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DOE OSTI2020

Crossing the Streams – Sampler and the TemplateEngine [Slides]

This presentation discusses Sampler, which is a versatile UQ and parametric study tool that can be applied to any SCALE Sequence. Sampler can perturb any quantity in any SCALE input. Recent work at ORNL has developed new types of covariance data that allow Sampler UQ to be applied to nearly all SCALE applications, including reactor depletion, UNF fuel characterization, source term analysis, and decay heat calculation. In SCALE 6.2 releases, CE data in transport cannot be perturbed. Sampler was originally designed for stochastic sampling with any sequence within SCALE and Parametric capability added in SCALE 6.2.2. Sampler can be used for uncertainty quantification, including sample data in static or depletion calculations and sample inputs for uncertainties in compositions and dimensions. The SCALE TemplateEngine allows for expanding templates to full inputs and the combination provides a powerful UQ tool.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Impact of Thermal Scattering Law on Similarity Assessment in Light-Water or Polyethylene-Moderated Systems [Slides]

This presentation found validation efforts revealed noticeable differences in c k for light-water and polyethylene moderated systems when compared to PST-002-001. Additionally, although 1 H-H 2 O and 1 H-poly use the same cross section and covariance data, the treatment of TSLs in SCALE lead to no contribution to ck between polyethylene application and benchmark experiment. When H 2 O contribution were removed the difference in c k appears to come from 239 Pu chi. Sensitivity profiles and data-induced uncertainty confirmed that the polyethylene application was more closely resembling the benchmark experiment. Specific differences between similar systems can be accounted for and examined to help understand differences in similarity (c k ) between systems was a conclusion reached by this presentation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Lost and Found Opportunities Around the Chlorine Worth Study [Slides]

This presentation begins with an Introduction and models and sensitivity results. Included within this lecture is a chlorine gap assessment discussion. referenced is an opportunity to test covariance data. This presentation wraps up with some conclusions and thoughts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Investigating Similarity Differences for Light-Water-Moderated and Polyethylene-Moderated Systems [Slides]

Validation efforts revealed noticeable differences in c k for light-water and polyethylene moderated systems when compared to experimental systems. Although 1 H-H 2 O and 1 H-poly use some of the same cross section and identical covariance data, the treatment of TSLs in SCALE lead to no contribution to c k between polyethylene application and benchmark experiment. When H 2 O contribution is removed the difference in c k appears to come from 239 Pu chi. Sensitivity profiles and data-induced uncertainty confirm that the polyethylene application more closely resembles the benchmark experiment. Interrogation techniques were used to observe how TSLs and nuclide IDs affect c k . Specific differences between similar systems can be accounted for and examined to help understand differences in similarity (c k ) between systems

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Implementation of Perturbation Theory and Sensitivity Capabilities in Griffin

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor Multiphysics analysis application, jointly developed by Argonne and Idaho National Laboratories under the DOE-NE NEAMS program. This fiscal year, capabilities for reactivity and sensitivity evaluation using perturbation methods were implemented and verified. The First Order Perturbation Method (FOPT) was employed to compute reactivity worth resulting from small perturbations in input parameters, while the Generalized Perturbation Theory (GPT) was used to evaluate sensitivities of a range of response types, including reaction rate ratio, k-eigenvalue, neutron generation time, and effective delayed neutron fraction. These perturbation methods enable users to quantify how response quantities change due to a perturbation in a input parameter without explicitly performing an additional transport simulation for each perturbed state. In particular, the GPT formulation accounts for indirect effects arising from flux changes by solving generalized inhomogeneous equations, for which a Neumann series-based iterative solution method was developed and implemented in Griffin. The implemented reactivity and sensitivity evaluation capabilities were verified using two test problems: an infinite homogeneous system and a two-dimensional hexagonal core. The results showed excellent agreement with reference solutions obtained by a direct method based on finite difference approximation as well as GPT-based results from the PERSENT code, confirming the accuracy of both reactivity and sensitivity evaluations. Additionally, preliminary uncertainty quantification (UQ) results were obtained by combining the sensitivity values computed using GPT and external covariance data, demonstrating that the implemented sensitivity results can be reliably used for uncertainty calculations. To further demonstrate the generality and practical strength of the implementation, the sensitivity evaluation capability was successfully applied to the Empire microreactor with a geometrically complex design that poses significant modeling challenges. The results confirm that Griffin enables sensitivity evaluations even for irregular and highly heterogeneous reactor configurations, thereby establishing a foundation for UQ applications in advanced reactor designs and analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

$\mathcal{R}$ -matrix analysis of η + nat Cl reactions relevant to molten-salt reactor designs [Abstract]

