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DOE OSTI2019

High-Resolution Regional Wave Hindcast for Hawaii

This report summarizes modeling efforts for hindcasting of wave climate within the exclusive economic zone (EEZ) around the state of Hawaii. Specifically, the report provides detailed description of model mesh development, sensitivity analysis of the configuration of model parameters, data used for model forcing and validation, model skill assessment, and model outputs for resource characterization. The unstructured, nested-grid modeling approach incorporates a regional high-resolution SWAN (Simulating WAves Nearshore) model within the global-regional WAVEWATCH III® (WW3) wave model. The models were configured to comply with the International Electrotechnical Commission (IEC) Technical Standards for wave energy resource characterization. The SWAN model domain covers the entire EEZ with a spatial resolution of approximately 300 m in the nearshore region. Two levels of WW3 nested grids, from 0.5 arc-degree at global to 7.5 arc-minute at regional scale, were used to provide boundary conditions that drive the high-resolution nearshore SWAN model. The WW3 and SWAN models were forced by 0.5 arc-degree Climate Forecast System Reanalysis wind and 5 km regional Weather Research and Forecasting wind, respectively. Wave resource and bulk wave parameters were simulated for a 32-year period from 1979 to 2010, and were subsequently validated using wave buoy data within the model domain. Model performance was evaluated using a set of error statistics. Overall, the model shows good skills in reproducing the wave climate in the Hawaii EEZ in both spatial and temporal domains. Comparisons of time series and scatter plots for the six IEC resource parameters and the associated error statistics tables, and horizontal two-dimensional plots of annually averaged and monthly averaged IEC parameters are provided.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Segmentation Model Distillation [Poster]

The process of training object detection (OD) or image segmentation model requires both a substantial amount of data and technical knowledge, which often creates challenges in applying these types of models to their full potential. In order to streamline the process of developing these models, we propose a new pipeline where a foundation model assists in the dataset generation. Then this resulting dataset is used to fine-tune a fast light-weight model to perform the custom segmentation or OD. This resulting model is also fit for real-time image segmentation, such as in a video stream.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

New Models and Methods for the Electroweak Scale

This is the Final Technical Report to the US Department of Energy for grant DE-SC0013529, New Models and Methods for the Electroweak Scale, covering the time period April 1, 2015 to March 31, 2017. The goal of this project was to maximize the understanding of fundamental weak scale physics in light of current experiments, mainly the ongoing run of the Large Hadron Collider and the space based satellite experiements searching for signals Dark Matter annihilation or decay. This research program focused on the phenomenology of supersymmetry, Higgs physics, and Dark Matter. The properties of the Higgs boson are currently being measured by the Large Hadron collider, and could be a sensitive window into new physics at the weak scale. Supersymmetry is the leading theoretical candidate to explain the natural nessof the electroweak theory, however new model space must be explored as the Large Hadron collider has disfavored much minimal model parameter space. In addition the nature of Dark Matter, the mysterious particle that makes up 25% of the mass of the universe is still unknown. This project sought to address measurements of the Higgs boson couplings to the Standard Model particles, new LHC discovery scenarios for supersymmetric particles, and new measurements of Dark Matter interactions with the Standard Model both in collider production and annihilation in space. Accomplishments include new creating tools for analyses of Dark Matter models in Dark Matter which annihilates into multiple Standard Model particles, including new visualizations of bounds for models with various Dark Matter branching ratios; benchmark studies for new discovery scenarios of Dark Matter at the Large Hardon Collider for Higgs-Dark Matter and gauge boson-Dark Matter interactions; New target analyses to detect direct decays of the Higgs boson into challenging final states like pairs of light jets, and new phenomenological analysis of non-minimal supersymmetric models, namely the set of Dirac Gaugino Models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

A Benchmarking Framework for Evaluating Large Language Model Capabilities in Nuclear Reactor Safety Applications

Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2019

A New Approach to Multivariate Network Traffic Analysis

© 2019, Springer Science+Business Media, LLC & Science Press, China. Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers.

