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DOE OSTI2022

North China Plain as a hot spot of ozone pollution exacerbated by extreme high temperatures

Abstract. A large population in China has been exposed to both severe ozone (O3) pollution and extreme heat under global warming. Here, the spatiotemporal characteristics of coupled extremes in surface O3 and heat (OPCs) over China are investigated using surface observations, a process-based chemical transport model (GEOS-Chem), and multi-model simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). North China Plain (NCP; 37–41∘ N; 114–120∘ E) is identified as a hot spot of OPCs, where more than half of the O3 pollution days are accompanied by high temperature extremes. OPCs over NCP exceeded 40 d during 2014–2019, exhibiting an increasing trend. Both O3 concentrations and temperatures are elevated during OPCs compared with O3 pollution days occurring individually (OPIs). Therefore, OPCs impose more severe health impacts to humans than OPIs, but the stronger health effects are mainly driven by the higher temperatures. GEOS-Chem simulations further reveal that enhanced chemical production resulting from hot and stable atmospheric conditions under anomalous weather patterns primarily contributes to the exacerbated O3 levels during OPCs. In the future, CMIP6 projections suggest increased occurrences of OPCs over NCP in the middle of this century, but by the end of this century, OPCs may decrease or increase depending on the pollutant emission scenarios. However, for all future scenarios, extreme high temperatures will play an increasingly important role in modulating O3 pollution in a warming climate.

Wang, Pinya (ORCID:0000000170347868)↗

DOE OSTI2021

Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.

02 PETROLEUM↗

DOE OSTI2021

Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations

Consequences from a slowdown or collapse of the Atlantic meridional overturning circulation (AMOC) could include modulations to El Niño–Southern Oscillation (ENSO) and development of the Pacific meridional overturning circulation (PMOC). Despite potential ramifications to the global climate, our understanding of the influence of various AMOC and PMOC states on ENSO and global sea surface temperatures (SSTs) remains limited. Five multicentennial, fully coupled model simulations created with the Community Earth System Model were used to explore the influence of AMOC and PMOC on global SSTs and ENSO. We found that the amplitude of annual cycle SSTs across the tropical Pacific decreases and ENSO amplitude increases as a result of an AMOC shutdown, irrespective of PMOC development. However, active deep overturning circulations in both the Atlantic and Pacific basins reduce ENSO amplitude and variance of monthly SSTs globally. Furthermore, the underlying physical reasons for changes to global SSTs and ENSO are also discussed, with the atmospheric and oceanic mechanisms that drive changes to ENSO amplitude differing based on PMOC state. These results suggest that if climate simulations projecting AMOC weakening are realized, compounding climate impacts could occur given the far-reaching ENSO teleconnections to extreme weather and climate events. More broadly, these results provide us with insight into past geologic era climate states, when PMOC was active.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Model bias in simulating major chemical components of PM 2.5 in China

High concentrations of PM 2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) in China have caused severe visibility degradation. Accurate simulations of PM 2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive datasets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. The model is unable to capture some of the main features in the diurnal pattern of the PM 2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Co-simulation Framework for Community-scale Building-grid Integration [SWR-21-75]

Distributed energy resources (DERs), including rooftop solar, energy storage, and flexible loads, are gaining popularity as costs decline and as building owners and utilities realize their benefits. DERs can improve distribution system efficiency, help prevent the need for expensive grid upgrades, and increase the resilience of local communities. However, they can also cause difficulties in grid operations and can require controls to achieve their benefits. To address this challenge, NREL researchers have developed a community-scale solution that assesses the impacts of DERs and their control strategies on a distribution system. The framework has been shown to reduce solar photovoltaic (PV) curtailment to 0%, mitigate the adverse impact of solar variability on the distribution voltage, and provide up to 5-day critical load support during emergency events. Utilizing 5 different modules representing the feeder, buildings, home energy management systems, an aggregator, and a utility controller, NREL expects this simulation technology to play a critical role in the continued integration of DERs. According to the Energy Information Administration (EIA), solar curtailments accounted for 94% of the total energy curtailed in the California Independent System Operator (CAISO) in 2020. By enabling Independent System Operators (ISOs) and utility operators to bring solar curtailments to 0%, the electrical grid can become less dependent on fossil-fueled power generation sources. NREL's co-simulation framework contains five major components: Distribution Feeder Model: describes the distribution feeder topology using OpenDSS, including the locations of all DERs. Residential Building Model: simulates a large number of buildings at a high resolution using OCHRETM. The model is equipped to control equipment based on signals from an external module. The model includes major household appliances such as HVAC and a water heater, non-dispatchable load models, a distributed PV system, and a home battery system. Home Energy Management System: optimizes the controls for the devices in a home using foreseeTM. The control can adjust based on the user preferences including cost, comfort, and convenience. In hierarchical control scenarios, where the houses follow signals from an aggregator, the home energy management system provides a flexibility band with a range of power and follows the dispatch signals received from aggregator. Community-Level Aggregator: solves for optimal energy dispatch based on the flexibility bands received from each home and the grid service signal received from the utility controller. Utility-Level Controller: provides grid signals for voltage control using Distributed Energy Resources (DERs), such as solar systems, in the community.

