Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “uncertainty analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An uncertainty-aware strategy for plasma mechanism reduction with directed weighted graphs

In this work, we present a framework for the analysis and reduction of plasma mechanisms by means of weighted directed graphs, in which reactions and species are both treated as nodes. The methodology consists of two distinct analyses. The first, which is qualitative, relies on graph spatializations via force-directed algorithms to discover the predominant global patterns in the chemical model. The second ranks the reactions based on their shortest paths' lengths from/to the species of interest and their relative contributions to the power balance. Further, this quantitative investigation enables a strategy for mechanism reduction that is fully automatized, as it does not require any expert knowledge, highly effective, as it generates reduced mechanisms that are highly accurate while relying on a small number of processes, and easily interpretable, as the algorithm justifies the importance of the retained reactions by outputting their related chemical pathways. Additionally, the work proposes a methodology extension that employs ensembles of graphs to improve the robustness of the reduced mechanism to reaction parameter uncertainties. The approach, here tested for steady-state predictions of a plasma system characterizing negative hydrogen ion sources, is general and can be used in a wide variety of applications outside the particular nuclear fusion context demonstrated in this work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Combined Theoretical Analysis of the Parity-Violating Asymmetry for 48 Ca and 208 Pb

The recent experimental determination of the parity violating asymmetry A PV in 48 Ca and 208 Pb at Jefferson Lab is important for our understanding on how neutrons and protons arrange themselves inside the atomic nucleus. To better understand the impact of these measurements, we present a rigorous theoretical investigation of A PV in 48 Ca and 208 Pb and assess the associated uncertainties. Here, we complement our study by inspecting the static electric dipole polarizability in these nuclei. The analysis is carried out within nuclear energy density functional theory with quantified input. We conclude that the simultaneous accurate description of A PV in 48 Ca and 208 Pb cannot be achieved by our models that accommodate a pool of global nuclear properties, such as masses and charge radii, throughout the nuclear chart, and describe—within one standard deviation—the experimental dipole polarizabilities in these nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Characterization of Uranium Foil Irradiations at the WSU TRIGA Reactor Using a New Reactor Model in Scale

A new reactor model of the Washington State University TRIGA was developed in the SCALE neutron transport code, and its fidelity was verified by comparison to MCNP and available data for several reactor parameters. The model was used to characterize irradiations designed to produce the short-lived actinides 237 U and 239 U, two key isotopes for nuclear forensics. These short-lived actinides, their decay daughters 237Np and 239Pu, and total fissions (via 99 Mo) were measured in irradiated foils at Los Alamos National Laboratory and other labs with good agreement among parent/daughter pairs and among labs. The laboratory-measured isotope ratios were used as a benchmark for the model determination of reaction rates. The flux distribution in the foils was also determined. Finally, using the continuous energy TSUNAMI-3D module of SCALE, a sensitivity/uncertainty analysis was performed to determine the effects on foil reactions caused by other system-wide reactions and by uncertainties in the known cross-sections.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Description and Use of SCALE Sampler Parametric Capability for Engineering Analysis and Optimization

The Sampler sequence was introduced into the SCALE nuclear modeling and simulation suite in SCALE 6.2 to perform uncertainty quantification via random sampling of nuclear data, material number densities, and dimensions. Sampler was expanded with the introduction of a parametric capability in SCALE 6.2.2. This paper discusses input for the Sampler parametric sequence and presents two case studies of analyses performed using the sequence. These case studies include preconceptual design of a package for transporting high assay low-enriched uranium (HALEU) oxide and scoping calculations to support subcritical limit development for a future update of the ANSI/ANS-8.1 (ANS-8.1) standard. The parametric capability within Sampler provides many benefits to analysts. For instance, parametric sweeps are frequently used to identify optimum parameter values as part of safety analysis or system design, but such sweeps can require substantial engineering time or may rely on custom-written scripts or scripts such as Write One, Run Many (or WORM) developed outside of any software quality assurance program. With the parametric capabilities in Sampler, however, a large number of inputs can be generated automatically without recourse to scripting by individual analysts. The parametric capability can also be used in lieu of the CSAS5S search sequence to identify optimum parameters more simply with straightforward inputs and outputs. Sampler can also be used to calculate input parameters from engineering specifications. For example, diameters can be converted to radii, or masses can be used to calculate number densities. Overall, the Sampler parametric capability provides a robust feature within SCALE, eliminating the need for user-developed scripting.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

An overview of wind-energy-production prediction bias, losses, and uncertainties

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

17 WIND ENERGY↗
DOE OSTI2026

Improving statistical precision in Monte Carlo samples with negative weights via reweighting and uncertainty quantification

High statistical precision is critical for Monte Carlo (MC) samples in high energy physics and is degraded by negatively weighted events. This paper investigates a procedure to learn the relationship between the negative and positive weight distributions of any sample, allowing the reduction of statistical uncertainty by reweighting kinematically equivalent events with the same sign. A robust uncertainty quantification method is required for the practical application of such method. Two methods for the estimation of the reweighting uncertainty are developed: one at the event and another one at the final observable level. The latter method is strongly favored. The gains in statistical precision are then quantified. The method is demonstrated on Sherpa vector boson plus jets samples when using all generated events and when restricted to the signal region of a mock analysis. It is demonstrated to significantly reduce stochastic behavior in sparse MC samples while decreasing the overall uncertainty with a sufficiently well-known reweighting function.

