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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
DOE OSTI2019

Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties

Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.

58 GEOSCIENCES↗
DOE OSTI2019

The impact of reliable range estimation on battery electric vehicle feasibility

Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range or when their journeys were unexpectedly extended. This paper quantifies the impact of reliable range estimation on BEV feasibility using GPS-tracked travel survey data, collected over an 18-month period (from November 2004 to April 2006) in the Seattle metropolitan area. BEV feasibility is quantified as the number of days when travel adaption is needed if a driver replaces a conventional gasoline vehicle (CGV) with a BEV. The distribution of BEV range is estimated based on the real-world fuel efficiency data. A driver is assumed to choose between using a BEV or a substitute gasoline vehicle, based on the cumulative prospect theory (CPT). BEV is considered feasible for a particular driver if he/she needs to use a substitute vehicle on less than 0.5% of the travel days. By varying the values of some CPT parameter, the percentage of BEV feasible vehicles could change from less than 5% to 25%. Additionally, the numerical results show that with a 50% reduction in the standard deviation and 50% increase in the mean of the BEV range distribution BEV feasibility increases from less than 5% of the sampled drivers to 30%.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2021

Composited structure of non-precipitating shallow cumulus clouds

The normalized distributions of thermodynamic and dynamical variables both within and outside shallow clouds are investigated through a composite algorithm using large-eddy simulations of oceanic and continental cases. The normalized magnitude is maximum near the cloud centre and decreases outwards. While relative humidity (RH) and cloud liquid water ( q l ) decrease smoothly to match the environment, the vertical velocity, virtual potential temperature (θ v ), and potential temperature (θ) perturbations have more complicated behaviour towards the cloud boundary. Below the inversion layer, θ' v becomes negative before the vertical velocity has turned from an updraft to a subsiding shell outside the cloud, indicating the presence of a transition zone where the updraft is negatively buoyant. Due to the downdraft outside the cloud and enhanced horizontal turbulent mixing across the edge, the normalized turbulent kinetic energy (TKE) and horizontal turbulent kinetic energy (HTKE) decrease more slowly from the cloud centre outwards than the thermodynamic variables. The distributions all present asymmetric structures in response to the vertical wind shear, with more negatively buoyant air, stronger downdrafts, and larger TKE on the downshear side. We discuss several implications of the distributions for theoretical models and parameterizations. Positive buoyancy near the cloud base is mostly due to the virtual effect of water vapour, emphasizing the role of moisture in triggering. The mean vertical velocity is found to be approximately half the maximum vertical velocity within each cloud, providing a constraint to achieve possible power-law distributions for some models. Finally, the normalized distributions for different variables are used to estimate the vertical heat and moisture fluxes within clouds. The results suggest that distributions near the cloud edge and variability of maximum perturbations need careful treatment. The fluxes are underestimated in the inversion layer because cloud-top downdrafts cannot be captured well.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mycorrhizal Distributions Impact Global Patterns of Carbon and Nutrient Cycling

Most tree species predominantly associate with a single type of mycorrhizal fungi, which can differentially affect plant nutrient acquisition and biogeochemical cycling. Uncertainties in mycorrhizal distributions are non-trivial, and current estimates disagree in up to 50% over 40% of the land area, including tropical forests. Remote sensing capabilities for mycorrhizal detection show promise for refining these estimates further. Here, we address for the first time the impact of mycorrhizal distributions on global carbon and nutrient cycling. Using the state-of-the-art carbon-nitrogen economics within the Community Land Model version 5, we found Net Primary Productivity (NPP) increased throughout the 21st century by 20%; however, as soil nitrogen has progressively become limiting, the costs to NPP for nitrogen acquisition—that is, to mycorrhizae—have increased at a faster rate by 60%. This suggests that nutrient acquisition will increasingly demand a higher portion of assimilated carbon to support the same productivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast

To improve the energy system resilience and economic efficiency, the wind power as a renewable energy starts to be deeply integrated into smart power grids. However, the wind power forecast uncertainty brings operational challenges. In order to provide a reliable guidance on operational decisions, in this paper, we propose a short-term wind power probabilistic forecast. Specifically, to model the rich dynamic behaviors of underlying physical wind power stochastic process occurring in various meteorological conditions, we first introduce an infinite Markov switching autoregressive model. This nonparametric time series model can capture the important properties in the real world data to improve the prediction accuracy. Then, given finite historical data, the posterior distribution of flexible forecast model can correctly quantify the model estimation uncertainty. Built on it, we develop the posterior predictive distribution to rigorously quantify the overall forecasting uncertainty accounting for both inherent stochastic uncertainty and model estimation error. Furthermore, the proposed approach can provide accurate and reliable short-term wind power probabilistic forecast, which can be used to support smart power grids real-time risk management.