Performed with support from the U.S. Nuclear Criticality Safety Program in an effort to provide improved chlorine cross section and corresponding covariance data, the R-matrix analysis of neutron induced reactions for two stable chlorine isotopes, 35,37 Cl, was performed in the energy range of thermal up to 1.2 MeV. Starting from the repository of the ENDF/B-VIII.0 library and following recent measurement campaigns, this work represents a significant improvement in the evaluation of the ( η , p ) reaction channel. The evaluation methodology uses the $\mathcal{R}$ -matrix code SAMMY to generate a set of Reich-Moore resonance parameters. As predicted by recently measured 35 Cl( η , p ) data, the presented evaluation features a dramatic increase in the magnitude of the ( η , p ) reaction channel over the ENDF/B-VIII.0 and previous nuclear data ENDF/B released libraries. Together with details of the evaluation procedure, the impact of the updates in the outgoing proton emissions on reactivity coefficients for different molten-salt reactor designs is presented and discussed.

charged-particle↗

DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Covariate Dependent Sparse Functional Data Analysis

This study proposes a method to incorporate covariate information into sparse functional data analysis. The method aims at cases where each subject has a limited number of longitudinal measurements and is associated with static covariates. This research is motivated by several use cases in practice. One representative example is void swelling, a nuclear-specific material degradation mechanism. Void swelling is affected by many covariates, including alloy composition and irradiation type. How to accurately model the complicated joint effects of such covariates on the swelling process is the key to mitigating the effect of swelling and ensuring safe operation. Unlike most of the existing methods, the proposed method can handle high-dimensional covariates with the informative covariate identification procedure and sparse and irregularly spaced measurements, that is, does not require complete or dense observations. The main innovation of the proposed method is that we model the variation coming from covariates and the variation left conditioned on covariates, such that the functional principal component analysis and Gaussian process can be conducted in a unified manner. Further, we also propose a systematic approach to identify important covariates in the hypothesis testing context. The methodology is demonstrated on applications in nuclear engineering and healthcare and simulation studies.

42 ENGINEERING↗

DOE OSTI2024

Eddy Covariance Flux / Processed Data

This dataset consists of estimates of mean and turbulent quantities from a vertical array of instruments installed on the barge met mast, including: sonic anemometers, relative humidity and temperature, pressure, and rain. One of the levels consists of a paired sonic anemometer and infrared gas analyzer for direct covariance sensible and latent heat fluxes. Mean and turbulent quantities are determined over 20-minute averaging periods.

17 WIND ENERGY↗

DOE OSTI2024

Eddy Covariance Flux / Processed Data

17 WIND ENERGY↗

DOE OSTI2024

Eddy Covariance Flux / Processed Data

This dataset consists of estimates of mean and turbulent quantities from a vertical array of over a dozen instruments, which include sonic anemometers, relative humidity and temperature, pressure, rain, ocean temperature and salinity, ocean skin temperature, and surface wave spectra and bulk wave parameters. One of the levels consists of a paired sonic anemometer and infrared gas analyzer for direct covariance sensible and latent heat fluxes. Mean and turbulent quantities are determined over 20-minute averaging periods.

17 WIND ENERGY↗

DOE OSTI2021

Data compression and covariance matrix inspection: Cosmic shear

Covariance matrices are among the most difficult pieces of end-to-end cosmological analyses. In principle, for two-point functions, each component involves a four-point function, and the resulting covariance often has hundreds of thousands of elements. We investigate various compression mechanisms capable of vastly reducing the size of the covariance matrix in the context of cosmic shear statistics. This helps identify which of its parts are most crucial to parameter estimation. We start with simple compression methods, by isolating and “removing” 200 modes associated with the lowest eigenvalues, then those with the lowest signal-to-noise ratio, before moving on to more sophisticated schemes like compression at the tomographic level and, finally, with the massively optimized parameter estimation and data compression (MOPED). We find that, while most of these approaches prove useful for a few parameters of interest, like Ω m , the simplest yield a loss of constraining power on the intrinsic alignment (IA) parameters as well as S 8 . For the case considered—cosmic shear from the first year of data from the Dark Energy Survey—only MOPED was able to replicate the original constraints in the 16-parameter space. Finally, we apply a tolerance test to the elements of the compressed covariance matrix obtained with MOPED and confirm that the IA parameter A IA is the most susceptible to inaccuracies in the covariance matrix.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Theoretical and calculable dependent variables and their covariance in nuclear data libraries [Slides]

This presentation begins by defining observables, including theoretical observable, calculable observable, and measured observable. It also provides definitions and examples of experimental effects. Additionally, uncertainty in evaluated libraries and a metric to probe evaluated uncertainty is presented. In conclusion, being related to theoretical quantities such as resonance parameters, the uncertainty in ENDF libraries (e.g., ENDF/B-VIII.0) can be overestimated because it is usually evaluated under the guidance of experimental uncertainty: lim Δσexp.corr.→0 Δσ = Δσ theoretical model . The presentation states that a clear distinction of the uncertainty between nuclear theoretical models and experimental corrections should be revisited and this is important since (e.g., transport) simulations need theoretical quantities convoluted with specific operational parameters and material configurations. It also states that strong coupling between uncertainty quantification methodologies and optimization procedures exists, and it is necessary to develop methodologies to obtain uncertainty on theoretical models from physical and mathematical constraints.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Water loss through evapotranspiration after precipitation events in bioenergy crops grown in similar climatic conditions

