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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine learning model to project the impact of COVID-19 on U.S. motor gasoline demand

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people's resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.

COVID-19 pandemic↗
DOE OSTI2025

Networked Microgrid Topology Reconfiguration to Promote Fairness in Proactive Load Shedding

Increasing occurrences of natural disasters and grid emergency events consistently challenge the safe and reliable operations of power systems. During such emergency situations, system operators may proactively shed load to mitigate risks. However, uncoordinated implementation of load shedding may disrupt electricity supply and even lead to cascading failures. Meanwhile, it is crucial to address potential biases affecting different customers when executing load shedding. This paper addresses the dynamic topology reconfiguration problem for networked microgrids with distributed energy resources under emergency conditions. Specifically, we propose a novel rolling-horizon optimization model that integrates fairness-aware constraints into the networked microgrid topology reconfiguration. Unlike existing approaches that focus solely on efficiency or apply fairness considerations in static settings, our method explicitly incorporates temporal fairness constraints to restrict repeated or excessive load curtailment for load blocks. Moreover, the fairness-aware constraints are specifically developed for the context of dynamic networked microgrid topology reconfiguration, and are designed to be convex or amenable to linear reformulations, which offers a more tractable alternative to traditional models with non-convex formulations. Numerical studies on a modified IEEE 13-bus system and a larger-sized SMART-DS networked microgrid system demonstrate the performance of the proposed algorithm towards more fairness-aware networked microgrid topology reconfiguration decision-making.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Uncertainty Quantification of Capacitor Switching Transient Location using Machine Learning

Identification of capacitor switching transient location provides valuable insight into the state of the associated equipment. Machine learning (ML) models, and convolutional neural networks (CNNs) in particular, have demonstrated remarkable performance in signal location. However, ML models are data driven whose predictions are affected by noise in data and may also suffer from large extrapolation errors when applied to new conditions. Uncertainty quantification (UQ) is necessary to ensure model trustworthiness and avoid overconfident predictions in extrapolation. Here, in this work, we propose a novel UQ method, called PI3NN, to quantify prediction uncertainty of ML models and integrate the method with CNNs for transient source location. PI3NN calculates Prediction Intervals by training 3 Neural Networks and uses root-finding methods to determine the interval precisely. Additionally, PI3NN can identify out-of-distribution (OOD) data in a nonstationary condition to avoid overconfident prediction. Results indicate that with PI3NN, transient signals are not only correctly identified, but when said signals are subject to corruptions characteristic of an actual power monitoring system (e.g. non-ideal sensors), the model recognizes when it is uncertain about its predictions, effectively letting the user know when to accept or discard the results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Canadian Oil Sands Extraction and Upgrading: A Synthesis of the Data on Energy Consumption, CO 2 Emissions, and Supply Costs

As Canadian crude bitumen production from oil sands has increased in recent decades, the nation’s oil and gas industry has become a significant contributor to national greenhouse gas emissions. Canada has developed carbon emission reduction targets to meet its Nationally Determined Contributions and Mid-Century Strategy goals. A detailed profile of energy consumption pathways in the oil sands industry is necessary to identify potential areas of improvement and to monitor progress toward meeting emissions reduction targets. Much of the existing literature for oil sands modeling provides input assumptions with different technological boundaries. For a set of oil sands extraction and upgrading technologies, this study first reviews the literature and then quantifies energy input requirements, CO 2 emissions, and operating costs for a set of consistent technological boundaries and energy units. Summary results refer to requirements and costs at the production facility, excluding transportation and blending costs. An energy system diagram of oil sands production that matches these boundaries is provided, which can be used by integrated assessment models, oil sands companies, and government ministries to evaluate the present and future energy consumption and emissions pathways of the oil sands industry.

02 PETROLEUM↗
DOE OSTI2022

A guideline to document occupant behavior models for advanced building controls

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. Here, the literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.

