SEARCH · Search NASA

Results for “uncertainty assessment”

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.

SourceResearch connectionFrom yearThrough year

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

At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

DOE OSTI2026

OC6 Project Phase IV: Validation of Numerical Models for Novel Floating Offshore Wind Support Structures

The fourth phase of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) aims to assess the ability of the tools to model a novel floating wind design. The design that was selected for this project is the TetraSpar designed by Stiesdal Offshore Technologies. The platform is a unique design that features a spar like stability with a very low center of gravity, but does not require deep-water ports. This combination is achieved with a system made of two separate steel structures, a hull at the water’s surface that supports the wind turbine tower, and a keel that is suspended below the main body. This lower keel has a very high density and drives the total platform’s low center of gravity. Scaled model tests of the TetraSpar design were performed in 2018-2019 by the University of Maine. The tests were performed with 1:43 Froude scaling. The data collected in the model tests provide a detailed description of the response of the platform in various controlled and relatively well understood conditions. Comparison with these data provides a good opportunity for validation of numerical models. The OC6 Phase IV work will focus on the specific parameters used in these scaled model tests. The load cases (LC) considered in this project are as follows: LC 1.X - Equilibrium and Static Offset Tests LC 2.X - Platform Free Decays LC 3.X - Turbine Thrust (Wind Only) LC 4.X - Regular and Irregular Waves (Wave only) LC 5.X - Combined Wind and Wave Loading

17 WIND ENERGY↗

DOE OSTI2024

Assessing the release, transport, and retention of radioactive aerosols from hypothetical breaches in spent fuel storage canisters

Interim dry storage of spent nuclear fuel involves storing the fuel in welded stainless-steel canisters. Under certain conditions, the canisters could be subjected to environments that may promote stress corrosion cracking leading to a risk of breach and release of aerosol-sized particulate from the interior of the canister to the external environment through the crack. Research is currently under way by several laboratories to better understand the formation and propagation of stress corrosion cracks, however little work has been done to quantitatively assess the potential aerosol release. The purpose of the present work is to introduce a reliable generic numerical model for prediction of aerosol transport, deposition, and plugging in leak paths similar to stress corrosion cracks, while accounting for potential plugging from particle deposition. The model is dynamic (changing leak path geometry due to plugging) and it relies on the numerical solution of the aerosol transport equation in one dimension using finite differences. The model’s capabilities were also incorporated into a Graphical User Interface (GUI) that was developed to enhance user accessibility. Model validation efforts presented in this paper compare the model’s predictions with recent experimental data from Sandia National Laboratories (SNL) and results available in literature. We expect this model to improve the accuracy of consequence assessments and reduce the uncertainty of radiological consequence estimations in the remote event of a through-wall breach in dry cask storage systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Staged Z-Pinch Modeling with Hydra and Chicago Codes (CRADA Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC (“Contractor”), as manager and operator of Lawrence Livermore National Laboratory (“LLNL”) and Magneto Inertial Fusion Technologies, Inc. ("MIFTI" or "Participant"), to develop staged Z-pinch (SZP) modeling with HYDRA and CHICAGO codes. The contractor performed numerical simulations of the SZP to reproduce and validate Participant’s MACH2 simulations. Contractor used the state-of-the-art, radiation-magneto-hydrodynamic code HYDRA, as well as the hybrid particle-in-cell code CHICAGO. This work helped assess the physical and numerical uncertainties associated with the simulations and the overall fusion scheme. HYDRA is a well-established and validated code. This work has helped to independently assess the SZP concept to give confidence to the Participant and to the broader fusion community.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Performance Comparison of Circular and Spherical Error Probable Estimators

This report compares the performance of three Circular Error Probable (CEP) estimators: the Grubbs-Patnaik estimator, a new, non-iterative, radial-integration estimator, and a median estimator. It also compares the performance of two Spherical Error Probable (SEP) estimators. The performance of each estimator is assessed in terms of bias, uncertainty, robustness, and computational complexity. Robustness is evaluated with respect to outliers, variations in the underlying statistical distribution characterizing munition impact positions, and impact-position measurement errors. The performance assessments indicate the radial-integration and Grubbs-Patnaik estimators perform nearly identically providing the statistical distribution of impact-position coordinates is jointly normal with zero means. In that case, both estimators outperform the median estimator by about 2% relative to the true CEP in terms of estimator uncertainty. The bias performance of the radial-integration and median estimators is close to zero for jointly normal impacts, however, the Grubbs-Patnaik estimator can be significantly biased for jointly normal impacts with non-zero means. When the statistical distribution characterizing impact positions is known, but not jointly normal, the radial-integration estimator is superior. In this case, the median estimator also outperforms the Grubbs-Patnaik estimator but is not quite as good as the radial-integration estimator. If the statistical distribution characterizing impacts is unknown and not jointly normal, or if distribution parameters are difficult or impractical to estimate, or if test data is corrupted with outliers, then the median estimator dramatically outperforms the other estimators, especially in terms of estimation bias. Unexpectedly, measurement noise did not significantly degrade the performance of any of the estimators, except for cases with signal to noise ratios less than five. Although the Grubbs-Patnaik estimator has remained the gold standard for CEP estimation for over half a century, the performance assessments indicate the new, non-iterative, radial-integration estimator and the median estimator offer significant advantages and, in most practical real-world conditions, are superior estimators. These estimators are also useful for SEP estimation whereas the Grubbs-Patnaik estimator does not extend to three dimensions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Dynamic risk assessment for geologic CO 2 sequestration

