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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Goldsim Modeling of Vadose Zone Transport for E-Area Naval Reactor Component Disposal Areas: Model Description and Benchmarking

This report documents the development of a GoldSim® model of flow and radionuclide transport to the water table through the Naval Reactor Components Disposal Area (NRCDA) waste disposal sites and underlying vadose zones. The model is designed to be used for Monte Carlo uncertainty analysis in support of the E-Area Performance Assessment (PA). This report describes the model and shows results obtained from benchmarking the model to best-estimate deterministic results obtained using a PORFLOW model of NRCDA vadose zone transport. The PORFLOW model is three-dimensional while the GoldSim model is a simplified one-dimensional treatment. Nevertheless, the GoldSim model was able to accurately reproduce PORFLOW results with some adjustment to the nominal dispersion coefficient and vadose zone flow area used as “tuning” parameters. An example of the results obtained comparing GoldSim and PORFLOW calculation of releases of I-129, Tc-99, C-14 and Ni-59 from waste disposal containers at the 643-26E site is shown in Figure 1 below. For all of the test cases evaluated, GoldSim predicted peak concentrations within 6% of the PORFLOW values and peak times agreed within 8% with the majority of the results in better agreement. The close agreement between the two models provides confidence that GoldSim will give results accurately reflecting the behavior of releases from the NRCDA under off-normal operating conditions for sensitivity and uncertainty analysis

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Hybrid (PDE+ML) models in the context of land ice modeling

Focal Area(s): Primary focal areas are: predictive modeling through the use of AI-derived model components; advanced methods including network design/optimization/deep learning. Science Challenge: The atmospheric, ocean and ice dynamics components of the Energy Exascale Earth System Model (E3SM) are governed by Partial Differential Equations (PDEs) and significant efforts have been made during the last decades to develop such computational models. Here we propose to fundamentally improve these PDE-based codes by enhancing them with Machine Learning (ML) sub-models for complex, poorly understood physical processes in the context of ice sheet modeling. We propose to train these models with a novel approach that allows the assimilation of the different sources of data available (direct/indirect observations and possibly simulation data), improving on existing simplified models. We also highlight computational challenges originating from the coexistence of PDE-based and ML-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model Evaluation Protocol for Fire Models Involving Fuels at Liquefied Natural Gas Facilities.

This document provides a description of the model evaluation protocol (MEP) for pool fires, jet fires, and fireballs involving liquefied natural gas (LNG), refrigerant fluids, and byproducts at LNG facilities. The purpose of the MEP is to provide procedures regarding the assessment of a model's suitability to predict heat flux from fires. Three components, namely, a scientific assessment, model verification, and model validation comprise the MEP. The evaluation of a model satisfying these three components is to be documented in the form of a model evaluation report (MER). Discussion of models for the prediction of fire, detailed information on each of the three MEP components, the MEP procedure regarding new versions of previously approved models, and the format of the model evaluation report (MER) are provided.

03 NATURAL GAS↗
DOE OSTI2022

Evaluation of Reduced Order Model for HT-9 Creep and Modifications to Current HT-9 Creep Model in BISON

In order to leverage existing reduced order models (ROM) for the modeling of LANL-developed HT-9 cladding constitutive behavior, mechanistic-based models have been implemented into BISON. It is posited that the increased fidelity of the ROM will show improvement over engineering-scale models in terms of comparison against experimental measurements. INL will assist LANL in implementing an HT9 ROM in BISON. Once the HT-9 mechanistic constitutive model can be leveraged in nuclear performance simulations, the numerical results of fast reactor models will be analyzed and compared against the Fuels Irradiation & Physics Database (FIPD) and separate effects tests to attempt a validation process. INL and LANL will work together to perform any necessary improvement that is identified during the implementation and use processes. Successful completion of the milestone will enable the general use of validated mechanistic ROMs for HT-9 cladding in BISON. This report provides a brief introduction to the Los Alamos Reduced Order Model Applied to Nonlinear Constitutive Equations (LAROMANCE) code, mechanical testing used to illustrate its creep and plastic deformation predictions for HT-9 cladding, and a comparison with existing models implemented into BISON. Current models have been modified to more appropriately account for primary thermal creep and results for corresponding testing are included in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Updated Economic Model for Estimation of GDP Losses in the MACCS Offsite Consequence Analysis Code RDEIM Model Report for MACCS v4.2