Monte Carlo methods↗
DOE OSTI2022

Alternative analysis of the MINERVE ZPR oscillation experiments

Delayed neutrons are of fundamental importance in the field of nuclear reactor dynamics and control. However, the precursor yield fraction for a given nuclear reactor are dependent on the properties of the reactor. Thus, in-pile experiments, such as oscillation experiments are conducted in order to measure those values. In this work, an alternative analysis of the piston oscillation experiments that have been conducted in the MINERVE reactor in 2013 is performed. A new method which evolves effective terms that cancel out undesired drifts of the flux during the experiments is presented. The evaluation of the uncertainty on the values of the response function is also presented. Moreover, the effective delayed neutron fraction β{sub eff} is evaluated and is compared to results in previous works. As the analysis has led to an estimation of β{sub eff} with a large uncertainty, it has been deduced that the oscillation experiments that have been conducted in MINERVE are not a reliable method of experimentation to determine the value of β{sub eff} in the reactor and that the noise experiments are better suited for that purpose.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Quantifying uncertainty in Pareto estimates of global lake area

Abstract Size is a critical factor determining the rate and occurrence of specific lake processes such as carbon sequestration and greenhouse gas emissions and emerging evidence suggests that small lakes in particular have particularly large CO 2 flux rates. Because we do not have a complete census of all lakes, upscaling estimates of such processes to small lakes at broad spatial scales requires the use of lake size‐abundance distributions rather than empirical measurements of area. Existing lake census efforts are incomplete such that as lakes become smaller, they are more likely to be omitted either because they are too small to be resolved from remote sensing products or because of limited ground surveying effort (i.e., “censoring” of small lakes relative to large lakes). The present study explores one potential shortcoming of prior approaches estimating global lake area using lake size‐abundance distributions. Namely, that these prior approaches rely on frequentist curve fitting techniques combined with an ad‐hoc cutoff determination strategy (visual inspection to determine a likely censoring point). This yields an over‐exact lake area estimate that is typically reported with no uncertainty bounds. I show how these shortcomings can be addressed with a Bayesian model that produces larger estimates of lake area uncertainty relative to the typical approach. When used as part of a sensitivity analysis, such an approach has the potential to enable more robust intercomparisons among studies of aquatic processes upscaling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Local Bayesian Dirichlet mixing of imperfect models

Abstract To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Overview of preparation for the American WAKE ExperimeNt (AWAKEN)

The American WAKE ExperimeNt (AWAKEN) is a multi-institutional field campaign focused on gathering critical observations of wind farm–atmosphere interactions. These interactions are responsible for a large portion of the uncertainty in wind plant modeling tools that are used to represent wind plant performance both prior to construction and during operation and can negatively impact wind energy profitability. The AWAKEN field campaign will provide data for validation, ultimately improving modeling and lowering these uncertainties. The field campaign is designed to address seven testable hypotheses through the analysis of the observations collected by numerous instruments at 13 ground-based locations and on five wind turbines. The location of the field campaign in Northern Oklahoma was chosen to leverage existing observational facilities operated by the U.S. Department of Energy Atmospheric Radiation Measurement program in close proximity to five operating wind plants. The vast majority of the observations from the experiment are publicly available to researchers and industry members worldwide, which the authors hope will advance the state of the science for wind plants and lead to lower cost and increased reliability of wind energy systems.

17 WIND ENERGY↗
DOE OSTI2023

Predicting the neutrinoless double-β-decay matrix element of 136 Xe using a statistical approach

Calculation of the nuclear matrix elements (NMEs) for double-β decay is of paramount importance for guiding experiments and for analyzing and interpreting the experimental data, especially for the search of the neutrinoless double β decay mode (0v⁢ββ). However, there are currently still large differences between the NME values calculated by different methods, hence a quantification of their uncertainties is very much required. Here, in this paper, we propose a statistical analysis of 0⁢v⁢ββ NME for the 136 Xe isotope, based on the interacting shell model, but using three independent effective Hamiltonians, emphasizing the range of the NMEs' most probable values and its correlations with observables that can be obtained from the existing nuclear data. Consequently, we propose a common probability distribution function for the 0⁢vββ NME, which has a range of (1.55–2.65) at 90% confidence level, with a mean value of 1.99 and a standard deviation of 0.37.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

pypolymix

SAND2026-16700O Pypolymix builds stochastic surrogate models using a lightweight Python library. Researchers working with large-scale scientific models can use it to enhance optimization and uncertainty quantification studies. Pypolymix enables the efficient construction of surrogate models and improves analysis and performance of complex simulations. 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.

