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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

SCALE 6.3 Validation: Radiation Shielding

Safe and reliable use of scientific and engineering computer codes requires validation for the types of applications in which they will be used. An example in the nuclear reactor engineering and licensing field is radiation transport employed in shielding analyses. The validity of computer codes for shielding applications is demonstrated in this report for SCALE version 6.3.0. Representative benchmarks corresponding to shielding analyses are selected for the validation study. Typical measurement results analyzed from these benchmarks include neutron fluxes, detector count rates, detector energy response functions, neutron and gamma dose rates, neutron activation rates and activities, neutron leakage fluxes, and skyshine dose rates. Thousands of points of comparison between measurement and calculation are presented in this work. Other than rare outliers typically explained by either a lack of information or large uncertainties in the experiment conditions, material, or dimensions, the Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) radiation transport computer code with built-in variance reduction methods distributed with the SCALE computer code system agrees well with the measurement results. In selected benchmarks, MAVRIC is also compared to Monte Carlo N- Particle® (MCNP® ) 1 calculations. Both computer codes generally agree well within the estimated uncertainties. With the release of SCALE 6.3.0, Shift was integrated as an alternative transport solver in MAVRIC, denoted MAVRIC-Shift. Although the traditional MAVRIC using Monaco was used primarily in this validation study, many results have also been generated using MAVRIC-Shift. Agreement between MAVRIC-Monaco and MAVRIC-Shift is generally very good. The benchmarks presented in this report were obtained from reliable sources such as the International Criticality Safety Benchmark Evaluation Project Handbook, the Shielding Integral Benchmark Archive & Database, and other shielding validation work found in the literature. Additional datapoints and benchmarks will be added to future versions of this report to expand the shielding validation suite.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Towards a Robust Sampling Approach: A Computational Review and Design

As pointed out in several other works, the estimation of the reliability of the electrical grid can not be conducted without the estimation of the stochastic phenomena of electicity demand and electricity production by variable renewable sources. Therefore, sampling procedures have become integral in the design of engineering structures and analysis. Commonly referred to as Monte Carlo uncertainty analysis or integration, the general objective of these procedures is to establish specifics about the uncertainty of an output characteristic of such an engineering system, given uncertainty about its input characteristics. These sampling procedures are applied in a context in which establishing such specifics cannot be performed through other means. Variance-reduction techniques are designed to lessen the variability among estimators to estimate statistics of those output uncertainties. Importance-sampling techniques, on the other hand, are designed to reduce the number of samples needed to estimate a particular statistic—e.g., a tail probability. The combination of these approaches can reduce the computational burden considerably for a particular estimator and statistic. Importance sampling—geared and designed as it is toward improving a particular estimator—suffers, unfortunately, from the unintended consequence of reducing the performance of other estimators in terms of their variance. The objective of this paper is to offer an alternative sampling procedure where this variance does not grow unacceptably large for a suite of estimators. Moreover, it is anticipated that, with additional knowledge of how an engineering output characteristic responds to its input characteristics, tuning parameters of the input’s sampling procedure can be set to improve the output characteristic’s estimation. This work proves the effectiveness of the suggested alternative approach. Such positive outcome will lead to a decrease of the computational burden of performing stochastic optimization of integrated energy systems (e.g., components dispatch, and portfolio composition). In particular, capturing the contribution to the overall system cost of rare and unlikely events and patterns of the electricity demand and production will become less computationally expensive. This is due to the fact that the approach demonstrated here will allow the sampling of those rare occurrences more frequently without misrepresenting their probabilistic impacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

An exploration of benchtop X–ray emission spectroscopy for precise characterization of the sulfur redox state in cementitious materials

The evolution of sulfur chemistry in cements is best known in the bailiwick of failure mechanisms via sulfate attack but is equally important for its contributions to the reduction capacity of cementitious materials often used for immobilizing nuclear waste streams destined for long-term storage, e.g., cementitious waste forms (CWF). The total reduction capacity of CWFs, encompassing contributions from both S and Fe reductants, and its implications toward radionuclide immobilization is most often studied by destructive wet chemistry methods requiring acid digestion in the presence of Ce(IV) and subsequent titration and colorimetric interpretation. Here, we investigate a similarly analytical but nondestructive alternative, benchtop high resolution wavelength-dispersive x-ray fluorescence spectroscopy, most commonly known as x-ray emission spectroscopy (XES), for probing the bulk sulfur oxidation state distribution. We present here an initial investigation of S XES, including an improved experimental protocol for lab XES of inhomogeneous samples, both as a complement to the Ce(IV) test and for new scientific opportunities that it enables for observing changes in sulfur chemistry. We discuss future improvements and opportunities, including: (1) the practical challenges associated with coordinating XES and Ce(IV) liquid extraction for a more comprehensive perspective on reduction capacity and for a high-precision evaluation of uncertainties in the Ce(IV) test; and (2) new opportunities, due to the nondestructive nature of XES, for controlled evolution studies aimed at elucidating specific chemical responses of CWF’s exposed to invasive gas or liquid species or to accelerated aging by radiative dose or thermal treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Has Reducing Ship Emissions Brought Forward Global Warming?