This report updates the Regional Disruption Economic Impact Model (RDEIM) GDP-based model described in Bixler et al. (2020) used in the MACCS accident consequence analysis code. MACCS is the U.S. Nuclear Regulatory Commission (NRC) used to perform probabilistic health and economic consequence assessments for atmospheric releases of radionuclides. It is also used by international organizations, both reactor owners and regulators. It is intended and most commonly used for hypothetical accidents that could potentially occur in the future rather than to evaluate past accidents or to provide emergency response during an ongoing accident. It is designed to support probabilistic risk and consequence analyses and is used by the NRC, U.S. nuclear licensees, the Department of Energy, and international vendors, licensees, and regulators. The update of the RDEIM model in version 4.2 expresses the national recovery calculation explicitly, rather than implicitly as in the previous version. The calculation of the total national GDP losses remains unchanged. However, anticipated gains from recovery are now allocated across all the GDP loss types – direct, indirect, and induced – whereas in version 4.1, all recovery gains were accounted for in the indirect loss type. To achieve this, we’ve introduced new methodology to streamline and simplify the calculation of all types of losses and recovery. In addition, RDEIM includes other kinds of losses, including tangible wealth. This includes loss of tangible assets (e.g., depreciation) and accident expenditures (e.g., decontamination). This document describes the updated RDEIM economic model and provides examples of loss and recovery calculation, results analysis, and presentation. Changes to the tangible cost calculation and accident expenditures are described in section 2.2. The updates to the RDEIM input-output (I-O) model are not expected to affect the final benchmark results Bixler et al. (2020), as the RDEIM calculation for the total national GDP losses remains unchanged. The reader is referred to the MACCS revision history for other cost modelling changes since version 4.0 that may affect the benchmark. RDEIM has its roots in a code developed by Sandia National Laboratories for the Department of Homeland Security to estimate short-term losses from natural and manmade accidents, called the Regional Economic Accounting analysis tool (REAcct). This model was adapted and modified for MACCS. It is based on I-O theory, which is widely used in economic modeling. It accounts for direct losses to a disrupted region affected by an accident, indirect losses to the national economy due to disruption of the supply chain, and induced losses from reduced spending by displaced workers. RDEIM differs from REAcct in in its treatment and estimation of indirect loss multipliers, elimination of double-counting associated with inter-industry trade in the affected area, and that it is intended to be used for extended periods that can occur from a major nuclear reactor accident, such as the one that occurred at the Fukushima Daiichi site in Japan. Most input-output models do not account for economic adaptation and recovery, and in this regard RDEIM differs from its parent, REAcct, because it allows for a user-definable national recovery period. Implementation of a recovery period was one of several recommendations made by an independent peer review panel to ensure that RDEIM is state-of-practice. For this and several other reasons, RDEIM differs from REAcct.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Predicting High Energy Arcing Fault Zones of Influence for Aluminum Using an Arc Flash Modeling Approach: Evaluation of a model bias, uncertainty, parameter sensitivity and zone of influence estimation

This report documents the development of an arc flash hazard model to calculate the incident energy and zone of influence from high energy arcing faults involving aluminum. The NRC has identified the potential for (HEAFs) involving aluminum to increase the damage zone beyond what is currently postulated in fire probabilistic risk assessment (PRA) methodologies. To estimate the hazard from HEAFs involving aluminum an arc flash model was developed. Differences between the initial model and nuclear power plant (NPP) fire PRA scenarios were identified. Modification of the initial model established from existing literature and test data was used to minimize these differences. The developed model was evaluated against NRC datasets to understand the model prediction and relative uncertainties. Finally, a range of fire PRA zone of influences (ZOI) were developed based on the developed model, target fragility estimates and update HEAF PRA methodology. The results were developed to support an NRC LIC-504 evaluation in tandem with other modeling efforts. The report documents the effort and provides a reference for any future advancements in arc flash modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Assessment and validation of NEAMS tools for high-fidelity multiphysics transient modeling of microreactors: Application of NEAMS codes to perform multiphysics modeling analyses of micro-reactor concepts