17 WIND ENERGY↗
DOE OSTI2023

Effects of wind field characteristics on pitch bearing reliability: a case study of 5 MW reference wind turbine at onshore and offshore sites; Auswirkungen von Windfeldeigenschaften auf die Zuverlässigkeit von Rotorblattlagern: eine Fallstudie über eine 5-MW-Referenz-Windkraftanlage an Onshore- und Offshore-Standorten

This paper presents a study on pitch bearing basic rating life affected by wind field characteristics at both onshore and offshore wind sites. The National Renewable Energy Laboratory 5 MW reference wind turbine is selected for the study. Wind field characteristics including reference hub height mean wind speed, wind speed distribution, wind shear, and vertical inflow are studied. A decoupled approach is employed where global analysis is performed first. Second, the load effects from the global analysis are applied on a reference pitch bearing designed based on best industrial practices. For the case study onshore site, it is found that the Kernel density estimation best fits the wind distribution, while the International Electrotechnical Commission proposed distribution appears to be not suitable. Moreover, it is shown that the seed number has high effect on the bearing life in turbulence wind and the wind speeds around rated have the highest contribution in both bearing fatigue damage and extreme load failure. The results contribute to better understanding of the wind field characteristics on the pitch bearing life.

17 WIND ENERGY↗
DOE OSTI2019

Quantifying Rates of Erosion Using Historical Aerial Photos - 19478

Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. In this work multi-temporal historical aerial images are analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. Scans of photographic negatives were obtained for multiple photographs and several negatives were scanned with a calibrated photogrammetric scanner by Geomni (formerly Blue Skies Consulting) in Belen, New Mexico. Other negatives were provided by USDA or scanned on a high-resolution, large format media scanner in the United States National Archives Cartographic Research Room, College Park, Maryland (1939). An additional set of aerial photographs was obtained from the USGS EROS Data Center web site. For other years, photographic prints were available for aerial photography. Several prints were scanned with a large-format media scanner at 1200 dpi. Ground control points for selected digital images used in the stereoscopic analyses were identified in ArcMap. Ground control points were collected by matching features which could be identified in the aerial photography as well as the orthophotography or shaded relief imagery from the Lidar dataset. Such features included fence line intersections, road intersections, railroad bridges, and structures, depending on the age of the photography. Many old fence lines and abandoned roads remained visible in the shaded relief imagery while they were completely obscured by tree canopy in modern photography. Ground control elevations were sampled from the Lidar-based DEM or directly from point clouds. The image coordinates and matching three-dimensional projected coordinates of the ground control points were exported to IDL where parameters for a three-dimensional direct linear transformation (DLT) were obtained. Built-in singular value decomposition routines were used to solve an overdetermined set of equations for the eleven parameters of the DLT for a particular historical photograph. A solution to the DLT parameters was obtained for each historical aerial photograph in which features of interest were identified using the stereoscope. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. There was inevitable uncertainty associated with the estimation of points from the historical aerial imagery. This includes estimation of changes in locations of features like gully heads through time. In this work a method to estimate the uncertainty associated with measurements of locations of features using the stereoscopic analysis is developed and applied. For features with locations in space that do not change (e.g., road intersections, etc.) the difference in measurements of locations from the Lidar dataset and that of the estimate from the historical aerial image provides an observation of the measurement error. These points used to estimate the measurement error are referred to as control points. For each of the historical aerial images, a different measurement error distribution is estimated using control points. These measurement errors are combined with the estimated distances from the stereoscopic analyses to produce distributions of gully retreat rates and hillslope erosion rates used along with output from Erosion Working Group (EWG) Landscape Evolution Models (LEMs) to provide statistical probability distributions for input into the PPA Model. Specifically, gully head retreat rate and gully widening rate were characterized using statistical probability distributions. The central limit theorem was then applied to develop rates that represent long-term averages that are used to inform a PPA Model built in GoldSim. The importance of these rates with respect to characterization of future risk can then assessed using sensitivity analyses. This provides an approach to determine if subsequent reduction of the uncertainty associated with the probability distributions of erosion rates will correspond to meaningful reduction in the estimation of future risk from the site. Ultimately this is a general method that utilizes historical aerial imagery to estimate rates of erosion with uncertainty. This information can be used to inform PPA models to project future risks from a given site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Transit Time Estimation in Catchments: Recent Developments and Future Directions