At a geologic CO 2 sequestration (GCS) site, geologic uncertainty usually leads to large uncertainty in the predictions of properties that influence metrics for leakage risk assessment, such as CO 2 saturations and pressures in potentially leaky wellbores, CO 2 /brine leakage rates, and leakage consequences such as changes in drinking water quality in groundwater aquifers. The large uncertainty in these risk-related system properties and risk metrics can lead to over-conservative risk management decisions to ensure safe operations of GCS sites. The objective of this work is to develop a novel approach based on dynamic risk assessment to effectively reduce the uncertainty in the predicted risk-related system properties and risk metrics. We demonstrate our framework for dynamic risk assessment on two case studies: a 3D synthetic example and a synthetic field example based on the Rock Springs Uplift (RSU) storage site in Wyoming, USA. Results show that the U.S. National Risk Assessment Partnership’s Open Source Integrated Assessment Model (NRAP-Open-IAM) coupled with a conformance evaluation can be used to effectively quantify and reduce the uncertainty in the predictions of risk-related system properties and risk metrics in GCS.

58 GEOSCIENCES↗

DOE OSTI2024

FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.

Hari, Gautam↗

DOE OSTI2022

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections - greenhouse gas (GHG) emissions scenario, model response, and internal variability - have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Sequential Design of Experiments for Pilot Testing of Novel Solvent System

The CCSI2 program is supporting a six-month test campaign at the National Carbon Capture Center (NCCC) for evaluation of a novel water-lean solvent. This presentation describes CCSI2’s efforts in process modeling of the solvent system for both coal and natural gas-based flue gas sources and initial uncertainty quantification (UQ) work to estimate parametric uncertainty in key sub-models of interest (e.g., thermodynamics, mass transfer, reaction kinetics). Moreover, perspective is provided on how UQ and sequential design of experiments (SDoE) tools are used to assess the impact of model uncertainty on projected process performance, use this information to optimize data collection during the campaign, and refine process models through data collection. This framework is expected to reduce the overall model uncertainty, and thus risk associated with scale-up as the process moves towards commercialization.

Morgan, Joshua↗

DOE OSTI2023

Towards Realistic and High Fidelity Models for Nuclear Reactor Power Synthesis Simulation with Self-Powered Neutron Detectors