Balamurugan, Sivasathya Pradha↗

DOE OSTI2024

Timescale Dependence of the Precipitation Response to CO 2 ‐Induced Warming in Millennial‐Length Climate Simulations

Abstract Previous work has shown that estimates of climate sensitivity vary over time in response to abrupt CO 2 forcing in climate model simulations. The energy fluxes that drive warming in response to increasing CO 2 also influence precipitation, which prompts the question: Does the precipitation response therefore also vary over time? We investigate by examining the response of precipitation to warming forced by greenhouse gases—the hydrological sensitivity—in a set of millennial‐length climate simulations with multiple climate models, Long Run Model Intercomparison Project (LongRunMIP). We compare hydrological sensitivity calculated from three different timescales of the simulations: years 1–20, 21–150, and 151–1000. We show that the hydrological sensitivity lacks a consistent dependence on timescale, in contrast to climate sensitivity. Decomposition of the surface energy budget reveals that the relative muting of the multi‐model mean hydrological sensitivity is driven by surface downwelling shortwave flux.

Him (Kinen) Kao, Wing↗

DOE OSTI2022

Large balancing areas and dispersed renewable investment enhance grid flexibility in a renewable-dominant power system in China

Renewable energy is poised to play a major role in achieving China's carbon neutrality goal by 2060; however, reliability and flexibility is a big concern of a renewable-dominant power system. Various strategies of enhancing flexibility are under discussion to ensure the reliability of such a system, but no detailed quantitative analysis has been reported yet in China. We combine the advantages of a capacity expansion model, SWITCH-China, with a production simulation model, PLEXOS, and analyze flexibility options under different scenarios of a renewable-dominant power system in China. We find that a larger balancing area offers direct flexibility benefits. Regional balancing could reduce the renewable curtailment rate by 5-7%, compared with a provincial balancing strategy. National balancing could further reduce the power cost by about 16%. However, retrofitting coal power plants for flexible operation would only improve the system flexibility marginally.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Weakening of the Extratropical Storm Tracks in Solar Geoengineering Scenarios

Solar geoengineering that aims to offset global warming could nonetheless alter atmospheric temperature gradients and humidity and thus affect the extratropical storm tracks. Here, we first analyze climate model simulations from experiment G1 of the Geoengineering Model Intercomparison Project, in which a reduction in incoming solar radiation balances a quadrupling of CO 2 . The Northern Hemisphere extratropical storm track weakens by a comparable amount in G1 as it does for increased CO2 only. The Southern Hemisphere storm track also weakens in G1, in contrast to a strengthening and poleward shift for increased CO 2 . Using mean available potential energy, we show that the changes in zonal-mean temperature and humidity are sufficient to explain the different responses of storm-track intensity. We also demonstrate similar weakening in a more complex geoengineering scenario. Here, our results offer insight into how geoengineering affects storm tracks, highlighting the potential for geoengineering to induce novel climate changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

The Changing Influence of Precipitation on Soil Moisture Drought With Warming in the Mediterranean and Western North America

Abstract Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.

