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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

On the Positive Bias of Peak Horizontal Velocity from an Idealized Doppler Profiler

In the presence of 3-D turbulence, peak horizontal velocity estimates from an idealized Doppler profiler are found to be positively biased due to an incomplete specification of the vertical velocity field. The magnitude of the bias was estimated by assuming that the vertical and horizontal velocities can be separated into average and perturbation values and that the vertical and horizontal velocity perturbations are normally distributed. Under these assumptions, properties of the Type-I Extreme Value Distribution for maxima, known as the Gumbel distribution, can be used to obtain an analytical solution of the bias. The bias depends on geometric properties of the profiler configuration, the variance in the horizontal velocity, and the unresolved variance in the vertical velocity. When these variances are normalized by the average horizontal velocity, the bias can be mapped as a simple function of the normalized variances.

Short, David A.↗
NASA NTRS2015

Resolving Ice Cloud Optical Thickness Biases Between CALIOP and MODIS Using Infrared Retrievals

Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of two bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g approx. = 0.75 in the mid-visible spectrum, 5-15% smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products.This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28%), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

Cloud↗
NASA NTRS2019

A Systematic Approach to Isolating the Causes and Impacts of Climate Model Bias Employing Analysis Increments

One of the most promising approaches to isolating the causes and understanding the impacts of climate model formulation errors is through the utilization of information contained in short-term forecast errors. The underlying assumption being that by capturing the errors at the very early stages of growth (before nonlinearities develop), we should be able to associate those errors with specific deficiencies in the model's formulation of the relevant physical processes. Here, we provide an example of utilizing the long-term mean of the 6-hourly analysis increments (first guess forecast minus analysis) produced by MERRA-2 to correct the NASA/GEOS AGCM over well-defined regions, thereby allowing us to quantify how the reduced tendency errors in these regions manifest themselves both locally and remotely through large-scale teleconnections to reduce the model's climatological biases. We extend previous work (focused on boreal summer) to address the full annual cycle of model bias. A key result is the large seasonality of the errors over the Tibet region and their impacts on the Northern Hemisphere jet biases, and related precipitation and temperature biases over North American. Attempts to correct the phase and amplitude errors of the climatological boreal winter ridge over western North America (a likely key requirement for improving winter climate forecasts over North America) reveal a surprising complexity in the sources of the errors in what is otherwise manifest as a rather simple PNA-like bias structure.

Schubert, S.↗
NASA NTRS2021

Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction with Satellite Data for Rural Areas

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS-Chem, WRF-Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non rural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two-part study develops a multimodel and multi-AOD bias-correction technique that has the potential to improve PM2.5 forecasts in both rural and non rural areas in near real time, and be readily implemented at state levels.

Huanxin Zhang↗
DOE OSTI2015

Bias in Dynamic Monte Carlo Alpha Calculations

A 1/N bias in the estimate of the neutron time-constant (commonly denoted as α) has been seen in dynamic neutronic calculations performed with MCATK. In this paper we show that the bias is most likely caused by taking the logarithm of a stochastic quantity. We also investigate the known bias due to the particle population control method used in MCATK. We conclude that this bias due to the particle population control method is negligible compared to other sources of bias.

42 ENGINEERING↗
DOE OSTI2015

A Perpendicular Biased 2nd Harmonic Cavity for the Fermilab Booster

A perpendicular biased 2nd harmonic cavity is currently being designed for the Fermilab Booster. Its purpose cavity is to flatten the bucket at injection and thus change the longitudinal beam distribution so that space charge effects are decreased. It can also with transition crossing. The reason for the choice of perpendicular biasing over parallel biasing is that the Q of the cavity is much higher and thus allows the accelerating voltage to be a factor of two higher than a similar parallel biased cavity. This cavity will also provide a higher accelerating voltage per meter than the present folded transmission line cavity. However, this type of cavity presents technical challenges that need to be addressed. The two major issues are cooling of the garnet material from the effects of the RF and the cavity itself from eddy current heating because of the 15 Hz bias field ramp. This paper will address the technical challenge of preventing the garnet from overheating.

Tan, C. Y.↗
DOE OSTI2016

Spiral biasing adaptor for use in Si drift detectors and Si drift detector arrays

A drift detector array, preferably a silicon drift detector (SDD) array, that uses a low current biasing adaptor is disclosed. The biasing adaptor is customizable for any desired geometry of the drift detector single cell with minimum drift time of carriers. The biasing adaptor has spiral shaped ion-implants that generate the desired voltage profile. The biasing adaptor can be processed on the same wafer as the drift detector array and only one biasing adaptor chip/side is needed for one drift detector array to generate the voltage profiles on the front side and back side of the detector array.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Effect of Inverter Loading Ratio on Energy Estimate Bias: Preprint