As presented in this report, a weighting function–based inferencing method is being applied to synthesize the power distribution in next-generation and university research reactors based on simulated self power neutron detector (SPND) responses. The overall goal is to assess the impacts of sensor uncertainty and true power distribution perturbations on the error in the synthesized power distribution. Regarding sensor uncertainty, the NuScale Small Modular Reactor (SMR) and the Westinghouse AP1000 serve as testbeds for analyzing the impact of varying the sensor uncertainty, as well as varying the number of sensors per sensor string in the reactor core. The reactor models are informed by Monte Carlo N-Particle (MCNP) neutron flux tallies. For the NuScale SMR and Westinghouse AP1000, the SPND response functions (i.e., the response of the SPNDs to individual segments of fuel) were determined homogeneously. Regarding an analysis of power distribution perturbation detection, the Texas A&M Testing, Research, Isotopes, General Atomics Reactor (TAMU TRIGA) reactor was used as a demonstration case with one particular arrangement of SPNDs; the response functions for this reactor model were determined heterogeneously, making this a uniquely high-fidelity demonstration of perturbation detection. Finally, SPND models generated in the Geometry and Tracking 4 (Geant4) code have been generated and tested for comparison with traditionally implemented analytical SPND models, with the intent for Geant4 integration with the full methodological framework. SPND current outputs as a function of distance from some fuel assembly segment in the NuScale SMR are compared with the analytically determined currents. Results from the sensor uncertainty simulations for the NuScale SMR and AP1000 indicate that the average error in the inferred power distribution on the fuel assembly segment level is reasonably low, being slightly less than the random uncertainty applied to all respective SPNDs in both cores. For example, if all SPNDs in the core have a random uncertainty of 5%, then the corresponding fuel assembly segment level error (i.e. difference between the true and inferred local power) is ~2–3%. However, the maximum error in the inferred power distribution on the fuel assembly segment level can be considerably high (>15%) when SPND random uncertainties start to exceed ~3%. In general, the average and maximum errors in the inferred power distribution were slightly higher in the AP1000 as opposed to the NuScale SMR for the sensor string configurations considered herein. Another result determined from analysis of the sensor uncertainty simulations was that increasing the number of SPNDs per string does not clearly reduce inferred power distribution error and can in fact make the error large in some cases; however, this assessment may skewed due to imposed iteration limits. Results from the perturbation detection demonstration using the high-fidelity TAMU TRIGA model indicate that, given the arrangement of 17 SPND strings and 4 SPNDs per string considered herein, there is a clear, provable ability to infer a localized Gaussian-type peak perturbation in the 3D power distribution. Such a perturbation was detected with an average fuel assembly segment level error of 0.19%, and the general visualization of the detected perturbation clearly indicates that the magnitude and shape were appropriately resolved. Finally, the electrical current output generated by the Geant4 modeled SPND indicates significant magnitude differences than the analytically modeled SPND, demonstrating the need for accurate SPND models which account for finite sensor geometry effects to inform the power synthesis work described herein.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris‐Flow Inundation

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris-Flow Inundation

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Impact of nuclear data covariance libraries on uncertainty quantification of sodium cooled fast reactor simulation

Uncertainty quantification in core modelling to assess reactor performance is a regular practice to identify design safety limits. Best-estimate plus uncertainty (BEPU) methods are commonly used to propagate uncertainties to core responses from input parameters, characterized by variance-covariance libraries. This study evaluates the impact of different covariance libraries on the uncertainty associated with core safety parameters and their propagation through reactor transient modelling for a metallic core (ABR-1000). The COMAC-1.0, COMMARA-2.0, and COMMARA-2.1 covariance libraries are used to propagate nuclear data uncertainties to neutron feedback coefficients using the Generalized Perturbation Theory, then through transient safety analyses to establish the confidence interval for safety performance of the reactor using stochastic sampling. The considered transients include Unprotected Transient Over Power and Unprotected Loss Of Flow. Most influential nuclide and reaction pairs in different variance-covariance libraries are identified and their impact on the uncertainty of safety parameters are evaluated. Significant differences were observed between uncertainties from COMAC compared to COMMARA libraries. Uncertainties from COMMARA libraries were consistently larger than COMAC for all cases. For structure, coolant density feedback coefficients and control rod worth, total nuclear data uncertainties were approximately 50% larger for COMMARA than COMAC. Differences were also observed in identification of top contributors of uncertainty in terms of reaction-cross sections. For instance, largest contribution to total uncertainty of radial expansion coefficient from COMMARA-2.0 and 2.1 originates from Na{sup 23} P1 Elastic cross section (0.81%) but from Pu{sup 239} Fission (0.53%) with COM C-1.0. Such differences are evaluated in this research for all quantities of interest. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach .

Despite widespread use of factor fixing in environmental modeling, its effect on model predictions has received little attention and is instead commonly presumed to be negligible. We propose a proof-of-concept adaptive method for systematically investigating the impact of factor fixing. The method uses Global Sensitivity Analysis methods to identify groups of sensitive parameters, then quantifies which groups can be safely fixed at nominal values without exceeding a maximum acceptable error, demonstrated using the 21-dimensional Sobol’ G-function. Furthermore, three error measures are considered for quantities of interest, namely Relative Mean Absolute Error, Pearson Product-Moment Correlation and Relative Variance. Results demonstrate that factor fixing may cause large errors in the model results unexpectedly, when preliminary analysis suggests otherwise, and that the default value selected affects the number of factors to fix. To improve the applicability and methodological development of factor fixing, a new research agenda encompassing five opportunities is discussed for further attention.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Assessing Model Prediction Trustworthiness in the Presence of Model-Form Uncertainty

Explore the source record for details and available documents.