The NEAMS Multiphysics Applications team aims at providing assessment of code useability and functionality for microreactor design and analyses, together with demonstration of their capabilities to properly capture the steady-state and time-dependent behavior of different microreactor concepts. In FY-24, significant progress was achieved in improving multi-physics models of several microreactors systems: HP-MR, GC-MR and KRUSTY. These efforts focused on solving more complex multiphysics problems enabled by enhanced tools capability, verifying and validating results obtained, providing feedback to developers for suggested improvements, and sharing these models to facilitate user training. A series of new multiphysics transients were completed on the HP-MR (using Griffin/BISON/Sockeye) with core startup transient, control drum inadvertent rotation accident, and hydrogen leakage from hydride moderator (also including SWIFT). On the GC-MR, a new full-core model was developed and analyzed through a series of new multiphysics (Griffin/BISON/SAM) transients to simulate moderator leakage (also including SWIFT), flow blockage and coolant depressurization. Additional and updated TRISO failure analyses were completed on the HP-MR unit-cell and GC-MR assembly models leveraging improved TRISO modeling capabilities. The amount of SiC failure following accidental transients at end-of-life was null. However, GC-MR assembly TRISO analysis highlighted Pd penetration rate can be problematic and may require design changes on the studied microreactor concept. The neutronics discrepancies observed on the KRUSTY model in previous years were resolved using hybrid set of Monte Carlo/Deterministic cross-sections. The multiphysics (Griffin neutronics / BISON thermal-mechanics) 15₵ insertion transient simulation displayed good agreement when comparing with experimental data. Initial modeling of the 30 ₵ reactivity insertion also displays promising results. Such close agreement provides important validation data that can be leveraged by the NEAMS program and by microreactor vendors to support licensing of their technology. Finally, important experience was gathered with the NEAMS tools leading to several user feedback shared with tools developers, especially with regards to MOOSE mesh generator and Griffin. This project led to many publications demonstrating modeling capabilities, and to three models shared on the Virtual Test Bed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Model Evaluation Protocol for Fire Models Involving Fuels at Liquefied Natural Gas Facilities (Version 2)

This document provides a description of the model evaluation protocol (MEP) for pool fires, jet fires, and fireballs involving liquefied natural gas (LNG) and processing fuels at LNG facilities. The purpose of the MEP is to provide procedures regarding the assessment of a model’s suitability to predict heat flux from fires. Three components, namely, a scientific assessment, model verification, and model validation comprise the MEP. The evaluation of a model satisfying these three components is to be documented in the form of a model evaluation report (MER). Discussion of models for the prediction of fire, detailed information on each of the three MEP components, the MEP procedure regarding new versions of previously approved models, and the format of the model evaluation report (MER) are provided.

03 NATURAL GAS↗
DOE OSTI2016

A physically based model of the ionizing radiation from active galaxies for photoionization modeling

We present a simplified model of active galactic nucleus (AGN) continuum emission designed for photoionization modeling. The new model oxaf reproduces the diversity of spectral shapes that arise in physically based models. We identify and explain degeneracies in the effects of AGN parameters on model spectral shapes, with a focus on the complete degeneracy between the black hole mass and AGN luminosity. Our reparametrized model oxaf removes these degeneracies and accepts three parameters that directly describe the output spectral shape: the energy of the peak of the accretion disk emission E{sub peak}, the photon power-law index of the non-thermal emission Γ, and the proportion of the total flux that is emitted in the non-thermal component p{sub NT}. The parameter E{sub peak} is presented as a function of the black hole mass, AGN luminosity, and “coronal radius” of the optxagnf model upon which oxaf is based. We show that the soft X-ray excess does not significantly affect photoionization modeling predictions of strong emission lines in Seyfert narrow-line regions. Despite its simplicity, oxaf accounts for opacity effects where the accretion disk is ionized because it inherits the “color correction” of optxagnf. We use a grid of mappings photoionization models with oxaf ionizing spectra to demonstrate how predicted emission-line ratios on standard optical diagnostic diagrams are sensitive to each of the three oxaf parameters. The oxaf code is publicly available in the Astrophysics Source Code Library.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Biomass burning emission analysis based on MODIS aerosol optical depth and AeroCom multi-model simulations: implications for model constraints and emission inventories