Building control↗
DOE OSTI2023

Internal consistency and diversity scenario development: A comparative framework to evaluate energy model scenarios

Energy modeling frameworks and scenario analysis help us explore the potential impact of our actions and uncertainties in future energy systems. Despite their importance, there is no systematic procedure for evaluating the scenario development process. In a literature review, we identify two core elements of the scenario development process: internal consistency and diversity which are oftentimes missing from scenarios. Here, to address this gap, we create the Internal consistency and Diversity Scenario Development (IDSD) comparative framework which aims to assess the feasibility and diversity of scenarios for a given energy model. With this framework, we review commonly used energy models and demonstrate our framework on their scenarios. The IDSD comparative framework can serve several purposes absent from previous scenario development work by aiding energy modelers and report writers in crafting high-quality scenarios. First, the IDSD is a reflective tool which can improve the quality of the scenario development process, enabling a comparative assessment of energy models and scenarios. Second, the IDSD can provide guidance to modeling frameworks with existing scenarios and those still in development; this feedback will enable modelers to improve the development and the communication of the limitations of their scenarios. Third, this study has highlighted areas for improvement in the scenario development of some commonly used energy model frameworks. Finally, there is a complete lack of explanation regarding the stakeholder selection process. Addressing these identified items could increase opportunities for advanced energy technology uptake and improve our options for achieving a more resilient energy system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Comparing optical four-flux model results with experimental data obtained by integrating sphere measurements

Four-flux theory is a way to model scattering through multiple layers of a system based on diffuse and collimated properties. When compared with measurement results obtained using an integrating sphere or a goniophotometer, an approximation is often made as the physical instrument cannot separate the collimated component from the diffuse light scattered in the forward direction. This paper tries to clarify the meaning of the word diffuse for the different cases and outlines simple corrections to improve the accuracy when comparing four-flux models and measured data based on sample haze and the geometry of the integrating sphere.

Bilokur, Maryna (ORCID:0000000191839650)↗
DOE OSTI2022

Security Enhancements for Distributed Energy Resource Systems Interconnected with Distribution Networks: Final Technical Report

The revised IEEE 1547 Standard defines new complex communication-adjustable voltage and frequency regulation and ride-through characteristics, while maintaining the general antiislanding requirement for unintentional islanding situations. Unintentional islanding is prohibited while intentional islanding is specifically allowed, thus effectively enabling microgrid operation mode. Further, IEEE 1547-2018 Standard introduces new requirements in terms of interoperability so that the DER plant/circuit segments may be seamlessly integrated with the utility networks but at the same time become vulnerable to a cyber-attack. Traditional cybersecurity measures including encryption, authentication and role-based access control may not be fully implementable to all communication protocols specified in the IEEE 1547 Standard. Therefore, in this project we have identified, researched, implemented and tested several cyberphysical approaches that rely mostly on the behavior of the DER circuit and may help with validating the incoming command and control action potentially coming through an insecure communications channel. Additionally, we have built semantic models and communications profiles for DER facilities and have implemented lightweight IEC 61850 based publisher-subscriber GOOSE messaging mechanism, with security extensions in terms of authentication and encryption. The project proposed information models for integration into UCA OpenFMB 2.0 profiles focusing on grid code compliance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study

