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DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Polymer Additive Manufacturing for Marine Renewable Energy Applications: Best Practices, Research Trends, and Current Challenges

Additive manufacturing (AM) is a rapidly growing technology space, not only for prototyping, but is also becoming more feasible at larger scales and increasing component quantities. There are a large variety of AM processes and materials available to users and effectively applying those processes and materials to a specific use case can be challenging. One specific area where AM could be particularly beneficial is marine renewable energy (MRE). Not only is MRE a relatively nascent industry with a near-term need for rapid deployments and prototype testing, but developers could also see long-term benefits from the broad variety of environmentally resistant materials available and the ability to manufacture complex geometries that AM technologies offer. Over the past 4 years, AM materials have played an increasing role in the Advanced Materials project; a multi-year, multi-laboratory research project funded by the U.S. Department of Energy's Water Power Technologies Office, with the main goal of reducing barriers to the adoption of complex materials in the MRE industry. The primary focus of this project is to develop test methods and generate datasets to understand the long-term performance of advanced materials in marine environmental and address specific material challenges as they arise. This report provides an extensive overview of the research that has been performed specific to AM polymers as part of the Advanced Materials project. The intention of this document is to provide recommendations of best practices with regards to material selection, mechanical test method development, and design practices, lessons learned along the way, current research trends, and ongoing challenges with regards to AM polymers in marine environments. In particular, this report focuses on several key aspects: Material and process selection, Environmental conditioning and subsequent degradation quantification through mechanical characterization, Composite reinforcements on AM polymer substrates, Adhesion of instrumentation for mechanical characterization and loads measurements, Protective coatings for preventing biofouling and water ingress, Other MRE case studies where AM has proved particularly useful. Ultimately, we hope that the test methods that have been developed, data generated, and lessons learned from this research will be valuable to the MRE community (researchers and developers alike), as well as other industries, and can be used as a reference point as the respective MRE and AM industries continue to grow and mature.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

System-Level Integration of Modular Language Models for Real-Time Risk Assessment in Third-Party Risk Management Systems

Large enterprises typically rely on dedicated teams to govern and implement security measures throughout their supply chains, ensuring compliance with enterprise security procedures. There is a significant reliance on Third-Party Risk Management (TPRM) platforms, which often require complete, highly structured information from potential vendors. The review and compliance assurance processes are time- and labor intensive, often requiring several rounds of review between the supply chain security risk management teams, business users, and potential vendors, leading to delays in the supply chain processing and consumer experience. Significant challenges in the risk management paradigm include handling unstructured data in various formats and providing real-time feedback to users to reduce the required review time. This paper presents a novel solution to these challenges. A modular multi-step system architecture is proposed using advances in language processing, specifically for unstructured responses and provides real-time feedback (i.e., 3 seconds) so that users can improve their responses before the TPSRM team review. This novel system architecture will increase information accuracy and significantly reduce time and labor during the review process.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Discovery Science and Inertial Fusion Energy Research at the Jupiter Laser Facility (Research Performance Progress Report)

The goal of this project is to provide operational support to run the Jupiter Laser Facility within the LaserNetUS network to achieve the following four objectives: • Develop new science, techniques, and platforms for discovery science and IFE research, in partnership with academia, other LaserNetUS nodes, and the greater community. • Advance the development of secondary sources of photons and particles for applications relevant to discovery science and IFE; • Serve as a testbed for new laser, optical, target, and diagnostic capabilities that will advance IFE; • Attract, train, and retain talent in high-energy-density and laser science that will be essential in furthering development of IFE, and help build new collaborations between national laboratory and academic researchers. We have accomplished our four objectives by: • Providing access to the three JLF platforms (Titan, Janus, and COMET) to LaserNetUS users after their proposals have been independently reviewed and ranked by the LaserNetUS PRP; • Providing expertise and technical capabilities to support focused science research thrusts at the facility; • Promoting and maintaining technical relationships and collaborations with other members of the LaserNetUS community to nurture and grow the discovery science and IFE workforce— especially students and early career scientists—on LaserNetUS facilities. • Augmenting JLF with specific improvements while coordinating efforts with LLNL organizations and collaborators to provide users with optimal laser, target, and diagnostic resources to help them maximize technical impact.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