We assessed the biomass burning (BB) smoke aerosol optical depth (AOD) simulations of 11 global models that participated in the AeroCom phase III BB emission experiment. By comparing multi-model simulations and satellite observations in the vicinity of fires over 13 regions globally, we (1) assess model-simulated BB AOD performance as an indication of smoke source–strength, (2) identify regions where the common emission dataset used by the models might underestimate or overestimate smoke sources, and (3) assess model diversity and identify underlying causes as much as possible. Using satellite-derived AOD snapshots to constrain source strength works best where BB smoke from active sources dominates background non-BB aerosol, such as in boreal forest regions and over South America and southern hemispheric Africa. The comparison is inconclusive where the total AOD is low, as in many agricultural burning areas, and where the background is high, such as parts of India and China. Many inter-model BB AOD differences can be traced to differences in values for the mass ratio of organic aerosol to organic carbon, the BB aerosol mass extinction efficiency, and the aerosol loss rate from each model. The results point to a need for increased numbers of available BB cases for study in some regions and especially to a need for more extensive regional-to-global-scale measurements of aerosol loss rates and of detailed particle microphysical and optical properties; this would both better constrain models and help distinguish BB from other aerosol types in satellite retrievals. More generally, there is the need for additional efforts at constraining aerosol source strength and other model attributes with multi-platform observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0)

Abstract. Herein, we present a description of the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0). This iron processing module was developed for use within Earth system models and has been updated within a modal aerosol framework from the original implementation in a bulk aerosol model. MIMI simulates the emission and atmospheric processing of two main sources of iron in aerosol prior to deposition: mineral dust and combustion processes. Atmospheric dissolution of insoluble to soluble iron is parameterized by an acidic interstitial aerosol reaction and a separate in-cloud aerosol reaction scheme based on observations of enhanced aerosol iron solubility in the presence of oxalate. Updates include a more comprehensive treatment of combustion iron emissions, improvements to the iron dissolution scheme, and an improved physical dust mobilization scheme. An extensive dataset consisting predominantly of cruise-based observations was compiled to compare to the model. The annual mean modelled concentration of surface-level total iron compared well with observations but less so in the soluble fraction (iron solubility) for which observations are much more variable in space and time. Comparing model and observational data is sensitive to the definition of the average as well as the temporal and spatial range over which it is calculated. Through statistical analysis and examples, we show that a median or log-normal distribution is preferred when comparing with soluble iron observations. The iron solubility calculated at each model time step versus that calculated based on a ratio of the monthly mean values, which is routinely presented in aerosol studies and used in ocean biogeochemistry models, is on average globally one-third (34 %) higher. We redefined ocean deposition regions based on dominant iron emission sources and found that the daily variability in soluble iron simulated by MIMI was larger than that of previous model simulations. MIMI simulated a general increase in soluble iron deposition to Southern Hemisphere oceans by a factor of 2 to 4 compared with the previous version, which has implications for our understanding of the ocean biogeochemistry of these predominantly iron-limited ocean regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

CERF – A Geospatial Model for Assessing Future Energy Production Technology Expansion Feasibility

