Results for “Data Characterization” — Page 5

DOE OSTI2021

Advanced Cost-Effective Coal-Fired Rotating Detonation Combustor for High Efficiency Power Generation

Coal-based detonation as a means of heat addition in power generation devices is a revolutionary “out of the box” technology concept, wherein it is theoretically possible to achieve an increase in total pressure across the combustor as opposed to a loss of available energy as is the case in conventional combustion systems. Detonation is a revolutionary technology concept of pressure gain combustion that exploits pressure rise from the combustion process to augment high flow momentum and pressures. Recognizing that pressure gain combustion has become a topic of elevated national interest, high energetic modes (exergy) pressure gain combustion (PGC) systems are required. Pressure gain combustion is a key enabling technology for maintaining technological superiority in the development of advanced power generation systems. Pressure gain combustion is an innovative scheme of turbulent combustion that considerably increases thermodynamic cycle efficiencies (~10-20%). This project extends the State-of-the-Art by providing the first direct experimental and computational data of operability dynamics and performance characterization data and create the first validated Coal-Fired RDC investigations. Coal-based RDC is an innovative revolutionary technology that would result in high efficiency power generation and reduced of harmful emissions. Also, the fundamental and open source nature of the project will benefit a broader scientific and industry audience who will be able to utilize the outcomes of this project for advancing the coal-fired RDC power generation development.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Accelerating Additive Manufacturing Process Design for Energy Conversion Materials using In-situ Sensing and Machine Learning

One promising candidate for manufacturing of the bismuth Telluride thermoelectric legs is laser powder bed fusion (LPBF) additive Manufacturing (AM). AM processing parameters highly influence the material properties, however current processing parameter development methods in AM are costly and time consuming. In-situ sensors allow for the capture of physically relevant process information on a layer-by-layer basis and will be used to aide process development. To optimize the AM process for the best thermoelectric performance, process variables, in-situ process sensor data and ex-situ material characterization data are collected. Several different interpretable machine learning (ML) approaches are used, and the performance of each method are assessed. Significant input process variables include laser focus, hatch spacing and laser power. The best performing models are used to determine the manufacturing parameters that maximize the power factor. AM of bismuth telluride material provides the ability to create complex geometries enabling more efficient energy conversion.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Determining the effects of U/Pu ratio on subsolidus phase transitions in U-Pu-Zr metallic fuel alloys

Here, ternary alloys consisting primarily of uranium, plutonium, and zirconium (U-Pu-Zr) are among the leading candidate fuel systems considered for fast spectrum nuclear reactors. Despite historical operation data from the testing of U-Pu-Zr rods in the Experimental Breeder Reactor-II, considerable uncertainty about the evolution of phases and microstructure across the ternary composition space exists. Due to sluggish kinetics and other difficulties in handling metal actinide specimens, quantitative measurements of phase-transitions in U-Pu-Zr alloys remain sparse in scientific literature, with most investigators reporting either phase-transition temperatures or phase identification data, but not both from the same specimens. The purpose of this paper is to critically compare experimental and calculated phase transition data and correlate with the microstructure and phase characterization data of as-cast and annealed U-Pu-Zr alloys. Phase transition peaks were measured using differential scanning calorimetry in the subsolidus regions (723-948 K) of three ternary U-Pu-Zr alloys with the same zirconium concentration but various U/Pu ratios. Overlapping peaks were deconvoluted using a Frazier-Suzuki peak fitting algorithm, and the critical peak temperatures and enthalpies were calculated. In general, increasing concentrations of Pu were associated with enhanced thermal stability of the body-centered cubic γ phase upon both heating and cooling. Experimental phase transition temperatures in this study tended to agree well with the predictions of the established ternary phase diagrams and other reported phase transition temperatures in literature. Additionally, the TAF-ID thermodynamic database was used to calculate a U-Pu-40 at.% Zr pseudobinary diagram as well as ternary diagrams from 773 to 973 K. The equilibrium phase transition temperatures tended to be considerably lower than measured peak temperatures upon both heating and cooling. Recommendations for improving the quality of data in future U-Pu-Zr characterization studies are also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study: Final Report