Kim, Jinoh↗

DOE OSTI2026

SAF: a promising approach to meet growing jet fuel demand

SAF provides a promising approach to aid the rising jet fuel demand from increased travel around the world and reduce the lifecycle emissions from the aviation sector. Although the feasibility of SAF pathways has been demonstrated through economic and environmental metrics quantification, the models used to quantify these variables have a high degree of variability in terms of accuracy and thereby reliability. To understand how to adopt and commercialize SAF, we need to harmonize these process models and assess metrics and technical limitations related to their production technologies. We find the production cost of SAF using hydro processed fatty acids and esters (HEFA), Fischer-Tropsch (FT), and alcohol-to-jet (ATJ) to be $\$$3-$\$$6/gallon gasoline equivalent (gge) and life cycle emissions to be lower than Jet A, except for ATJ using corn grain (≤25%). HEFA utilizing oil feedstocks has the lowest production cost (~$\$$2.9/gge) and highest jet yield (>150 gge/dry ton), while FT has the largest emission reduction (94%) compared to fossil jet. A unique contribution of this study is a comparative analysis of metrics related to SAF processes across technical, economic, and sustainability aspects. A cross-comparison of these metrics shows HEFA using fats, oils, and grease have the most favorable ratings, while HEFA using algae and ATJ using corn stover have more neutral and unfavorable ratings, respectively. These ratings can be improved by implementing the right combination of practical and technological advancements.

09 BIOMASS FUELS↗

DOE OSTI2022

Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density

Improvements in wind energy technology, reduced costs, and ambitious clean energy goals have led to projections of high wind contribution in coming years. Developing methodologies to design wind plants with a variety of siting constraints and turbine sizes helps enable high wind penetration, and gain a better understanding of how wind plants are sensitive to setback constraints and turbine design. In this paper, we present a two-step optimization method to simultaneously determine the optimal number of turbines and their locations in a wind plant domain divided into many small, discrete parcels. We present the optimized performance metrics of a wind plant optimized with different turbine sizes and ratings, and with different siting restrictions within the wind plant. Our results indicate that taller and larger turbines are more sensitive to increasing siting constraints. We also compare the optimal wind plant layouts and performance for wind plants optimized for minimum COE and maximum profit. Wind plants optimized for profit had 130%-190% of the capacity of plants optimized for COE, which demonstrates that the optimal results are greatly affected by the objective function, which should be carefully considered. Finally, in this paper we demonstrate the effect of increasing siting constraints on wind plant capacity density, and how the results change when different land areas are used to calculate capacity density. When using the entire wind plant boundary area to determine capacity density, increasing siting constraints decreases the capacity density. However, when we only use the available area (the area left after removing the siting constraints) to calculate the capacity density, increasing the siting constraints increases capacity density. This is a critical insight because of how capacity density is typically defined and used in research, and has important implications for assessment of technical potential and capacity expansion modeling, as well as future wind deployment potential.

17 WIND ENERGY↗

DOE OSTI2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Computational and Systems Biology Advances to Enable Bioagent Agnostic Signatures

Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen interactions directed by a human immunological model. Although well-developed technical assays exist for many aspects of human-pathogen interaction, the analytic methods and pipelines to combine and holistically interpret the results of such assays are immature and require further investments to exploit new technologies. In this manuscript, we discuss potential immunologically based bioagent-agnostic approaches and the computational tool gaps the community should prioritize filling.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Conceptual Design of Engineering-Scale Molten Salt Spill Tests to Support Molten Salt Reactor Licensing

Nuclear accident progression analysis codes for reactor licensing must be validated using experimental data that was generated at a large enough scale so that the results are representative of full-scale accidents. This report describes a conceptual design of engineering-scale molten salt spill tests that will provide the necessary datasets to support molten salt reactor (MSR) licensing. Design considerations, salt compositions, test variables, processes to be quantified, and suitable measurement techniques are presented and discussed. Aspects of the conceptual engineering-scale molten salt spill test design presented in this report were made using insights from recent laboratory-scale molten salt spill tests conducted at Argonne. Additional input from model developers, MSR vendors, and technical experts will benefit the design of the proposed engineering-scale molten salt spill tests.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Towards Development of a Conceptual Model for Mercury in Bear Creek, Oak Ridge, Tennessee (FY23 Update)