Electricity system expansion planning faces significant challenges not only because of commonly modeled uncertainties such as future demand growth, fuel prices, and technology cost and performance, but also because of challenges such as climate change and carbon policies that can affect supply and demand. Changes in energy technology, natural resource availability, and population migration and growth will affect the feasibility of power plant siting, potentially hindering the achievement of policy goals. New modeling approaches are needed that couple future projections of energy system expansion with “on the ground” feasibility modeling—where both are informed by a consistent set of assumptions about future climate trends, resource availability, climate policy, socioeconomics, and environmental regulations. This research describes the development and evaluation of the Capacity Expansion Regional Feasibility (CERF) model, a geospatial tool for scenario analysis of electricity system expansions.

grid expansion, water-energy nexus, power plant si↗
DOE OSTI2018

Need A Boost? A Comparison of Traditional Commuting Models with the XGBoost Model for Predicting Commuting Flows

Commuting models estimate the number of commuting trips from home to work locations in a given area. Since their infancy, they have been increasingly used in a variety of fields to reduce traffic and pollution, drive infrastructure choices, and solve a variety of other problems. Traditional commuting models, such as gravity and radiation models, typically have a strict structural form and limited number of input variables, which may limit their ability to predict commuting flows as well as machine learning models that might better capture the complex dynamics of the commuting process. To determine whether machine learning models might add value to the field of commuter flow prediction, we compare and discuss the performance of two standard traditional models with the XGBoost machine learning algorithm for predicting home to work commuter flows from a well-known United States commuting dataset. We find that the XGBoost model outperforms the traditional models on three commonly used metrics, indicating that machine learning models may add value to the field of commuter flow prediction.

Morton, April↗
DOE OSTI2016

Comparison of Surrogate Models with Physical Models for Dynamic Probabilistic Risk Analysis Using the RAVEN Code

Dynamic probabilistic risk analysis (DPRA) has emerged in the nuclear industry as an effective tool to identify points of weakness in nuclear reactors and create a comprehensive approach to quantifying risk. While DPRA allows for detailed analysis, it usually requires thousands of runs due to the necessity of exploring low probability, high consequence events, which can be computationally prohibitively difficult to assess. Risk Analysis Virtual Environment (RAVEN) is a software package being developed under the Nuclear Energy Advanced Modeling and Simulation program at the Idaho National Laboratory. It is able to manage complex control logic to drive a RELAP-7 simulation and to handle sampling strategies including Monte Carlo, Latin Hypercube, Grid and Adaptive samplers. By performing a thorough investigation of the possibility space of an accident scenario with RELAP-7, RAVEN is able to identify consequences of accident scenarios that have not been encountered. The most computational resource intensive step of a RAVEN analysis is running the RELAP-7 code modeling the physical system behavior. Using surrogate models to represent the outcomes of more complex simulations rather than the full model can greatly reduce the computation effort required. In situations where the uncertainty distributions of a model are unknown, or when there are multiple sets of uncertainty distributions, DPRA generally requires rerunning the physical model for each set of uncertainty distributions used. By taking an unbiased sample of the physical model and creating a surrogate model from the results, the surrogate model can be sampled under a different set of uncertainty distributions to provide equivalent results. This paper presents an example of this approach. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Drivers of Change in Managed Water Resources: Modeling the Impacts of Climate and Socioeconomic Changes Using the US Midwest as a Case Study

A global integrated assessment model including a water-demand model driven by socio-economics, is coupled in a one-way fashion with a land surface hydrology – routing – water resources management model. The integrated modeling framework is applied to the U.S. Upper Midwest (Missouri, Upper Mississippi, and Ohio) to advance understanding of the regional impacts of climate and socio-economic changes on integrated water resources. Implications for future flow regulation, water supply, and supply deficit are investigated using climate change projections with the B1 and A2 emission scenarios, which affect both natural flow and water demand. Changes in water demand are driven by socio-economic factors, energy and food demands, global markets and prices. The framework identifies the multiple spatial scales of interactions between the drivers of changes (natural flow and water demand) and the managed water resources (regulated flow, supply and supply deficit). The contribution of the different drivers of change are quantified regionally, and also evaluated locally, using covariances. The integrated framework shows that water supply deficit is more predictable over the Missouri than the other regions in the Midwest. The predictability of the supply deficit mostly comes from long term changes in water demand although changes in runoff has a greater contribution, comparable to the contribution of changes in demand, over shorter time periods. The integrated framework also shows that spatially, water demand drives local supply deficit. Using elasticity, the sensitivity of supply deficit to drivers of change is established. The supply deficit is found to be more sensitive to changes in runoff than to changes in demand regionally. It contrasts with the covariance analysis that shows that water demand is the dominant driver of supply deficit over the analysed periods. The elasticity indicates the level of mitigation needed to control the demand in order to reduce the vulnerability of the integrated system in future periods. The elasticity analyses also emphasize the need to address uncertainty with respect to changes in natural flow in integrated assessment.