User Manual for the Thermal Hydraulic Code SE2RCT

SE2-ANL is a modified version of SUPERENERGY-2 which interfaces the power distribution in the ANL maintained physics codes for the user. SE2RCT is a special version of SE2-ANL developed for specific use on models of the Experimental Breeder Reactor-II (EBR-II). SE2RCT fulfills a specific role in the calculated results as part of the Fuels Irradiation and Physics Database (FIPD) and is built to connect with the Physics and Analysis DataBase (PADB) for metallic fuel research and development in advanced sodium-cooled fast reactors (SFRs) created for EBR-II. SE2RCT is primarily a thermal analysis code that interfaces with the RCT based pin power details instead of the built-in pin power dehomogenization strategy in SE2-ANL. Beyond the thermal hydraulic calculation, the SE2RCT code provides detailed axial distributions of the RCT calculated results. SE2RCT also calculates the the axial distribution of pin wise DPA for the cladding materials which is part of the FIPD calculated results. This report serves as the user manual for SE2RCT and thus eliminates the QA gap of missing documentation on the SE2RCT software. All of the features and limitations of SE2RCT are documented in this report. The input and output specifications of SE2RCT are discussed in detail, and some unique features of SE2RCT, e.g. accurate pin power and temperature results for problems with non-uniform composition within an assembly, are also demonstrated and tested.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Using FIPD and OPTD to Benchmark Metallic Fuel Performance

This report serves as an introduction, tutorial, and benchmark specification for out-of-pile tests on metallic fuel. It introduces a new user to the EBR-II legacy fuel performance test program and the fast reactor fuel performance databases built to preserve the records. It then details the information stored in each database and how to find it. A benchmark specification is included for a small set of out-of-pile tests on U-10Zr fuel to function as a tutorial demonstrating how the legacy fuel performance data sets stored in the FIPD and OPTD databases can be used together to benchmark fuel performance models for steady-state and transient performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Pxl

The code consists of several scripts. The image is fetched from a predefined URL, downloaded, and is displayed in the virtual environment at a predefined location. The image is also dynamically scaled to fit in the virtual environment depending on its size. The image is sent to have each pixel's value written to a CSV file. While the CSV is being written, a dictionary is populated with values that map each pixel to a temperature reading. The image's height, width, position and references to its corners are sent to make a button that is the same size as the image and placed right behind the image in the virtual scene. The button defines behavior that should be triggered when the user pushes the image. Unity provides visual cues that alert the user when they are about to touch something in the virtual environment. The code holds references to the cues for the left and right hands. The button queries the cue tracker and receives the position of the active cue in the form of a 3D vector. The 3D vector is compared to the bottom left corner of the image to transform the world space coordinates of the cue to the pixel space of the image. The specific pixel is approximated relative to how far left and up the user touched from the bottom left corner of the image. The approximated pixel value, which is represented as a coordinate in 2D space, is used to retrieve the temperature value associated with the approximated pixel. A window of text is then updated to display the temperature reading.

. Otis, KrystianeS [Idaho National Laboratory (INL↗

DOE OSTI2026

Update on Model Specification Development of Ternary Pumped Storage Technologies [Slides]

T-PSH is an innovative structure of C-PSH that features two turbines, one for pumping and one for generation, replacing the single turbine in C-PSH. These turbines are housed in separate chambers, with an additional set of penstocks connecting the pump turbine to the hydraulic system. This structure enables T-PSH to operate in an HSC mode, allowing for simultaneous pumping and generation, which provides greater operational flexibility compared to C-PSH. To model T-PSH's HSC mode, a user defined turbine and governor system model is required, including the shared penstock model and additional pump turbine model. This specification will focus on the details of the user-defined turbine and governor model, which leverages the HYGOV model.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Flexible Integration of Diverse HVAC Technologies in EnergyPlus via Python-Enabled Workflows

Analysis of advanced controls and novel system types is often not directly feasible in building energy simulation tools. Various techniques extend building energy simulation tool capabilities to allow the use of user-defined scripts and programs, but these approaches have limitations. The EnergyPlus Python plugin offers users new flexibility to use EnergyPlus to call an external Python module at specific points in the simulation, as well as to use Python to call EnergyPlus functionality through an application programming interface (API). This paper presents four case studies leveraging the EnergyPlus Python plugin to facilitate analysis of advanced controls and system types. The use of the Python plugin offers greater modularity and flexibility relative to previous approaches, is less error prone, and is simpler for users to adopt. The Python plugin allows EnergyPlus to be used in a more flexible manner and to accommodate the expanding realm of energy modeling applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗

DOE OSTI2025

Self-consistent equilibrium and transport simulations for NSTX-U plasmas enhanced via machine learning surrogate models