Over 3 million people still die every year from diseases caused by exposure to outdoor PM 2.5 air pollution, and more than a quarter of these premature deaths occur in China. In addition to clean air policies that target pollution emissions, climate policies aimed at reducing fossil-fuel CO 2 emissions (e.g., to avoid 1.5°C of warming) may also dramatically improve air quality and public health. Yet there has been no comprehensive accounting of public health outcomes under different energy pathways and local clean air management decisions in China. In particular, further research is needed to understand the relationships among climate and clean air polices and future health burdens in China, where an aging population will further exacerbate the impacts of air pollution. Using a China-focused integrated assessment model (GCAM-China) and a dynamic emission projection model (DPEC), we project future Chinese air quality in scenarios spanning a range of global climate targets (i.e. 1.5°0C, 2°C, NDC, unambitious, baseline, and 4.5°C) as well as national clean air actions (i.e. 2015-pollution, current-pollution, and ambitious-pollution). We then evaluate the health impacts of PM2·5 air pollution of scenario matrix using the chemical transport model WRF-CMAQ and the latest epidemiological concentration–response (C-R) functions (i.e. GBD2019). We find that, without ambitious climate mitigation (e.g., under current NDC pledges), Chinese deaths related the PM2.5 air pollution do not substantially decrease—and often grow—by mid-century, regardless of clean air policies and air quality improvements. For example, in scenarios that track China’s current NDC pledge and deploy best-available pollution control technologies, PM 2.5 -related deaths in China decrease slightly by 2030 (to 1.2 million per year) but no further by mid-century (Ambitious-pollution-NDC-goals; 1.2 million deaths in 2050) despite substantial and continuous improvements in population-weighted air quality (27.2 to 16.0 µg/m 3 from 2030 to 2050). The contrary trends of improving air quality and increasing PM 2·5 -related deaths in many of our scenarios reveals the extent to which extra efforts are needed to compensate for the established fact of increasing age of China’s population in future. Substantial decreases in China’s PM 2.5 -related deaths and age-standardized death rates (e.g., decreasing by 0.3–0.5 million deaths and 10.2–14.2 per 100,000 population age-standardized death rates per year) thus require the sort of large-scale transition in energy sources entailed by scenarios that meet international climate goals to avoid 1.5°C and 2°C of warming (Ambitious-pollution-2°C- and 1.5°C-goals).

58 GEOSCIENCES↗
DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Preliminary Plan to Inform Testing of a Heat Exchanger Test Article

This report presents a preliminary plan to guide the qualification testing of advanced heat exchanger (HX) components for nuclear-to-industrial heat transfer applications. The objective is to establish a defensible, physics-based methodology that integrates computational modeling, targeted experimentation, and in-service inspection considerations to demonstrate component performance and reliability under representative reactor conditions. The analysis identifies Sodium-cooled Fast Reactor (SFR) and High-Temperature Gas-cooled Reactor (HTGR) systems as reference configurations in terms of temperature, pressure, and chemical environment. Within these operating envelopes, dominant degradation mechanisms— including creep–fatigue interaction, flow-induced vibration, corrosion, and diffusion-bond deterioration—were evaluated to define test requirements. A comprehensive computationalexperimental framework is proposed to support life prediction and qualification activities. The framework couples high-fidelity structural-mechanics, thermal-hydraulic, and fluid-structure interaction models with accelerated degradation testing to produce a traceable linkage between microstructural evolution, mechanical performance, and remaining useful life (RUL). The approach adheres to established Verification, Validation, and Uncertainty Quantification (VVUQ) standards (ASME V&V 10/20; NUREG-2152) and incorporates a digital-twin architecture for continuous model refinement through data assimilation. The plan further outlines testing methodologies, including pre-test analyses, test-loop design parameters, and sensor placement strategies that maximize information yield while maintaining mechanistic fidelity. Complementary sections describe in-service inspection (ISI), on-line monitoring (OLM), and structural-health-monitoring (SHM) techniques applicable to compact HX geometries typical of advanced reactors. Collectively, these activities establish the technical foundation for demonstrating 40-60-year equivalent service life of advanced heat exchangers in support of the U.S. Department of Energy’s Advanced Reactor and Integrated Energy Systems programs. The forthcoming phase will execute the defined pre-test analyses, initiate hardware fabrication, and implement the integrated testing campaign to validate the proposed qualification methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Are scenario projections overly optimistic about future yield progress?