Mercury concentrations in fish in Bear Creek are elevated and comparable to concentrations seen in fish in East Fork Poplar Creek (EFPC) on the Oak Ridge Reservation, even though aqueous inorganic mercury concentrations are orders of magnitude lower in Bear Creek than in EFPC. Acknowledging that the relationship between aqueous and fish tissue mercury concentrations is not linear, and that methylmercury (MeHg) concentrations are likely more related to fish tissue concentrations than aqueous total mercury (Hg T ) concentrations, MeHg production is not easily predicted or controlled. In Bear Creek, where aqueous Hg T concentrations are low, factors other than mercury loading drive the transformation of mercury to MeHg and subsequent trophic transfer to fish. Initial development of a waste disposal facility (Environmental Management Disposal Facility; EMDF) in Bear Creek Valley has begun, and operation of the EMDF has the potential to increase mercury inputs to Bear Creek. Consequently, understanding the factors contributing to elevated MeHg concentrations in water and fish in Bear Creek has increased relevance and importance. This report summarizes data from recent and historical compliance and investigatory studies with an eye toward building a conceptual model to understand the processes affecting mercury transport and transformation in the Bear Creek watershed, as well as to highlight key knowledge gaps in our understanding of these processes that warrant further investigation. The conceptual model will provide a strong technical basis for prioritizing and optimizing potential mitigation actions or best management practices to minimize potential negative effects of the EMDF related to mercury in Bear Creek.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Lawrence, Massachusetts, Residential Building Efficiency and Electrification Analysis [Slides]

As part of the Communities Local Energy Action Program (CLEAP), the Lawrence Stakeholders Coalition (LSC) is interested in assessing and understanding the potential for and pathways to electrification for the City of Lawrence. The LSC's main questions are: What is the impact of various electrification packages on residential electricity bills and what types of buildings should the LSC target for electrification plus weatherization packages? This technical assistance, using ResStock tool modeling, aims to assist the Coalition's electrification and energy burden reduction planning by: 1. Providing cross-cutting data on housing stock characteristics, energy burden characteristics, fuel types, energy consumption, and system efficiency; and 2. Providing information on upgrade package costs, emissions reduction, and energy reductions by prioritized housing segment. The ResStock analysis presented here focuses on opportunities to reduce energy burden, energy consumption, and energy bills for single family homes, multifamily buildings, and mobile homes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Biomimicry in Clean Energy Futures: Workshop Report

On July 25, 2023, PNNL hosted a workshop to explore the potential for biomimicry – looking to nature and biology for solutions to human engineering challenges – to support clean energy futures and associated research. Participants represented a range of technical expertise ranging from ecological modeling to cyber-security. Workshop supporting material featured a literature review of biomimetic approaches in all renewable energy technologies and inspired from all kingdoms of life. Participants noted clear contrasts between natural design strategies and clean energy deployment and the electric grid. They also agreed that biomimicry offered a rich area of research potential but lacked definitive benefits or results in commercialization phases.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Reassessment of Boiling Water Reactor Rod Cusping Capability of VERA-MPACT

This technical report assesses the current modeling capabilities of the Virtual Environment for Reactor Applications (VERA) for minimizing control rod cusping in boiling water reactors. Assessment is performed on a 10 × 10 General Electric (GE)-14 assembly as well as a simplified version of the same assembly. Runs were performed using VERA for three available cusping treatments in the Michigan PArallel Characteristics Transport (MPACT) code: no treatment, subplane treatment, and fitted polynomial correction treatment. Subplane treatment is discussed, but specific results are not provided because of failures in the method of implementation for boiling water reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Reassessment of Boiling Water Reactor Rod Cusping Capability of VERA-MPACT