The Control-Oriented Transport SIMulator (COTSIM) is an advanced equilibrium and transport code designed for simulating tokamak discharges at computational speeds suitable for control applications. COTSIM’s modular framework enables users to select models that balance accuracy with speed according to specific needs, allowing the code to operate from fast to faster-than-real-time performance levels. This work presents recent enhancements to COTSIM’s predictive accuracy for NSTX-U scenarios, achieved by integrating neural-network-based surrogate models and self-consistent equilibrium calculations. To improve source deposition predictions, a surrogate model for NUBEAM has been incorporated. Additionally, a surrogate model for the Multi-Mode Module (MMM) now supports predictions of anomalous thermal, momentum, and particle diffusivities—key factors for modeling the evolution of temperature and rotation. Each surrogate model was specifically trained for the NSTX-U operational regime to enhance COTSIM’s accuracy while maintaining computational efficiency. Moreover, COTSIM now couples fixed-boundary equilibrium solvers with its transport solvers, enabling self-consistent predictions of plasma profiles and equilibrium evolution over the discharge. Simulation results demonstrate strong agreement between COTSIM and TRANSP predictions for NSTX-U discharges. These substantial advancements expand COTSIM’s utility in model-based control applications for NSTX-U. Potential applications include simultaneous optimization of equilibrium and transport scenarios, integration into digital twins, real-time profile estimation (e.g., temperature and rotation) from limited or noisy measurements, and advanced feedback-based scenario control.

Equilibrium and transport modeling↗

DOE OSTI2025

Advanced Method Optimization with Categorical and Constrained Continuous Parameters

Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space. Previous work defined and implemented a generalized technique for method optimization for continuous method parameters, but a knowledge gap remains for the incorporation of categorical variables into these advanced method optimization schemes. This work presents and validates a generalized optimization approach that incorporates both continuous and categorical variables while also utilizing a multivariate, multiobjective optimization scheme with Karush–Kuhn–Tucker conditions to bound the optimization space to solutions within the physical limitations of the parameter space. Method optimization from a case study using GC–MS for the analysis of 11 analytical standards with objectives to minimize peak width and maximize peak height resulted in a 3 orders of magnitude improvement in the average peak height and a 2 orders of magnitude improvement in the average peak width compared to the least optimal (but reasonable) instrumental parameters utilized in this study. This approach to optimization allows for a customizable method optimization in which users can include both continuous and categorical variables to achieve objectives specific to their analytical goals. This approach significantly reduces the labor and cost associated with traditional method development approaches and can be applied in a variety of scientific fields across a range of laboratory techniques (e.g., instrument method development, sample preparation, and extraction techniques).

Amorphous materials↗

DOE OSTI2025

Bayesian And Human Reliability Analysis (hra)-aided Method For The Reliability Analysis Of Software (bahamas)

The purpose of the BAHAMAS code is to provide a simplified process for performing quantitative evaluations of software reliability. The Bayesian and Human Reliability Analysis (HRA)-Aided method for the Reliability Analysis of software (BAHAMAS) was developed specifically to perform quantification under limited data conditions, i.e., when limited testing or operational data are available, such as during early development stages. BAHAMAS essentially examines the quality of a software development life cycle to determine the probability of specific types of software failure. BAHAMAS will have modules to support user input for detailed and simplified analyses. The user interface will also support software common cause failure analysis.

Wang, Congjian (0000000207789927)↗

DOE OSTI2024

SCALE Input and Result Files Supporting SCALE Inventory and Reactivity Analysis as Part of the Hermes 2021 PSAR Review

This dataset contains input and result files of computational simulations with the SCALE code system. The simulations cover radionuclide inventory and reactivity analyses of a fluoride salt-cooled high temperature pebble-bed reactor (PB-FHR), specifically the Hermes low-power PB-FHR demonstration reactor. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases

equilibrium core↗

DOE OSTI2024

Documentation for Python Automation in CYME for DER Hosting Capacity Analysis of Different Feeder Configurations

This file provides the documentation for several Python scripts that were developed by Sandia National Laboratories to enhance the automation and customization capabilities for performing various distribution system planning and analysis tasks in CYME. Specifically, these scripts (.py files detailed in Figure 1) enable the user to evaluate different distribution system configurations and the resulting impacts on hosting capacity results and other metrics. In general, these scripts—and the accompanying documentation—provide the foundation upon which future customized tools can be created. For example, the scripts show how to extract and modify parameters of various circuit components, set up and run analyses using built-in CYME tools (iteratively), and export reports for further evaluations and comparisons. Thus, the capabilities and syntaxes used in the scripts can be adapted and leveraged for countless other objectives.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Sierra/SD – User's Manual – 5.22

Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high-fidelity, validated models used in modal, vibration, static and shock analysis of weapons systems. This document provides a user’s guide to the input for Sierra/SD. Details of input specifications for the different solution types, output options, element types and parameters are included. The appendices contain detailed examples, and instructions for running the software on parallel platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Sierra/SD – User's Manual (V.5.24)

42 ENGINEERING↗