SciDAC↗
DOE OSTI2024

Bayesian Tensor Decompositions for Scalable Supervised Learning of Scientific Data (Final Report)

In this document we highlight the detailed accomplishments and progress that we have made in this period. This progress seeks to address the three main objectives to provide new algorithms for quantifying uncertainty in low-multilinear-rank models and to leverage them for data analysis. These include: (1) develop probabilistic models for low-multilinear-rank functions; (2) develop a suite of Bayesian learning approaches to learn the probabilistic models from data; (3) apply the techniques on challenging problems arising in DOE-relevant applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sensitivity to Cabibbo-Suppressed $\Lambda$ Production in MicroBooNE

We present a procedure to measure a flux averaged, restricted phase space cross section for direct (Cabibbo suppressed) Λ production using the MicroBooNE detector. This is a rare process, exclusively occurring in anti-neutrino interactions and to maximise the sensitivity of this analysis we combine predictions using the NuMI flux from two data taking periods, one in which this beam was run in neutrino mode and one in anti-neutrino mode. The event selection for this analysis was previously described in MicroBooNE public note 1097. The calculation of systematic uncertainties is shown and a method to propagate them through to the final cross section calculation is described, with final sensitivities shown in the form of Bayesian posterior distributions on the extracted cross section.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Advanced Reactor Cyber Analysis and Development Environment (ARCADE) for System-Level Design Analysis

Cybersecurity is a persistent concern to the safety and security of Nuclear Power Plants (NPPs), but has lacked data-driven, evidence-based research. Rigorous cybersecurity analysis is critical for the licensing of advanced reactors using a performance-based approach. One tool that enables cybersecurity analysis is modeling and simulation. The nuclear industry makes extensive use of modeling and simulation throughout the decision process but lacks a method to incorporate cybersecurity analysis with existing models. To meet this need, the Advanced Reactor Cyber Analysis and Development Environment (ARCADE) was developed. ARCADE is a suite of publicly available tools that can be used to develop emulations of industrial control system devices and networks and integrate those emulations with physics simulators. This integration of cyber emulations and physics models enables rigorous cyber-physical analysis of cyber-attacks on NPP systems. This report provides an overview of key considerations for using ARCADE with existing physics models and demonstrates ARCADE’s capabilities for cybersecurity analysis. Using a model of the Small Modular Advanced High Temperature Reactor (SmAHTR), ARCADE was able to determine the sensitivity of the primary heat exchangers (PHX) to coordinated cyber-attacks. The analysis determined that while the PHX’s failures cause disruption to the reactor, they did not cause any safety limits to be exceeded because of the plant design, including passive safety features. Further development of ARCADE will enable rigorous, repeatable, and automated cyber-physical analysis of advanced reactor control systems. These efforts will also help reduce regulatory uncertainty by presenting similar types of cybersecurity analyses in a common format, driving standard approaches and reporting.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Coupling RELAP5-3D to BISON

This report details two approaches for coupling the BISON nuclear fuel performance code with RELAP5-3D. Both approaches are shown to work well. The first is a Python-based approach, and the second combines the two applications into one executable with data passed in memory from one library to the other. This second coupling approach forms the basis for analysis of complex loss of coolant scenarios and enables future calculations of modeling uncertainties.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

HELPR Version 1.1.0 User Guide

Hydrogen Extremely Low Probability of Rupture (HELPR) is a modular probabilistic fracture mechanics modeling platform developed to assess structural integrity of pipelines for transmission and distribution of hydrogen. HELPR couples fatigue and fracture engineering models with probabilistic methods to generate fast predictions and enables quantification of prediction uncertainty and sensitivity. This user manual serves as a guide through the various analysis features HELPR contains.

08 HYDROGEN↗
DOE OSTI2025

Design of Critical Experiments Involving Graphite elements at the IPEN/MB-01 Plate-Type Core

A series of critical experiments were designed involving the use of characterized graphite elements in the new plate-type core of the IPEN/MB-01 reactor. Eleven configurations with different graphite quantities are considered, each producing distinct effects on neutron fluxes and offering opportunities to analyze varying sensitivities to k eff in the system. The analysis shows that the experiments can be performed with acceptably low k eff uncertainties considering an adequate characterization of the graphite elements to be used. The sensitivity results obtained using SCALE TSUNAMI calculations show that the experiments have a notable k eff sensitivity coefficient to graphite. In the configurations tested, the k eff sensitivity to graphite TSL is low. This experiment will be useful for criticality safety validation of applications using uranium fuel enriched to around 19.75 wt % 235 U, light water and graphite. It will also be useful to gain more insight into graphite material properties and their effect on k eff as a result of using the well-characterized graphite elements. The execution of the experiments is planned for the summer of 2025. The 11 designed configurations serve as a basis for the final design, and fewer configurations will be executed. Once executed, these critical experiments will be evaluated and submitted for publication in the International Criticality Safety Benchmark Evaluation Project Handbook, supporting the DOE/NRC Collaboration for Criticality Safety Support for Commercial-Scale HALEU for Fuel Cycles and Transportation project goal.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