Subhourly effects, particularly variability in solar irradiance, can lead to underestimation of inverter clipping losses and overestimation of energy in hourly photovoltaic system performance models, particularly for systems with high inverter loading ratios. Direct simulation of this error can be complicated by factors such as the representation of spatial and temporal variability in hourly weather data and transient system conditions. In this work we take an alternative approach using real system power measurements to show that energy predictions from typical industry models suffer from a bias that increases with inverter loading ratio. We also show that this loading ratio-dependent bias is strongly correlated with an empirical subhourly inverter clipping bias derived from real power plant data. Finally, we show that this bias is not necessarily specific to any one model or weather dataset by recreating similar biases with alternatives of each.

clipping↗
DOE OSTI2017

Validation and bias quantification of criticality safety codes for SRNS operations

The validation and bias quantification methods employed by the Criticality Safety Engineering group at Savannah River Nuclear Solutions (SRNS) are discussed. Both SCALE and MCNP are validated against benchmark experiments, where these benchmark models have been divided into subgroups representative of the types of fissile material operations at SRNS. A quantification of the bias is made for each subgroup for each code and the three methods employed for quantification are discussed. The bias information is employed directly in the establishment of the Upper Safety Limit (USL) value of the neutron multiplication factor when using standardized documented SRNS methods. A simple example of validation and bias quantification is given and the procedure for setting the USL using the derived bias information is also presented. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗
DOE OSTI2016

Impact of Spatial Discretization on Reactivity Biases in Depleted TRIGA Fuel

Ongoing work at Kansas State University (KSU) and the University of Wisconsin involves the determination of fuel compositions in TRIGA reactors. Modeling of depletion requires a number of approximations to be made, which often includes selection of an appropriate spatial discretization for tracking material evolution in time. Incorporation of temperature feedback also requires such spatial zoning. Any approximation made leads to computational biases, i.e., differences between computed and accurate values of some parameter of interest. The objective of this study is to understand the potential magnitude of biases introduced by the selected spatial discretization. Specifically, biases in the reactivity of a square-pitch unit cell representative of a typical TRIGA fuel element were studied as a function of burnup for different radial and axial discretizations. Temperature effects were also studied by introducing a fixed, radial temperature profile. The results indicate a very fine radial discretization (> 10 radial regions) may be needed to eliminate biases (of statistical significance) from temperature feedback effects, while an axial discretization of {approx_equal}7 regions may be sufficient to eliminate biases due to evolution of the fuel composition. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Exchange Bias Between van der Waals Materials: Tilted Magnetic States and Field-Free Spin–Orbit-Torque Switching

Magnetic van der Waals heterostructures provide a unique platform to study magnetism and spintronics device concepts in the 2D limit. Here, studies of exchange bias from the van der Waals antiferromagnet CrSBr acting on the van der Waals ferromagnet Fe3GeTe2 (FGT) are reported. Additionally, the orientation of the exchange bias is along the in-plane easy axis of CrSBr, perpendicular to the out-of-plane anisotropy of the FGT, inducing a strongly tilted magnetic configuration in the FGT. Furthermore, the in-plane exchange bias provides sufficient symmetry breaking to allow deterministic spin–orbit torque switching of the FGT in CrSBr/FGT/Pt samples at zero applied magnetic field. A minimum thickness of the CrSBr of >10 nm is needed to provide a non-zero exchange bias at 30 K.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A study of reactivity biases and their dependence on spatial discretization in depleted TRIGA fuel

The impact of spatial discretization on reactivity biases in TRIGA fuel models was analyzed here. In particular, unit-cell analyses in 2-D and 3-D were performed using Serpent to understand how spatial discretization affects the accuracy with which the effects of material evolution and temperature feedback are resolved. For temperature-dependent cases, a simple, single-channel model was employed. Analysis of 2-D models showed that essentially no radial discretization resolution is needed to eliminate biases (i.e., to reduce biases to the level of stochastic uncertainties) due to material evolution but that more than eight, equal-area, radial regions are needed to resolve temperature-feedback effects. Analysis of 3-D models showed that at least seven, equal-volume, axial regions may be required to resolve material evolution with temperature feedback leading to insignificant additional bias. Because of memory constraints, a full-core model with radially- and axially-resolved fuel elements may be impractical, especially for production-level analyses. Consequently, an “effective Doppler temperature” was determined empirically as a function of the radially-averaged temperature and may be used for future, full-core analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Evaluation of hydrologic components of community land model 4 and bias identification

Runoff and soil moisture are two key components of the global hydrologic cycle that should be validated at local to global scales in Earth System Models (ESMs) used for climate projection. Here, we have evaluated the runoff and surface soil moisture output by the Community Climate System Model (CCSM) along with 8 other models from the Coupled Model Intercomparison Project (CMIP5) repository using satellite soil moisture observations and stream gauge corrected runoff products. A series of Community Land Model (CLM) runs forced by reanalysis and coupled model outputs was also performed to identify atmospheric drivers of biases and uncertainties in the CCSM. Results indicate that surface soil moisture simulations tend to be positively biased in high latitude areas by most selected CMIP5 models except CCSM, FGOALS, and BCC, which share similar land surface model code. With the exception of GISS, runoff simulations by all selected CMIP5 models were overestimated in mountain ranges and in most of the Arctic region. In general, positive biases in CCSM soil moisture and runoff due to precipitation input error were offset by negative biases induced by temperature input error. Excluding the impact from atmosphere modeling, the global mean of seasonal surface moisture oscillation was out of phase compared to observations in many years during 1985–2004. The CLM also underestimated runoff in the Amazon, central Africa, and south Asia, where soils all have high clay content. We hypothesize that lack of a macropore flow mechanism is partially responsible for this underestimation. However, runoff was overestimated in the areas covered by volcanic ash soils (i.e., Andisols), which might be associated with poor soil porosity representation in CLM. Finally, our results indicate that CCSM predictability of hydrology could be improved by addressing the compensating errors associated with precipitation and temperature and updating the CLM soil representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Antiferromagnet thickness dependence and rotatable spins in exchange biased CoO/Fe films