The relationship between precipitation and evapotranspiration (ET) is critical to understanding water cycle related dynamics in ecosystems, including crops. Existing studies of bioenergy crops have primarily focused on annual or seasonal ET rates, with less attention given to the immediate ET response following precipitation events. This study examines the variation in ET rates in the days subsequent to precipitation events across various bioenergy crops—corn, switchgrass, and prairies—utilizing 13 years (2010–2022) of growing season data. Meteorological and eddy covariance flux data were collected from seven eddy covariance flux towers as part of the GLBRC scale-up experiment at the Kellogg Biological Station Long Term Ecological Research sites. The analysis revealed that average ET peaked the day after precipitation and declined linearly over the following days, with a statistically significant relationship (p-value = 0.00027, R2 = 0.96). Neither the type of biofuel vegetation nor the historical land use significantly influenced ET post-precipitation events (p-values = 0.53 and 0.153, respectively). Key predictors of ET following precipitation events include shortwave radiation, season, day of the year, ambient temperature, vapor pressure deficit (VPD), long-wave radiation, precipitation amount, soil moisture, and annual variability. These findings enhance our comprehension of ET responses in bioenergy crop systems, with implications for water management in sustainable agriculture.

09 BIOMASS FUELS↗

DOE OSTI2022

Kronecker-structured covariance models for multiway data

Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce multi-indexed data, e.g. over spatial, frequency, and temporal dimensions. We will address the challenges of covariance representation of multiway data and review some of the progress in statistical modeling of multiway covariance over the past two decades, focusing on tensor-valued covariance models and their inference. We will illustrate through a space weather application: predicting the evolution of solar active regions over time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Applying a Template of Expected Uncertainties to Updating 239 Pu(n,f) Cross-section Covariances in the Neutron Data Standards Database

Templates of uncertainties expected in specific measurement types were recently developed. One aim of these templates is to help evaluators in identifying (1) missing or suspiciously low uncertainties and (2) missing correlations between uncertainties of the same and different experiments, when estimating covariances for experimental data employed in their evaluations. These templates also provide realistic estimates of standard deviations and correlations for a particular uncertainty source and measurement type that can be used by evaluators in situations where they are not supplied by the experimenters. This information allows for a more comprehensive uncertainty analysis across all measurements considered in an evaluation and, thus, more realistic evaluated covariances. Here, in this work, we extend a template that is applicable to uncertainties expected in neutron-induced fission, (n,f), cross-section measurements. It is applied to improving covariances of 239 Pu(n,f) cross-section measurements in the database underlying the Neutron Data Standards evaluations. This particular example was chosen since this evaluation is primarily based on experimental information. Also, some uncertainties of individual 239 Pu(n,f) cross-section experiments in this database were suspected to be underestimated. The evaluated uncertainties obtained after updating the covariances in the database by means of the template indeed do increase compared to their original values. Even more importantly, the evaluated mean values change noticeably. These modified cross sections impact application calculations significantly, as is demonstrated by employing them in simulations of the effective neutron multiplication factor for a few selected critical assemblies. However, this updated evaluated 239 Pu(n,f) cross section should not be interpreted as the final one that should replace values of the current Neutron Data Standards project. Evaluations for the Neutron Data Standards of the 239 Pu(n,f) cross section must be linked to many other observables included in the associated database, most notably to cross sections for 235 U(n,f), but also to those for 10 B(n, α ), 6 Li(n,t), 238 U(n,f), and 238 U(n, γ ), because of included measurements of the 239 Pu(n,f) cross section that appear as ratios to these reactions. Some of these other reactions are correlated to further observables in the database. Hence, updating uncertainties of data sets of any of these observables can potentially impact the 239 Pu(n,f) cross section. Uncertainties for all measurements of these linked physical observables have to be updated before a comprehensive evaluation of the 239 Pu(n,f) cross section and its corresponding uncertainties can be provided.

239Pu Neutron-induced fission cross-section↗

DOE OSTI2024

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗

DOE OSTI2020

Advanced User Interface Capabilities [Slides]

This tutorial will review the data plotting and geometry visualization capabilities in the Fulcrum user interface. This tutorial will help you become familiar with Fulcrum’s 2D plot, and 2D and 3D geometry visualization features. You will learn how to identify plottable data items, compose and export plot and plot data for SCALE plot formats (SDF, Ampx MG/CE, PLT, F71, PTP, SPF, ORIGEN Gamma data, etc.) and visualize, navigate, cut, hide, and export the geometry and spatial data (fission-, dose-map, etc.) overlays in 2D and 3D. No prior experience with SCALE is required. Attendees can follow along using 6.3.0-beta.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