magnitude of the bias is dependent upon the time of day, suggesting a solar heating effect in the radiosonde sensor. Using CLASS profiles, the impact of vertical variability in relative humidity upon the GOES upper tropospheric humidity measurements is also examined. The upper tropospheric humidity inferred from the GOES 6.7-micron channel is demonstrated to agree to within roughly 5% of the relative humidity vertically averaged over the depth of atmosphere to which the 6.7-micron channel is sensitive. The results of this study encourage the use of satellite measurements in the 6.7-micron channel to quantitatively describe the distribution and temporal evolution of the upper tropospheric humidity field.

Soden, B. J.↗
NASA NTRS1994

The accuracy of temperature distributions used to derive the net transport for a zonally averaged model

Comparisons of satellite-derived temperatures with correlative temperatures indicate that the LIMS temperatures are accurate and contain more of the needed vertical resolution for calculating a residual mean circulation for transporting tracer-like species. Generally, the LIMS temperatures are accurate to at least 2 K. Other satellite data sets are comprised of temperatures with coarser vertical resolution, leading to biases that occur with an error pattern that is characteristic of their resolution. Their biases exceed 2 K at some altitudes. Retrievals of species using an infrared limb emission technique are sensitive to any temperature bias. Generally, the IMS comparisons with other data sets for ozone and water vapor are good to better than 20 percent; this represents an independent confirmation of the quality of LIMS and temperatures. Zonal mean comparisons between LIMS and SAMS temperatures also indicate agreement to better than 2 K from about 7 to 2hPa. Therefore, we are confident that SAMS N2O and CH4 are relatively free of temperature bias in that region. These factors support the generally good agreement in G90 between model N2O transported using a LIMS-derived RMC and the N2O contours from SAMS.

Remsberg, Ellis E.↗
NASA NTRS1978

Processing laser velocimeter high-speed burst counter data

The statistical calculations of the mean and potential bias errors are considered with respect to their applicability to the randomly sampled velocity data obtained from a laser velocimeter. Techniques for displaying the resulting data in the form of arrow plots, flow streamline plots, and contour maps are also discussed. A description is presented of the operation of a new hardware interface between the signal processing electronics and the computer. The interface has been constructed to make it possible to use recently developed data processing techniques for obtaining turbulent power spectra from laser velocimeter data. Additional capabilities which were built into the interface allow the measurement of the velocity vector for each particle passing through the laser velocimeter sample window. By processing the data with extended versions of the turbulent power spectra techniques, cross spectra, interactions between velocity magnitude fluctuations, and flow angle fluctuations may be investigated.

Meyers, J. F.↗
NASA NTRS1979

Orbit/attitude estimation with LANDSAT Landmark data

The use of LANDSAT landmark data for orbit/attitude and camera bias estimation was studied. The preliminary results of these investigations are presented. The Goddard Trajectory Determination System (GTDS) error analysis capability was used to perform error analysis studies. A number of questions were addressed including parameter observability and sensitivity, effects on the solve-for parameter errors of data span, density, and distribution an a priori covariance weighting. The use of the GTDS differential correction capability with acutal landmark data was examined. The rms line and element observation residuals were studied as a function of the solve-for parameter set, a priori covariance weighting, force model, attitude model and data characteristics. Sample results are presented. Finally, verfication and preliminary system evaluation of the LANDSAT NAVPAK system for sequential (extended Kalman Filter) estimation of orbit, and camera bias parameters is given.

Hall, D. L.↗
DOE OSTI2024

Bias Correcting NOAA's High-Resolution Rapid Refresh (HRRR) Wind Resource Data for Grid Integration Applications [Slides]

Many weather years of high-quality wind data are widely accepted in the grid integration community to be important for studying wind energy technical potential, energy system operations, and grid resilience. NREL makes high-quality wind and solar resource data available. NREL's Grid-Atmosphere workshop (March 2024) identified NREL National Solar Radiation Database as widely used in grid integration modeling, but there is less agreement on commonly used wind datasets. One important factor identified by ESIG's 2023 report 'Weather Dataset Needs for Planning and Analyzing Modern Power Systems' for gold standard wind data is regular updates. To address the need for regular updates, NREL's team can now process all currently available and regularly updated High-Resolution Rapid Refresh (HRRR) outputs. HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). One barrier to NREL using HRRR is systematic bias and consistency with NREL's existing wind datasets (e.g. WIND Toolkit, 'WTK') across weather years. To address this barrier, we show that the HRRR can be interpolated and bias-corrected to be consistent with NRE's existing datasets. We call the new dataset BC-HRRR (bias-corrected HRRR). As with historical datasets like the WTK, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling). BC-HRRR's (2015-present) consistency with WTK (2007-2013) allows NREL to extend internal grid integration tooling with 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. The rest of this slide deck documents the BC-HRRR processing methods, validation, and its implications for intended use.

17 WIND ENERGY↗