Collect and evaluate data on federal fleet operations as part of the Advanced Vehicle Testing Activity’s Federal Fleet Vehicle Data Logging and Characterization Study. The Advanced Vehicle Testing Activity study seeks to collect and evaluate data to validate the utilization of advanced plug-in electric vehicle (PEV) transportation. This report summarizes the fleets studied to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively referred to as PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

Analysis of Graphite Materials for Fast-Charging Capabilities in Lithium-Ion Batteries

The application of lithium-ion batteries in electric vehicles calls for decreased cost, increased energy density, increased power density, as well as low-temperature and fast-charging capability. In regard to fast charging, it is generally understood that it is the kinetic overpotential at the graphite anode that limits the cycling rate in modern commercial battery formulations. Here, we study the fast-charging capabilities of a series of graphite samples from commercial suppliers, as part of a materials validation effort at Argonne National Laboratory. Lastly, we combine materials characterization data from the raw samples with electrochemical characterization of graphite laminate electrodes in full-cell configuration to explore the causes of differences in rate performance and its impact on cycle life.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Manuscript Workflows from and Processed Organic Matter Composition of Experimentally Burned Open Air and Muffle Furnace Vegetation Chars across Differing Burn Severity and Feedstock Types from Pacific Northwest, USA (v3)

This dataset includes processed organic matter chemistry data from an experimental study designed to compare how the chemical composition of organic matter changes across different burn conditions and vegetation materials representative of major land cover types of the Pacific Northwest, USA. Chars were created in a closed muffle furnace or on an open burn table from four different feedstock species representing vegetation commonly impacted by fire regimes across the Pacific Northwest, USA. Source data and associated metadata (including methods and geospatial information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022). This dataset provides processing scripts and processed data for both solid and dissolved phase organic matter characterization data from experimentally generated chars. These processed data can be used to compare how different burn conditions may influence resultant organic matter chemistry and help further our understanding of potential biogeochemical impacts on river corridors post-fire. The processed data were subsequently analyzed; and the results and ecological implications of the findings were published in peer-reviewed manuscripts. The scripts and workflows used to develop the manuscripts are also included in this data package.This data package was originally published June 2024. It was updated September 2024 (new and modified files) and in January 2025 (modified files). See the change history section in the readme for more details.This dataset is comprised of one data package readme, one data dictionary (dd), one file level metadata (flmd), and folders containing (A) processed data; (B) general processing scripts; and (C) additional folders with specific manuscript analysis scripts and processed data. Step-by-step instructions to assist the user in recreating the workflow used to generate the results in the manuscripts is also provided. The processed data folder includes (1) a folder of processed Parallel Factor Analysis (PARAFAC) and spectra indices outputs from excitation emissions matrix (EEM) fluorescence and absorbance data; (2) a folder of processed solid state carbon-13 (13-C NMR) integrals; (3) folder of high resolution characterization of organic matter via 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) processed data outputs from Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude), blank corrections and data aggregation, and calculated molecular indices. All files are .pdf, .csv, .html, .Rmd, .R, or .RData.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A critical analysis of U-Pu-Zr phase transitions using calorimetric, microstructural, and phase equilibria data

Metallic fuels consisting primarily of uranium, plutonium, and zirconium (U-Pu-Zr) are a leading material candidate for fast-spectrum nuclear reactors. Early demonstration programs proved the principle of safe and efficient fast reactor operation, however there is still considerable uncertainty regarding the phase equilibria and microstructural evolution across the ternary composition space. Quantitative phase formation and identification measurements are scarce and often incomplete, with studies reporting either phase transition temperatures or phase identification data, but not both from the same specimens. In this study, we critically compared experimental and calculated phase transition data and correlated with the microstructure and phase characterization data of as-cast and annealed U-Pu-Zr alloys. Differential scanning calorimetry (DSC) was used to measure phase transitions in the subsolidus regions (723−948 K) of three ternary U-Pu-Zr alloys with similar plutonium concentrations but various U/Zr ratios. Due to sluggish kinetics and narrow ranges of phase stability, complex peaks required the use of a Frazier-Suzuki peak fitting algorithm to deconvolute and calculate transition peak temperatures and enthalpies. We also identified trends of phase transition behavior by critically comparing our DSC data with previous phase transition measurements as well as historical and calculated phase equilibrium diagrams. In conclusion, this provides a critical approach for benchmarking and assessing the quality of new U-Pu-Zr phase equilibria data prior to its incorporation into nuclear material databases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Gas Adsorption Analyzer