Environmental Sciences & Ecology↗

DOE OSTI2021

Response of the Indian Summer Monsoon to Global Warming, Solar Geoengineering and its Termination

The response of the Indian Summer Monsoon (ISM) to global warming, solar geoengineering and its termination is examined using the multi-model mean of eight global climate model simulations from G2 experiment of the Geoengineering Model Intercomparison Project. Under the global warming scenario, land-ocean temperature contrasts and low-level monsoon circulation progressively strengthen accompanied by enhanced precipitation over the Indian subcontinent. Notably, in the solar geoengineered scenario, marginal surface cooling is projected over the majority of the ISM region, and there is strengthening of both upper and lower level circulation. Upon the termination of the geoengineering, the climatic conditions temperature, precipitation, and winds — would abruptly change to what it would have been under the global warming scenario, and such abrupt change may cause devastating consequences.

Bhowmick, Mansi↗

DOE OSTI2022

Anthropogenic sulfate aerosol pollution in South and East Asia induces increased summer precipitation over arid Central Asia

Precipitation has increased across the arid Central Asia region over recent decades. However, the underlying mechanisms of this trend are poorly understood. Here, we analyze multi-model simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) to investigate potential drivers of the observed precipitation trend. We find that anthropogenic sulfate aerosols over remote polluted regions in South and East Asia lead to increased summer precipitation, especially convective and extreme precipitation, in arid Central Asia. Elevated concentrations of sulfate aerosols over remote polluted Asia cause an equatorward shift of the Asian Westerly Jet Stream through a fast response to cooling of the local atmosphere at mid-latitudes. This shift favours moisture supply from low-latitudes and moisture flux convergence over arid Central Asia, which is confirmed by a moisture budget analysis. High levels of absorbing black carbon lead to opposing changes in the Asian Westerly Jet Stream and reduced local precipitation, which can mask the impact of sulfate aerosols. This teleconnection between arid Central Asia precipitation and anthropogenic aerosols in remote Asian polluted regions highlights long-range impacts of anthropogenic aerosols on atmospheric circulations and the hydrological cycle.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

PCMDI PMP Interactive Visualization Web Page, Atmospheric River Metrics

We present an open-source Python-based framework that can be used for objective evaluation of Atmospheric Rivers (ARs) in climate models. With a comprehensive pool of pre-defined metrics, this package is useful for systematic AR evaluation across a range of model simulations, such as the MIPs in CMIP5/6 models and large ensemble experiments.

Dong, Bo↗

DOE OSTI2022

A Positive Low Cloud–Sea Surface Temperature Feedback in the East Asian Marginal Seas during El Niño Mature Winters and Their Following Spring

During the winter and subsequent spring of an El Niño year, the East Asian marginal sea (EAMS) exhibits positive sea surface temperature anomalies (SSTAs) and fewer low clouds, while the western North Pacific experiences negative SSTAs. In this study, we suggest that the positive SSTAs in EAMS are maintained by a positive low cloud–SST feedback. In neutral winters and springs, the EAMS is covered by low clouds, which have a cooling effect on surface temperatures. During an El Niño year, a western North Pacific anomalous anticyclone is established, and along its northwestern flank, there are favorable conditions for convergence of moisture and weaker surface latent heat flux over the EAMS. Once a positive SSTA has been established, a further reduction of turbulent mixing results in less low cloud and enhanced solar heating of the ocean mixed layer; this reinforces and maintains both the positive SSTA and the lack of low cloud via a positive feedback mechanism. Further, the concurrent increase of low cloud–SST feedback and anticyclone circulation strengths is evident in the coupled-model simulations from phase 6 of the Coupled Model Intercomparison Project. Furthermore, sensitivity experiments, performed with the atmospheric components of Community Earth System Model (CESM2), reveal that a positive SSTA helps to maintain the western North Pacific anomalous anticyclone. Four pacemaker-coupled experiments by CESM2, with sea surface temperature in the equatorial Pacific restored to the observational anomalies plus the model climatology and altered low cloud feedback over EAMS, suggest that the low cloud–SST feedback results in more than the maintenance of a positive SSTA over the EAMS: the positive feedback is also a previously overlooked mechanism for the maintenance of the western North Pacific anomalous anticyclone.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Advancing Understanding of Deep Convective Anvil Clouds