Abstract Ships brighten low marine clouds from emissions of sulfur and aerosols, resulting in visible “ship tracks”. In 2020, new shipping regulations mandated an ∼80% reduction in the allowed fuel sulfur content. Recent observations indicate that visible ship tracks have decreased. Model simulations indicate that since 2020 shipping regulations have induced a net radiative forcing of +0.12 Wm −2 . Analysis of recent temperature anomalies indicates Northern Hemisphere surface temperature anomalies in 2022–2023 are correlated with observed cloud radiative forcing and the cloud radiative forcing is spatially correlated with the simulated radiative forcing from the 2020 shipping emission changes. Shipping emissions changes could be accelerating global warming. To better constrain these estimates, better access to ship position data and understanding of ship aerosol emissions are needed. Understanding the risks and benefits of emissions reductions and the difficultly in robust attribution highlights the large uncertainty in attributing proposed deliberate climate intervention.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Modeling Large Dust Aerosols in the Community Earth System Model Version 2 (CESM2)

Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.

Li, Longlei [Cornell Univ., Ithaca, NY (United Sta↗
DOE OSTI2025

Measurements of radial neutral density profiles from Balmer- α emission in Wendelstein 7-X

Radial neutral density profiles are estimated from measurements of passive H α emission in the Wendelstein 7-X stellarator. To parametrize the generally three-dimensional distribution with a low number of degrees of freedom, the neutral density is reduced to a flux surface quantity. Accounting for emission from excitation and recombination processes, neutral density profiles are derived independently for each of the available lines of sight. Density profiles obtained from the different viewing geometries are found to vary within one order of magnitude. Toroidally oriented lines of sight predict systematically lower neutral densities when compared to poloidally oriented ones. This discrepancy is attributed to the simplifications inherent in the imposed model and significant differences in integration volumes across the viewing geometries. In line with expectations, obtained neutral densities are found to decrease with increasing plasma density. Key restrictions of the model include the reduction of the neutral density to a flux surface quantity, uncertainties in the plasma profiles and instrument function, and line integration effects outside the last closed flux surface.

Wendelstein 7-X↗
DOE OSTI2024

Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness

Abstract We present a systematic study demonstrating the impact of lattice QCD data on the extraction of generalised parton distributions (GPDs). For this purpose, we use a previously developed modelling of GPDs based on machine learning techniques fulfilling the theoretical requirements of polynomiality, a form of positivity constraint and known reduction limits. A special care is given to estimate the uncertainty stemming from the ill-posed character of the connection between GPDs and the experimental processes usually considered to constrain them, like deeply virtual Compton scattering (DVCS). Moke lattice QCD data inputs are included in a Bayesian framework to a prior model based on an Artificial Neural Network. This prior model is fitted to reproduce the most experimentally accessible information of a phenomenological extraction by Goloskokov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate $$\xi $$ ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.

Physics↗
DOE OSTI2023

Selecting representative geological realizations to model subsurface CO 2 storage under uncertainty

Carbon capture and storage (CCS) is one of the quickest and most effective solutions for reducing carbon emissions. The majority of subsurface storage occurs in saline aquifers, for which geological information is lacking which in turn results in geological uncertainty. To evaluate uncertainty in CO 2 injection projections, the use of multiple geological realizations (GRs) has been practiced very commonly. In this approach, hundreds or thousands of high-resolution GRs is used that quickly becomes computationally expensive. This issue can be addressed with representative geological realizations (RGRs) that preserve the uncertainty domain of the ensemble GRs. Here, in this study, we propose the use of unsupervised machine learning (UML) frameworks, including dissimilarity measurement, dimensionality reduction, clustering and sampling algorithms ta select a predetermined number of RGRs. We compare the simulation outputs of the RGR sets and the ensemble using the Kolmogorov–Smirnov (KS) test to select the best UML. The UML frameworks and their associated selection processes are evaluated using a saline aquifer with a single CO 2 injection well and 200 GRs with varying uncertain petrophysical characteristics. The best UML framework is selected to use only 5% of the GRs while maintaining the uncertainty domain of the ensemble GRs. In addition, the best UML framework is tested using a saline aquifer with three CO 2 injection wells and varied GRs. The results show that our proposed UML framework can be used to choose RGRs, capturing the whole uncertainty domain. Our approach leads to a significant reduction in the computational cost associated with scenario testing, decision-making, and development planning for CO 2 storage sites under geological uncertainty.