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

3Flex,BET specific surface area and pore volume, A↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) X-Ray Diffraction

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Cryogenic Refrigerator Applied to Freezing Test

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Aerodynamic Particle Sizer, Condensation Particle Counter, and Meteorological Instrument Data Analysis

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Snake River Plain FORGE: Well Data for USGS-142

Well data for the USGS-142 well located in eastern Snake River Plain, Idaho. This data collection includes lithology reports, borehole logs, and photos of rhyolite core samples. This collection of data has been assembled as part of the site characterization data used to develop the conceptual geologic model for the Snake River Plain site in Idaho, as part of phase 1 of the Frontier Observatory for Research in Geothermal Energy (FORGE) initiative. They were assembled by the Snake River Geothermal Consortium (SRGC), a team of collaborators that includes members from national laboratories, universities, industry, and federal agencies, lead by the Idaho National Laboratory (INL).

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

A Semi-Empirical Density Law for Ternary, Homogeneous PuCl 3 /HCl/H 2 O Solutions

Nuclear material operations pose unique hazards that are not encountered in other chemical, energy, or manufacturing industries. One of these hazards is the potential for a nuclear criticality accident when handling fissile isotopes such as 235 U and 239 Pu. These hazards are particularly high when fissile material is dissolved in solution as the neutron behaviors of the system can change rapidly with the physical and chemical changes accessible in solution. Current estimates of solution density used for criticality safety are outdated and hinder fissile material handling. Developing new estimates for these safety calculations requires experimental characterization and the derivation of empirical density models. We have derived a density law describing PuCl 3 /HCl/H 2 O solutions from experimental data characterizing solution density. Density data was treated using a Pitzer-derived eight-parameter equation, defining density as a function of analyte concentrations, temperature, and interactions between these variables. The model is predictive across the concentration and temperature ranges from which it was derived. The potential effects of varying oxidation states of plutonium, which are easily accessible in aqueous media, on the bulk solution density of the ternary system were also investigated. The resulting Pitzer-derived density law was applied to a nuclear criticality safety model, and the impact of the experimental characterization of solution density relative to previous estimates was demonstrated to be significant and suggest that the current approach to estimating density in nuclear criticality safety calculations may lead to overly conservative controls.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Exploring the effects of data quality, data worth, and redundancy of CO2 gas pressure and saturation data on reservoir characterization through PEST Inversion

This study examined the impacts of reservoir properties on CO2 migration after subsurface injection and evaluated the possibility of characterizing reservoir properties using CO2 monitoring data such as saturation distribution. The injection reservoir was assumed to be located 1400-1500 m below the ground surface such that CO2 remained in the supercritical state. The reservoir was assumed to contain layers with alternating conductive and resistive properties, which is analogous to actual geological formations such as the Mount Simon Sandstone unit. The CO2 injection simulation used a cylindrical grid setting in which the injection well was situated at the center of the domain, which extended up to 8000 m from the injection well. The CO2 migration was simulated using the PNNL-developed simulator STOMP-CO2e (the water-salt-CO2 module). We adopted a nonlinear parameter estimation and optimization modeling software package, PEST, for automated reservoir parameter estimation. We explored the effects of data quality, data worth, and data redundancy on the detectability of reservoir parameters using CO2 saturation monitoring data, by comparing PEST inversion results using data with different levels of noises, various numbers of monitoring wells and locations, and different data collection spacing and temporal sampling intervals. This study yielded insight into the use of CO2 saturation monitoring data for reservoir characterization and how to design the monitoring system to optimize data worth and reduce data redundancy.