In this project we use ground-based DOE ARM observations, satellite observations, and Weather Research and Forecasting (WRF) model simulations to arrive at a more complete understanding (or, new theory) of how atmospheric heating profiles in thunderstorm cores drives how quickly air rises in the storms. The rate at which air motion increases or decreases with altitude directly relates to how much cloud mass is ejected laterally into the adjacent atmosphere, which then influences the development and maintenance of “stratiform raining cloud anvils”. Our work has led to new equations connecting the heating in these thunderstorms to these stratiform clouds, with the connection modulated by the stability structure of the atmosphere (i.e., if more stable, then a more rapid change in thunderstorm heating with altitude results in more stratiform cloud, independent of whether over land or ocean, or how organized the thunderstorms are). Improving knowledge of stratiform clouds is important, since they contribute to substantial accumulated rainfall (important to agriculture and local hydrology) as well as radiation, given their large areal extents and lifetime. The radiation impacts likely impact simulated cloud feedback in all Earth System models (ESMs).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region

This data package contains observation-based and model-simulated datasets (all provided in NetCDF format) for evaluating how wintertime lake-ice representation affects regional weather and climate over the Laurentian Great Lakes (freshwater lake ecosystem) during the high–ice-cover winter of 2009. The observational component includes: (1) Stage IV gridded precipitation at 4 km, hourly resolution for January–February 2009 over the Great Lakes region (radar–gauge multisensor precipitation analyses); (2) Great Lakes Surface Environmental Analysis (GLSEA) satellite-derived lake-ice coverage at 1.3 km, daily resolution for the 2009 winter months, providing ice coverage over Lakes Superior, Michigan, Huron, Erie, and Ontario; and (3) in situ measurements at the Standard Rock site on Lake Superior from the Great Lakes Evaporation Network (GLEN) at hourly resolution, including near-surface atmospheric variables and sensible and latent heat fluxes (air–lake exchange) at a fixed point location. The modeling component provides corresponding fields from two simulations, both archived at 4 km, hourly resolution: a standalone Weather Research Forecasting model (WRF) run driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), and a two-way coupled model using WRF and the Finite Volume Community Ocean Model (WRF-FVCOM, a 3-D hydrodynamic lake model). These outputs include variables relevant to air–lake interaction and lake-effect processes (e.g., near-surface temperature, humidity, wind, precipitation, and surface turbulent fluxes), enabling direct comparison with the observational datasets. Users can analyze and visualize these NetCDF files with common tools such as Python (e.g., xarray, netCDF4, numpy, pandas), NCO/CDO, Panoply, or ncview; NetCDF variables can also be converted to other formats (e.g., CSV, GeoTIFF) using these utilities.

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗

DOE OSTI2023

Coupling SCALE with DAKOTA for Axial Burnup Profiles Assessment in Burnup Credit

This paper presents a computational study that demonstrates the application of the SCALE code system in conjunction with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) for the analysis of key factors influencing the evaluation of burnup credit (BUC) in pressurized water reactors (PWRs). The primary objective of this analysis is to characterize the model by utilizing parameterization, uncertainty quantification, and optimization studies. Using this approach, we can comprehensively assess the system and conduct informed predictive studies. This study highlights the effectiveness of the SCALE code system integrated within the DAKOTA framework in terms of efficiency and capability. With the coupling of the burnup code ORIGAMI with the CSAS or TSUNAMI-3D sequence embedded in a DAKOTA analysis, we can characterize the factors that influence the k eff of PWR 17x17 spent nuclear fuel (SNF) in the GBC-32 computational benchmark cask for the assessment of BUC in criticality safety analysis. The coupling methodology used in this study is not exclusive to BUC analysis. However, the choice to apply this methodology to the BUC problem is particularly significant because of the diverse range of aspects it encompasses in nuclear criticality safety analyses. This problem presents a unique opportunity to explore and address multiple facets of such analyses related to BUC and illustrates the capability of the SCALE code system with DAKOTA. This analysis makes use of historical reference data for the axial burnup profile, where the entire space within the bounds is considered. Both SCALE and DAKOTA are currently integrated in the Nuclear Energy Advanced Modeling Simulation (NEAMS) Workbench code system, which has a user-friendly graphical interface that simplifies the setup of simulations and configuration of input parameters as well as the visualization of simulation results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