Portone, Teresa↗

DOE OSTI2023

Urban Flood Modeling: Uncertainty Quantification and Physics‐Informed Gaussian Processes Regression Forecasting

Abstract Estimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models. We consider the effects of the model's complexity and uncertainty in key input parameters. The effect of rainfall intensity on the uncertainty in water depth predictions is also studied. As a test study, we choose the Interconnected Channel and Pond Routing (ICPR) model of a part of the city of Minneapolis. The uncertainty in the ICPR model's predictions of the floodwater depth is quantified in terms of the ensemble variance using the multilevel Monte Carlo (MC) simulation method. Our results show that uncertainties in the studied domain are highly localized. Model simplifications, such as disregarding the groundwater flow, lead to overly confident predictions, that is, predictions that are both less accurate and uncertain than those of the more complex model. We find that for the same number of uncertain parameters, increasing the model resolution reduces uncertainty in the model predictions (and increases the MC method's computational cost). We employ the multilevel MC method to reduce the cost of estimating uncertainty in a high‐resolution ICPR model. Finally, we use the ensemble estimates of the mean and covariance of the flood depth for real‐time flood depth forecasting using the physics‐informed Gaussian process regression method. We show that even with few measurements, the proposed framework results in a more accurate forecast than that provided by the mean prediction of the ICPR model.

Kohanpur, Amir H.↗

DOE OSTI2022

The role of collisional energy transfer in the thermal and prompt dissociation of 1-methyl allyl

Collisional energy transfer plays a pivotal role in a-priori calculations of the pressure-dependent rate constants obtained from a master equation. However, accurate determinations of collisional energy transfer parameters are rare and so most kinetic studies rely on best-fits of these parameters to experimental measurements (if available) or estimates guided from literature studies. In this work, we have quantified the effect of the uncertainty in energy transfer parameters on the thermal and prompt dissociation kinetics of a resonance stabilized radical, 1-methyl allyl (1MA), of relevance to the combustion of 1- and 2-butene isomers. Simulations using literature kinetics models were performed to assess the impact of these uncertainties on flame propagation and speciation data in laminar flames of 1- and 2-butene. Analyses of the uncertainty propagated by the energy transfer parameters for 1MA dissociation to the flame simulations, in particular the laminar flame speed, indicate an intricate coupling between the kinetics of 1MA dissociation (chain propagation) and its reaction with H-atoms (chain-termination). Ab-initio based theoretical calculations were also performed to obtain pressure-dependent kinetics for the reaction of 1MA with H-atoms. Lastly, theoretically calculated energy transfer parameters were used to best characterize the kinetics and branching between the chain propagating 1MA dissociation and the chain-terminating reaction 1MA + H.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Refining Planetary Boundary Layer Height Retrievals From Micropulse‐Lidar at Multiple ARM Sites Around the World

Abstract Knowledge of the planetary boundary layer height (PBLH) is crucial for various applications in atmospheric and environmental sciences. Lidar measurements are frequently used to monitor the evolution of the PBLH, providing more frequent observations than traditional radiosonde‐based methods. However, lidar‐derived PBLH estimates have substantial uncertainties, contingent upon the retrieval algorithm used. In addressing this, we applied the Different Thermo‐Dynamic Stabilities (DTDS) algorithm to establish a PBLH data set at five separate Department of Energy's Atmospheric Radiation Measurement sites across the globe. Both the PBLH methodology and the products are subject to rigorous assessments in terms of their uncertainties and constraints, juxtaposing them with other products. The DTDS‐derived product consistently aligns with radiosonde PBLH estimates, with correlation coefficients exceeding 0.77 across all sites. This study delves into a detailed examination of the strengths and limitations of PBLH data sets with respect to both radiosonde‐derived and other lidar‐based estimates of the PBLH by exploring their respective errors and uncertainties. It is found that varying techniques and definitions can lead to diverse PBLH retrievals due to the inherent intricacy and variability of the boundary layer. Our DTDS‐derived PBLH data set outperforms existing products derived from ceilometer data, offering a more precise representation of the PBLH. This extensive data set paves the way for advanced studies and an improved understanding of boundary‐layer dynamics, with valuable applications in weather forecasting, climate modeling, and environmental studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

ICME for Creep of Ni-Base Superalloys in Advanced Ultra-Supercritical Steam Turbines

The goal of this project contains three major aspects: (1) critical assessment of existing experimental data, (2) critical assessment of existing modeling capabilities and (3) development of new modeling capabilities that are critical but currently missing in predicting long-term creep behavior of Ni-base superalloys used in advanced ultra- supercritical (A-USC) steam turbines for coal-fired power plants. The project uses advanced materials informatics to datamine the experimental data, uncertainty quantification tools to rigorously assess existing creep-life models, and integration of phase-field and FFT-based crystal plasticity to develop a multi-scale, physics-based, microstructure-sensitive creep model.

20 FOSSIL-FUELED POWER PLANTS↗