58 GEOSCIENCES↗
DOE OSTI2024

HiFiPDV2 v. 2

SAND2024-01061O The HiFiPDV-2 software is similar to other photonic Doppler velocimetry (PDV) analysis codes in that it converts signals from intensity-time space to frequency-time space using a short-time Fourier transform (STFT). It then analyzes peaks of the resulting power spectral density information to extract the velocity-time history. The unique aspects of HiFiPDV and HiFiPDV-2 are that this reduction process is repeated for an array of reasonable STFT input variables. The resulting population distribution of output velocity-time histories is evaluated to determine the most likely velocity history and its associated uncertainty. This uncertainty is defined as the systematic uncertainty of the PDV signal as it is a function of input values to the reduction process. The HiFiPDV-2 program brings new functionality and efficiency to these calculations—most notably significant reductions in memory usage and calculation times, the ability to evaluate frequency-shifted signals, and the ability to filter out baseline signals. Developers recommend that users have at least 2 GB of RAM per CPU core in their system. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Voorhees, Travis↗
DOE OSTI2021

ACCRUE—An Integral Index for Measuring Experimental Relevance in Support of Neutronic Model Validation

A key challenge for the introduction of any design changes, e.g., advanced fuel concepts, first-of-a-kind nuclear reactor designs, etc., is the cost of the associated experiments, which are required by law to validate the use of computer models for the various stages, starting from conceptual design, to deployment, licensing, operation, and safety. To achieve that, a criterion is needed to decide on whether a given experiment, past or planned, is relevant to the application of interest. This allows the analyst to select the best experiments for the given application leading to the highest measures of confidence for the computer model predictions. The state-of-the-art methods rely on the concept of similarity or representativity, which is a linear Gaussian-based inner-product metric measuring the angle—as weighted by a prior model parameters covariance matrix—between two gradients, one representing the application and the other a single validation experiment. This manuscript emphasizes the concept of experimental relevance which extends the basic similarity index to account for the value accrued from past experiments and the associated experimental uncertainties, both currently missing from the extant similarity methods. Accounting for multiple experiments is key to the overall experimental cost reduction by prescreening for redundant information from multiple equally-relevant experiments as measured by the basic similarity index. Accounting for experimental uncertainties is also important as it allows one to select between two different experimental setups, thus providing for a quantitative basis for sensor selection and optimization. The proposed metric is denoted by ACCRUE, short for Accumulative Correlation Coefficient for Relevance of Uncertainties in Experimental validation. Using a number of criticality experiments for highly enriched fast metal systems and low enriched thermal compound systems with accident tolerant fuel concept, the manuscript will compare the performance of the ACCRUE and basic similarity indices for prioritizing the relevance of a group of experiments to the given application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bringing solar to agriculture: An interdisciplinary design and analysis of a Concord grape agrivoltaic system

Agrivoltaics presents an opportunity to integrate solar photovoltaics (PV) with agricultural production, but crop-specific challenges and operational constraints remain underexplored. This study develops and evaluates a Concord grape agrivoltaic system in the Lake Erie American Viticulture Area, where vineyards face economic pressures and land use conflicts with solar development. Starting with vertical, tracking, and overhead PV systems, we model photosynthetic photon flux density (PPFD) reduction in grapevines and power generation losses from shading using the Agrivoltaic Radiation Tool (ART). Based on these results, which indicate 0.47 % annual grapevine PPFD loss for vertical designs, 1.6 % for tracking, and up to 25 % for the overhead systems, the vertical and tracking designs are selected for further computational fluid dynamics (CFD) analysis to evaluate airflow interactions. CFD results show that vertical panels do not significantly impact airflow through the grapevine canopy, and that tracking systems in horizontal position may enhance airflow compared to a vine-only scenario. Considering operational constraints for tracking systems, the vertical design is selected for an economic evaluation to reveal key financial outcomes for solar developers (14-year payback period) and growers ($408 reduction in financial losses per acre annually). A sensitivity analysis quantifies uncertainty in power generation (±8.8 %) and PPFD (±5.0 %), ensuring model robustness across different vineyard conditions. Furthermore, these findings provide quantitative evidence for the feasibility of Concord grape agrivoltaics, demonstrating a synergistic opportunity for dual-use solar while preserving cultural heritage in grape-growing regions.