Historical increases in agricultural production were achieved predominantly by large increases in agricultural productivity. Intensification of crop and livestock production also plays a key role in future projections of agricultural land use. Here, we assess and discuss projections of crop yields by global agricultural land-use and integrated assessment models. To evaluate these crop yield projections, we compare them to empirical data on attainable yields by employing a linear and plateauing continuation of observed attainable yield trends. While keeping in mind the uncertainties of attainable yields projections and future climate change impacts, we find that, on average for all cereals on the global level, global projected yields by 2050 remain below the attainable yields. Additionally, this is also true for future pathways with high technological progress and mitigation efforts, indicating that projected yield increases are not overly optimistic, even under systemic transformations. Furthermore, on a regional scale, we find that for developing regions, specifically for sub-Saharan Africa, projected yields stay well below attainable yields, indicating that the large yield gaps which could be closed through improved crop management, may also persist in the future. In OECD countries, in contrast, current yields are already close to attainable yields, and the projections approach or, for some models, even exceed attainable yields by 2050. This observation parallels research suggesting that future progress in attainable yields in developed regions will mainly have to be achieved through new crop varieties or genetic improvements. The models included in this study vary widely in their implementation of yield progress, which are often split into endogenous (crop management) improvements and exogenous (technological) trends. More detail and transparency are needed in these important elements of global yields and land use projections, and this paper discusses possibilities of better aligning agronomic understanding of yield gaps and yield potentials with modelling approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Theory and Implementation of the Spectacular Nonlinear Viscoelastic Constitutive Model

This report is a comprehensive guide to the nonlinear viscoelastic Spectacular model, which is an isotropic, thermo-rheologically simple constitutive model for glass-forming materials, such as amorphous polymers. Spectacular is intermediate in complexity to the previous PEC and SPEC models (Potential Energy Clock and Simplified Potential Energy Clock models, respectively). The model form consists of two parts: a Helmholtz free energy functional and a nonlinear material clock that controls the rate of viscoelastic relaxation. The Helmholtz free energy is derived from a series expansion about a reference state. Expressions for the stress and entropy functionals are derived from the Helmholtz free energy following the Rational Mechanics approach. The material clock depends on a simplified expression for the potential energy, which itself is a functional of the temperature and strain histories. This report describes the thermo-mechanical theory of Spectacular, the numerical methods for time-integrating the model, model verification for its implementation in LAMÉ, a user guide for its implementation in LAMÉ, and ideas for future work. A number of appendices provide supplementary mathematical details and a description of the procedure used to derive the simplified potential energy from the full expression for the potential energy. The goal of this report is create a convenient point-of-entry for engineers who wish to learn more about Spectacular, but also to serve as a reference manual for advanced users of the model.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $ \$ $6B annually (at $ \$ $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year), and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE) missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

42 ENGINEERING↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $6B annually (at $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year),and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE)missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Response of Atmospheric River Width and Intensity to Aquaplanet Warming: A Detection Algorithm‐ and Background Moisture‐Independent Approach

The width of an atmospheric river (AR) is an important parameter when evaluating its impact. Although previous research suggests ARs will widen with global warming, a precise response has been muddled by the large sensitivity of width to a diverse set of AR detection techniques (ARDTs). Here, we propose a methodology that removes the influence of the ARDT by modeling AR‐integrated vapor transport (IVT) profiles as idealized exponential curves with free parameters given by background IVT, intensity above background IVT, and profile width. Notably, our definition for AR profile width does not include any explicit numerical thresholds, relative or absolute, for IVT. We apply our approach to a series of idealized aquaplanet experiments, first with a baseline sea surface temperature (SST) distribution, and then with +2K, +4K, and +6K uniform warming, so as to determine the contributions of each free parameter to AR width. We also apply our approach to high‐resolution atmosphere‐only models forced with SSTs modified to preserve historical variability but following projected warming over 2016–2050. Our results show that contributions to impacts‐relevant AR widening comes primarily from enhancements in background IVT and AR intensity, as opposed to from dynamic width changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impact of recent ENDF nuclear data update, high initial enrichment and high burnup fuel on critical experiments applicability determination via the integral index c k for burnup credit validation