Abstract Water transit time is now a standard measure in catchment hydrological and ecohydrological research. The last comprehensive review of transit time modeling approaches was published 15+ years ago. But since then the field has largely expanded with new data, theory and applications. Here, we review these new developments with focus on water‐age‐balance approaches and data‐based approaches. We discuss and compare methods including StorAge‐Selection functions, well/partially mixed compartments, water age tracking through spatially distributed models, direct transit time estimates from controlled experiments, young water fractions, and ensemble hydrograph separation. We unify some of the heterogeneity in the literature that has crept in with these many new approaches, in an attempt to clarify the key differences and similarities among them. Finally, we point to open questions in transit time research, including what we still need from theory, models, field work, and community practice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Geometric Tail Approximation for Reliability and Survivability

A common problem in developing high-reliability systems is estimating the reliability for a population of components that cannot be 100% tested. The radiation survivability of a population of components is often estimated by testing a very small sample to some multiple of the required specification level, known as an overtest. Given a successful test with a sufficient overtest margin, the population of components is assumed to have the required survivability or radiation reliability. However, no mathematical justification for such claims has been crafted without making aggressive assumptions regarding the statistics of the unknown distribution. Here we illustrate a new approach that leverages geometric bounding arguments founded on relatively modest distribution assumptions to produce conservative estimates of component reliability.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Continental Scale Hydrostratigraphy: Comparing Geologically Informed Data Products to Analytical Solutions

Abstract This study synthesizes two different methods for estimating hydraulic conductivity (K) at large scales. We derive analytical approaches that estimate K and apply them to the contiguous United States. We then compare these analytical approaches to three‐dimensional, national gridded K data products and three transmissivity (T) data products developed from publicly available sources. We evaluate these data products using multiple approaches: comparing their statistics qualitatively and quantitatively and with hydrologic model simulations. Some of these datasets were used as inputs for an integrated hydrologic model of the Upper Colorado River Basin and the comparison of the results with observations was used to further evaluate the K data products. Simulated average daily streamflow was compared to daily flow data from 10 USGS stream gages in the domain, and annually averaged simulated groundwater depths are compared to observations from nearly 2000 monitoring wells. We find streamflow predictions from analytically informed simulations to be similar in relative bias and Spearman's rho to the geologically informed simulations. R ‐squared values for groundwater depth predictions are close between the best performing analytically and geologically informed simulations at 0.68 and 0.70 respectively, with RMSE values under 10 m. We also show that the analytical approach derived by this study produces estimates of K that are similar in spatial distribution, standard deviation, mean value, and modeling performance to geologically‐informed estimates. The results of this work are used to inform a follow‐on study that tests additional data‐driven approaches in multiple basins within the contiguous United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Energy Release upon Bombardment of a Tungsten Surface by Deuterium Atoms

The depth distribution of the energy release (linear energy transfer) occurring upon bombardment of a tungsten target by deuterium atoms with energies ranging from 100 eV to 10 MeV is calculated. It is demonstrated that, contrary to common belief, the maximum energy release at energies below 100 keV is localized near the surface of the solid. At energies above 100 keV, the shape of the distribution changes, and a Bragg maximum emerges near the particle stopping point. The depth distribution of the energy release in tungsten under conditions typical of the ITER tokamak reactor is determined. This distribution provides an opportunity to estimate the wall heating by incident plasma atoms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Effect of graphite morphology on the tensile strength and thermal conductivity of cast iron