Carbon sequestration↗

DOE OSTI2013

Feedback-Based, System-Level Properties of Vertebrate-Microbial Interactions

Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered. To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/ EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions. In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections. More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Confirmatory Defect Analyses of AGR-5/6/7 Fuel

The Advanced Gas Reactor (AGR) Fuel Development and Qualification Program has executed a series of experiments to test and qualify tristructural isotropic (TRISO) particle fuels for use in high-temperature gas-cooled reactors. The most recent irradiation experiments, AGR-5/6/7, are testing fuel that was fabricated in near production-scale equipment. There are three important defect fractions that must be characterized, using a deconsolidation-leach-burn-leach (DLBL) method, to better predict and understand fuel performance during irradiation. These are the dispersed uranium fraction (DUF), exposed kernel fraction (EKF), and silicon carbide defect fraction (SDF). The DLBL characterization data from analyses of the four compact batches, fabricated and characterized by the BWX Technologies Nuclear Operations Group (BWXT-NOG), showed unexpectedly higher values for all three defect fractions relative to the parent TRISO particle defect fractions. The DUF and SDF should not have changed significantly during compact fabrication and the apparent increase in the EKF, a metric for particle damage, was higher than anticipated for the compacts with 40% volumetric packing fraction (PF). Analytical results showed no net increase in the EKF for the 25% PF compacts. Compacts from one compact batch of each packing fraction were sent to Oak Ridge National Laboratory (ORNL), along with uncompacted tristructural isotropic (TRISO) particles, overcoated to a thickness targeting a 40% PF, for confirmatory analyses of the defect fractions and determination of the source(s) of damage. The ORNL results did not confirm the BWXT-NOG results for either the DUF or the SDF; the ORNL results showed no significant change in these defect fractions relative to the parent TRISO particle lot. The ORNL results did, however, confirm that a large increase in the exposed kernel fraction was found in the 40% PF compacts and that the 25% PF compacts sustained little if any damage. Data suggest that a large majority of the mechanical damage to the TRISO particles occurred in the overcoater when targeting the 40% PF. The analytical laboratories at both BWXT-NOG and ORNL had inconsistent outcomes on analyses of individual batches. Although the ORNL data is more consistent with the parent TRISO lot and expectations, the data set is too limited in size to justifiably reject suspect data from either laboratory at this time. It is recommended that an additional analysis be done to provide sufficient data for estimating the true defect fractions for interpreting the radionuclide release data during the AGR-5/6/7 irradiation experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Developing an Automated Uncertainty Quantification Tool to Improve Watershed-Scale Predictions of Water and Nutrient Cycling

Managing the flow of water, nutrients, and contaminants in watersheds is vital to addressing pressing issues related to water scarcity, access to clean drinking water, energy production, resilience to natural and anthropogenic perturbations, and ecological restoration. Decisions about the management of watersheds critically depend on the accuracy with which the flow of water and chemicals through the watershed can be predicted by computer models. Prediction uncertainty can be reduced by matching the model to data, which are collected in the field at great expense. The contribution of watershed characterization data to reducing uncertainty of relevant model predictions can be evaluated in a so-called data-worth analysis, which provides transparent, quantitative metrics about a data set’s value for the support of relevant watershed management objectives. To achieve this goal, we developed a software package that implements the data-worth analysis approach for use with state-of-the-art watershed models. The purpose of the proposed data-worth analysis is to help decision-makers allocate resources for watershed characterization such that the uncertainty in model predictions can be significantly reduced, which leads to better, more effective management decisions. At the same time, watershed characterization costs can be reduced. The specific technical objectives of this SBIR/STTR Phase II project were to develop a framework and associated software toolsets that implement the uncertainty quantification and data-worth analysis approach for use with state-of-the-art watershed models. This goal was achieved by (A) developing a user-friendly, robust software package that is accessible to a wide audience, including watershed managers, policy-makers, and public stakeholders; (B) by demonstrating application of the prototype on several use cases that are representative of complex watershed management challenges spanning a range of scales and that consider different open-source, DOE-based codes and other modeling platforms; and (C) by gathering information about the needs and requirements from potential users to help guide future developments, ensuring that the final product will be commercially viable. The developed software consists of a graphical user interface that guides the user through a sequence of analysis steps, supported by toolsets that leverage state-of-the-art computational simulation-optimization capabilities. A prototype of the software runs on multiple platforms (PC, Mac, multi-processor Linux environment), is linked to diverse watershed simulators (e.g., ECOSYS, TOUGH2, TOUGHREACT, Amanzi-ATS), performs multiple analysis tasks (predictive simulations, sensitivity analysis, uncertainty analysis, automatic parameter estimation, and data-worth analysis, multicomponent geothermometry), and is readily extensible to include external simulators and analysis tools. The software is being commercialized and will be continually updated to address user needs.

58 GEOSCIENCES↗