In 2012, NUREG/CR-7109 reported on the validation of burnup credit calculations involving major and minor actinides and major fission products which was investigated for pressurized and boiling water reactor (PWR and BWR) fuel enrichments up to 5 wt% 235 U and assembly-average burnups up to 60 GWd/MTU. Recently, there has been interest in increasing the maximum enrichment used in PWR fuel as high as 8 wt% 235 U and correspondingly increasing the maximum assembly-average burnups to approximately 75 GWd/MTU. These proposed increases in enrichment and burnup necessitate reinvestigation of the validation basis for k eff calculations for this expanded application space. Additionally, the 2012 study was performed by using the Evaluated Nuclear Data File (ENDF)/B-VII.0 nuclear data with the SCALE 6 covariance library, and the effects of using the newly released ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data and covariance libraries should be evaluated. In this work, published in NUREG/CR-7309 in 2025, the validation assessment was performed consistently with NUREG/CR-7109: modeling irradiated fuel assemblies in the Generic Burnup Credit (GBC)-32 cask defined in NUREG/CR-6747. The TSUNAMI-3D sequence was used to generate sensitivity data for the application model, and the data were compared with sensitivity data from select benchmark models. The integral parameter c k is the metric of similarity used in this study and is consistent with NUREG/CR-7109, where a c k value in excess of 0.8 indicates sufficient similarity for use in validation. A new set of benchmark experiments with sensitivity data has been assembled for this effort. The number of experiments with available sensitivity data is now 2,104, compared to 474 in NUREG/CR-7109. This increase was facilitated by the efforts of the Nuclear Energy Agency to generate sensitivity data for a majority of the experiments in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook to supplement the data available in the Oak Ridge National Laboratory (ORNL) Verified, Archived Library of Inputs and Data (VALID). The complete set of benchmarks considered here includes experiments for low-enriched uranium (LEU), intermediate enriched uranium (IEU), and a mixture of uranium and plutonium (MIX) from the ICSBEP Handbook and VALID, as well as ORNL models of the Haut Taux de Combustion (HTC) experiments and other potentially relevant models not included in VALID. The updated similarity study shows that none of the extended burnup and higher enrichment combinations considered show a significant decrease in the number of potentially applicable experiments, meaning sufficient critical experiments exist for the validation of BUC criticality safety calculations, with initial enrichments up to 8 wt% 235 U and burnups up to 80 GWd/MTU. Additionally, both the ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data libraries can be used for validation since the number of critical experiments applicable for validation increases for most cases with the most recent nuclear data compared to the previous one. As in previous BUC validation studies, the French HTC experiments are the most similar in a majority of the application cases studied, especially from representative discharge burnups ranging from 40 to 80 GWd/MTU. In conclusion, these results match the conclusions presented in NUREG/CR-7109 regarding validation of the primary actinides in BUC analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Evaluating Exposures of Bay Mussels (Mytilus trossulus) to Contaminants of Emerging Concern through Environmental Sampling and Hydrodynamic Modeling.

Bay mussels (Mytilus trossulus) were transplanted to 18 locations representing a range of potential exposures throughout Puget Sound, WA. Tissues were analyzed for over 200 organic contaminants. Results indicated the widespread exposure of marine organisms to trace levels of organic contaminants including the synthetic opioid oxycodone, present at three urban sites, and the chemotherapy drug melphalan, present at nine locations, at levels that may be of biological concern. Land-use and wastewater outfalls were evaluated as potential sources of CECs to the nearshore. Exposure to alkylphenol ethoxylates was associated with increased impervious surfaces in upland watersheds. A hydrodynamic simulation was performed using the Salish Sea Model to integrate inputs from 99 wastewater sources to Puget Sound. Predictions were consistent with concentrations of several wastewater-associated contaminants and d15N enrichment. These results support the notion that Puget Sound nearshore biota suffer chronic exposures to a suite of contaminants from multiple sources and provide critical to focus future monitoring and management.

toxic contamination, Salish Sea, Sish Sea Model, m↗