The emergence of exchange bias and coercivity enhancement has been investigated in epitaxial CoO/Fe films with varied antiferromagnet (AF) thicknesses, even smaller than the critical value where the frozen CoO spins are detectable. Vector magnetometry and first-order reversal curve (FORC) measurements reveal different CoO thickness dependence of the exchange bias and coercivity enhancement, including the evolution of magnetization reversal from a high coercivity, low bias phase due to rotatable CoO moments to a high bias, low coercivity phase due to frozen CoO moments. Furthermore, the AF domain state is found to be metastable, which can be reoriented by external and exchange fields prior to the appearance of frozen spins, pointing to a generic origin of the training effect. Monte Carlo simulations show that the AF anisotropy energy barrier and the rotatable spins induced by magnetic field and exchange interaction at the interface are responsible for the observed effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Capture efficiency and bias from the defect dynamics near grain boundaries in BCC Fe using mesoscale simulations

The capture efficiency describes the capability of a sink, such as a grain boundary (GB), dislocation, and void, to absorb point defects (PDs). The bias defines the difference in capture efficiency between the absorption of a vacancy and dumbbell at a sink. Complete kinetic information on PDs, including diffusion barriers and diffusion orientations, as well as accurate saddle points, are needed to determine the capture efficiency and bias at a sink accurately, which is computationally demanding. Here, in the present study, the Self-Evolving Atomistic Kinetic Monte Carlo (SEAKMC) method was used to investigate the defect dynamics of PDs near different types of grain boundaries (GBs) (with both $\langle$100$\rangle$ and $\langle$110$\rangle$ families) accurately in body-centered cubic (BCC) iron (Fe). The capture efficiency, sink strength, and bias factor of different types of GBs were determined in Fe, which, different from traditional rate theory estimation, showed a distinct capture efficiency, sink strength, and bias in different GBs. The results demonstrate a strong positive correlation between the capture efficiency and the GB strain width, instead of the GB misorientation, GB energy, or GB-PD binding energy, which have been investigated previously. This work provides valuable insight into the radiation-induced microstructural evolution of GBs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Electrochemical Reactions Under Reverse Bias Create Additional Mobile Ions That Enable Hole Tunneling in Metal Halide Perovskite Diodes

Gradual reverse-bias breakdown in metal-halide perovskite diodes and solar cells is thought to originate from hole tunneling through steep bands in an ionic depletion region near the electron-transport layer after positively charged iodine vacancies accumulate near the hole-transport layer (HTL). However, typical reported mobile-ion concentrations near 1 x 10^17 cm-3 are too small to quantitatively explain significant tunneling-current densities and (Zener) breakdown observed near -5 V. Here, we show that inferred mobile-ion concentrations increase by more than 100x, to over 1 x 10^18cm-3 , within just 3 min of reverse bias at -6.0 V in p-i-n perovskite diodes. We attribute this increase to iodide oxidation and coupled iodine vacancy creation that must be balanced by reduction reactions near the HTL. Sub-optimal HTL coverage leads to direct contact between the transparent conducting electrode and perovskite, facilitates reduction events, enables the creation of even larger inferred mobile-ion concentrations (~1 x 10^19cm-3 ), and leads to faster degradation under reverse bias. This explains previous work that showed increased breakdown voltages and improved reverse-bias stability by implementing thick, uniform HTLs.

14 SOLAR ENERGY↗
DOE OSTI2025

Mitigating spectral bias in neural operators via high-frequency scaling for physical systems

Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes, which are present in multiscale physical systems. Therefore, they tend to produce over-smoothed solutions, which is particularly problematic in modeling turbulence and for systems with intricate patterns and sharp gradients such as multi-phase flow systems. In this work, we introduce a new approach named high-frequency scaling (HFS) to mitigate spectral bias in convolutional-based neural operators. By integrating HFS with proper variants of UNet, we demonstrate a higher prediction accuracy by mitigating spectral bias in single and two-phase flow problems. Unlike Fourierbased techniques, HFS is directly applied to the latent space, thus eliminating the computational cost associated with the Fourier transform. Additionally, we investigate alternative spectral bias mitigation through a diffusion model conditioned on neural operators. While the diffusion model integrated with the standard neural operator may still suffer from significant errors, these errors are substantially reduced when the diffusion model is integrated with a HFS-enhanced neural operator.

97 MATHEMATICS AND COMPUTING↗