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Computational Modeling of Atmospheric Processes at Texas Southern University

Texas Southern University (TSU) is strengthening its research program in atmospheric chemistry and physics with a climate science emphasis by leveraging partnerships with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility, Brookhaven National Laboratory (BNL), and the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This RDPP-supported program focuses on secondary organic aerosols (SOAs) and reactive atmospheric species that influence cloud formation, precipitation processes, and radiative forcing. SOAs play a critical role in cloud microphysics and Earth’s energy balance, yet the chemical and physical mechanisms governing SOA–cloud interactions remain a significant source of uncertainty in predictive climate models. Through computational modeling, observational data analysis, and national laboratory collaboration, this program develops a skilled cohort of students trained in atmospheric science, environmental data analysis, and climate-relevant modeling. These research experiences build technical competencies that are transferable to careers in government laboratories, academia, and industry. By engaging students from historically underrepresented communities in high-impact climate research, TSU expands participation in the atmospheric sciences workforce while contributing meaningful scientific insights to DOE-supported ARM research activities. This partnership strengthens national capacity in climate science and supports the development of the next generation of atmospheric researchers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

FY24 Task 4: Studies of Phosphate and Fluoride Solubility for Dissolution of High Phosphate Tank Waste

Knowledge gaps have been identified in phosphate solubility in almost every single- and multi-component system, and particularly for aluminate in phosphate/hydroxide, where uncertainties in predicted values of aluminum solubility are greater than 50%. Solubility data of multicomponent, aqueous electrolytes containing sodium hydroxide, sodium fluoride, sodium phosphate, sodium nitrite, sodium nitrate, and dissolved gibbsite are also sparse. For example, for solutions of (i) sodium nitrate and sodium phosphate, data are only available at 30 and 50 °C, and for (ii) dissolved gibbsite in sodium hydroxide and sodium phosphate, only two data points are available at 20 and 40 °C. There is no data available on mixtures of sodium hydroxide, sodium phosphate, sodium fluoride, and dissolved gibbsite. These knowledge gaps were identified in a technical review of waste solubility data and the impact of dilution on solution stabilities and will be addressed in this work to predict aluminum hydroxide and sodium phosphate solubility in multicomponent electrolytes upon dilution, and upon variation of temperature. Results will provide the technical basis to develop accurate models for (i) gibbsite solubility and mass transfer of aluminum between solid and liquid forms following the sluicing of sludge and saltcake with water; and (ii) further blending of these suspensions with bismuth from bismuth phosphate waste, and zirconium and uranium left from the fuel decladding. This work will be essential to developing a disposition path for retrieval solutions from bismuth phosphate wastes. This effort would also support sludge washing to further reduce phosphate concentration if that process were to be added back to the flowsheet in the future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Demonstration of Grid Services Using Mixed Grid-Forming and Grid-Following Technologies at the Wheatridge Renewable Energy Facility (Final Technical Report)

This Final Technical Report summarizes the analytical, modeling, and engineering work performed to evaluate grid-forming (GFM) inverter capabilities within a large hybrid renewable energy facility. The project focused on assessing the ability of advanced inverter-based resources to provide essential reliability services through coordinated operation of grid-forming (GFM) and grid-following (GFL) technologies. During Budget Period 1, the project developed a comprehensive framework of GFM performance metrics, including angle support, voltage regulation, frequency response, damping behavior, and current-limiting performance. Extensive electromagnetic transient (EMT) studies and hardware-in-the-loop (HIL) testing were conducted to validate GFM behavior under a range of operating conditions, including weak-grid scenarios, voltage disturbances, and multi-resource interactions. The project also established validated modeling approaches, plant-level control integration strategies, commissioning frameworks, and high-speed measurement infrastructure to support future field demonstration. Although the project concluded prior to field demonstration, the results provide a utility-scale foundation for evaluating, modeling, and deploying grid-forming technologies. The methodologies and tools developed contribute to industry understanding of inverter-based resource behavior and support future power system reliability under increasing renewable penetration.

14 SOLAR ENERGY↗