climate change, water resources management, socio-↗
DOE OSTI2019

Local composition activity coefficient model for mixed-gas adsorption equilibria

Taking into consideration the adsorbate–adsorbent interactions, a novel activity coefficient model is derived from the non-random two-liquid theory for mixed-gas adsorption equilibria. In contrast with the conventional activity coefficient models developed for bulk liquids, the new model correctly predicts negative deviations from ideality for adsorbed phase mixtures including azeotropic behavior exhibited by selected gas-adsorbent systems. Here, the requiring a single binary interaction parameter per adsorbate–adsorbate pair, the model successfully correlates wide varieties of binary adsorption isotherm data and it should be a powerful engineering thermodynamic tool in correlating and predicting mixed-gas adsorption equilibria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Exploring the use of dynamic linear panel data models for evaluating energy/economy/environment models — an application for the transportation sector

This paper evaluates the RoSE transportation sector scenarios of the GCAM and REMIND models for the U.S. region by comparing these models’ projected future final energy use levels (1) to historical trends and (2) across each other. To estimate and compare the intrinsic model elasticities, we explore the use of dynamic linear panel data models. On the basis of 26 scenarios (panels) from each of the two energy system models, our analysis suggests that most intrinsic model elasticities with respect to final energy use are roughly comparable to each other and to those observed historically. The key difference is the income elasticity of final energy use, which is significantly smaller for the two energy models than that derived from the historical development. This difference and other minor inconsistencies are interpreted through key assumptions underlying both energy system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Capturing the variable reactivity of goethites in surface complexation modeling by correlating model parameters with specific surface area

The development of predictive tools capable of simulating adsorption behavior on goethite (α-FeOOH) surfaces has evolved over the past several decades. Recent research has focused on capturing the variation of surface reactivity and adsorption behavior for different preparations of synthetic goethites. Here in this research, an approach to estimate modeling parameters based on the specific surface area (SSA) of goethites was established and combined with a Charge Distribution – Multi-site Complexation (CD-MUSIC) surface complexation model. It was assumed that differences in crystal face contributions (CFC), inner-Helmholtz capacitances (C 1 ), and protonation constants of surface oxygens (pK a ’s) among different preparations of goethite contribute to the variation of surface reactivity. Thus, correlations of these modeling parameters with the SSA of a range of goethites were identified by comprehensively combining microscopic, macroscopic, and modeling data reported in literature along with adsorption data collected from macroscopic experiments conducted as part of this study. A CD-MUSIC model shown to be capable of accurately predicting adsorption for several metal ions and oxyanion selenite on a 62.9 m 2 /g SSA goethite served as the basis for the development of the adsorption modeling approach described in this paper. The predictive capability of the newly developed modeling approach was verified by comparing predictions of protons, SeO 3 2- , and Cd 2+ adsorption with experimental data from goethites with SSAs ranging between 21.3 and 105 m 2 /g; in total, 40 adsorption data sets from 13 different goethites were used for model verification. The results from this study indicate that the variation of surface reactivity of goethites can be successfully simulated by changing CFC, C 1 , and pK 2,singly (i.e. second protonation constant of singly-coordinated oxygens on the goethite surface) based on the SSA, which implies that self-consistent adsorption parameters can be applied to predict adsorption behavior over a range of goethite preparations.

58 GEOSCIENCES↗