14 SOLAR ENERGY↗
DOE OSTI2024

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Nunes, Pedro (ORCID:0000000180012172)↗
DOE OSTI2021

An out-of-distribution-aware autoencoder model for reduced chemical kinetics

While detailed chemical kinetic models have been successful in representing rates of chemical reactions in continuum scale computational fluid dynamics (CFD) simulations, applying the models in simulations for engineering device conditions is computationally prohibitive. To reduce the cost, data-driven methods, e.g., autoencoders, have been used to construct reduced chemical kinetic models for CFD simulations. Despite their success, data-driven methods rely heavily on training data sets and can be unreliable when used in out-of-distribution (OOD) regions (i.e., when extrapolating outside of the training set). In this paper, we present an enhanced autoencoder model for combustion chemical kinetics with uncertainty quantification to enable the detection of model usage in OOD regions, and thereby creating an OOD-aware autoencoder model that contributes to more robust CFD simulations of reacting flows.Here, we first demonstrate the effectiveness of the method in OOD detection in two well-known datasets, MNIST and Fashion-MNIST, in comparison with the deep ensemble method, and then present the OOD-aware autoencoder for reduced chemistry model in syngas combustion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

An Analysis of Future Wind Energy Resources and Cost Uncertainties Across the United States

Wind power is a growing source of energy generation that relies on complex global atmospheric and earth system processes. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run wind energy models with possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States. Changes in mean annual energy production from 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Larger turbines and moderate reductions in system costs can offset the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will change and to what extent system costs can be reduced. An analysis of variance shows, in several states in the Midwest, uncertainty in future wind resources can influence the cost of wind energy nearly as much as uncertainty in future system costs.

17 WIND ENERGY↗
DOE OSTI2025

Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme

The precipitating hydrometeor parameters used in cloud microphysics schemes carry inherent uncertainties. The quantification of these uncertainties, together with parameter optimization, can significantly improve precipitation forecasts. This study investigates the effects of 13 parameters in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, which define the hydrometeor characteristics such as fall velocity–diameter and mass–diameter relationships, as well as the shape parameter of the drop size distribution for precipitating particles such as rain, snow, and graupel on simulated winter precipitation. A comparison between the model's pre-defined parameters and observations from the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign reveals that the fall velocity–diameter relationship for rain, the mass–diameter relationships for snow and graupel, and the shape parameters for all precipitating particles in the WDM6 scheme deviate from the median values observed by the two-dimensional video disdrometer (2DVD). To quantify parameter sensitivities, a perturbed parameter ensemble (PPE) of 256 simulations was conducted within parameter ranges constrained by 2DVD observations for three winter precipitation cases. Bayesian optimization was then applied to identify parameter sets that minimized the root mean square error (RMSE) for each case, achieving reductions of up to 30.2 %. These results demonstrate that ensemble-based uncertainty quantification and parameter optimization can help identify key parameters and provide a pathway to improving precipitation simulation performance. In addition, measurement sites can be strategically selected based on regions that show high sensitivity to variations in hydrometeor characteristic parameters.

Bayesian optimization↗
DOE OSTI2025

Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks

The computational cost of high-fidelity numerical models makes outer-loop analysis, which requires repeated interrogation of the model such as uncertainty quantification, computationally demanding. Multi-fidelity methods, which construct a surrogate model using data from an ensemble of models of varying cost and accuracy, can substantially reduce the cost of outer-loop analysis. However, these methods can be difficult to apply when the model ensemble does not admit a clear hierarchy a priori and the correlations between models are low. Consequently, in this paper, we present a multi-fidelity method that leverages dimension reduction to enhance the correlation between models, thereby reducing the amount of data needed to train a surrogate from an unordered ensemble of models. Our method utilizes basis adaptation to build low-dimensional polynomial chaos expansions of each model and employs Multi-fidelity Networks to encode the relationships among models. We show that the resulting method exhibit two notable advantages over its counterpart: (1) enhanced accuracy (both reduced bias and variance); and (2) reduced dependency on the graph structure encoding relationships among models. We demonstrate the approach on an analytical test problem and a challenging finite element model for a spent nuclear fuel. Our method produces a surrogate model that is significantly more accurate than either a single-fidelity surrogate or a multi-fidelity surrogate constructed without basis adaptation.

42 ENGINEERING↗
DOE OSTI2022

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗
DOE OSTI2021

The WRF-Solar Ensemble Prediction System to Provide Solar Irradiance Probabilistic Forecasts

In this study, we introduce the recently developed WRF-solar ensemble prediction system and a calibration method. The performances of forecast models are evaluated using the National Solar Radiation Database observational analysis for day-ahead solar irradiance predictions. The results demonstrate that the ensemble forecast improves the quality of the forecasts by considering the uncertainty of each ensemble member. The analog ensemble calibration contributed to the reduction of positive bias and an overall improvement in the probabilistic attributes, such as reliability and statistical consistency.

14 SOLAR ENERGY↗