Highlights: • The graphite morphology is characterized from 3D structure using X–ray tomography. • The size, distribution, shape and interconnectivity of graphite are estimated. • The influences of graphite morphology on tensile properties and thermal conductivity are evaluated. • The mechanism of tensile strength and the model of thermal conductivity are discussed. - Abstract: Graphite morphology is a very important parameter affecting the properties of cast iron. In this study, the graphite morphology was characterized using both 3D and 2D structures. The experimental results revealed that when the graphite was less spheroidal, the graphite had greater interconnectivity in the 3D structure of the cast iron. The size, distribution, volume fraction, and shape of the graphite were estimated. Moreover, the influences of the graphite's morphology, size, and distribution on the tensile property and thermal conductivity at different temperatures were estimated. The experimental results revealed that with an increase in temperature, the thermal conductivity initially increased, and then decreased, whereas the tensile strength decreased slightly at first, and then decreased rapidly. In addition, with an increase in the percentage of compacted graphite, the tensile strength decreased and the thermal conductivity increased. The fracture surfaces of tensile specimens were also estimated. Finally, the mechanism of tensile strength and the model of thermal conductivity were investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The Two-Particle Correlation Function for Systems with Long-Range Interactions

In this paper, we study the truncated two-particle correlation function in particle systems with long range interactions. For Coulombian and soft potentials, we define and give well-posedness results for the equilibrium correlations. In the Coulombian case, we prove the onset of the Debye screening length in the equilibrium correlations, for suitable velocity distributions. Additionally, we give precise estimates on the effective range of interaction between particles. In the case of soft potential interaction the equilibrium correlations and their fluxes in the space of velocities are shown to be linearly stable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A novel risk analysis methodology to evaluate the economic performance of a biorefinery and to quantify the economic incentives for participating biomass producers

In this work, a novel risk analysis methodology is presented to evaluate the economic performance of a biorefinery given the volatility in the market price of the final product and the variability in the biomass delivered cost. In addition, potential economic incentives for participating biomass producers are quantified for different farm participation rates. The Monte Carlo simulation model, IBSAL-MC, is used to estimate the biomass delivered cost distribution, and a modified risk heat map is used to visualize the expected return on investment (ROI) for various combinations of the market price of the final product and the biomass delivered cost. The developed methodology is applied to an under-construction cellulosic sugar facility located in Sarnia, southwestern Ontario. Four farm participation rates of 20% (base-case scenario), 30%, 40% and 50% are studied. Additionally, the results show that the expected annual ROI for the base-case scenario is estimated to be 1.3%. As the farm participation rate increases, the expected annual ROI increases from 1.3% at 20% farm participation rate to 3.4%, 4.6% and 5.1% at 30%, 40% and 50% rates, respectively. At high sugar market prices ($375–$525/tonne), the overall expected annual ROI increases to 9.5%, 11.4%, 12.6% and 13.0% in 20%, 30%, 40% and 50% farm participation rates, respectively. In this case, the economic incentives to share with biomass producers are estimated to be $14.10/dry tonne (dt), $15.77/dt and $16.33/dt by increasing the farm participation rate from 20% to 30%, 40% and 50%, respectively.

09 BIOMASS FUELS↗
DOE OSTI2021

Assessment of tritium effluent from Prototype Generation IV Sodium-cooled Fast Reactor

We report a tritium transport model for a pool-type sodium cooled fast reactor was developed for estimation of tritium effluents from the Prototype Generation IV Sodium-cooled Fast Reactor (PGSFR). For this purpose, the transport model developed for Experimental Breeder Reactor II and Fast Flux Test Facility was updated and validated. The tritium production rate from ternary fission and activations in PGSFR were calculated by the Argonne Reactor Computation (ARC) code suite for fast reactor analysis, and due to the potential uncertainties in source terms and variations of cold trap performance and material permeability during reactor operation, the tritium distributions and tritium effluents were estimated for several bounding cases. The upper bounding gaseous and liquid tritium release rates from PGSFR are similar to ~440.7 Ci/year and 14.7 Ci/year, respectively. The gaseous tritium effluent is about few orders of magnitude smaller than the regulatory constraint of the tritium concentration in the air. The peak tritium concentration in the steam-generator feedwater is similar to ~1.1x10 -3 mu Ci/g, which is required to be diluted prior to releasing as tritiated water. The accumulated tritium in the cold trap after five cycle operations is ~ 52,000 Ci. Because of significant conservatism in these upper bounding estimations, it is expected that the tritium effluent at the nominal case with plausible source terms would be about 2 orders of magnitude smaller than that of the upper bounding case.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