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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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DOE OSTI2016

Dynamic analysis environment for nuclear forensic analyses

We report a Dynamic Analysis Environment (DAE) software package is introduced to facilitate group inclusion/exclusion method testing, evaluation and comparison for pre-detonation nuclear forensics applications. Employing DAE, the multivariate signatures of a questioned material can be compared to the signatures for different, known groups, enabling the linking of the questioned material to its potential process, location, or fabrication facility. Advantages of using DAE for group inclusion/exclusion include built-in query tools for retrieving data of interest from a database, the recording and documentation of all analysis steps, a clear visualization of the analysis steps intelligible to a non-expert, and the ability to integrate analysis tools developed in different programming languages. Two group inclusion/exclusion methods are implemented in DAE: principal component analysis, a parametric feature extraction method, and k nearest neighbors, a nonparametric pattern recognition method. Spent Fuel Isotopic Composition (SFCOMPO), an open source international database of isotopic compositions for spent nuclear fuels (SNF) from 14 reactors, is used to construct PCA and KNN models for known reactor groups, and 20 simulated SNF samples are utilized in evaluating the performance of these group inclusion/exclusion models. For all 20 simulated samples, PCA in conjunction with the Q statistic correctly excludes a large percentage of reactor groups and correctly includes the true reactor of origination. As a result, employing KNN, 14 of the 20 simulated samples are classified to their true reactor of origination.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Guide to Using Onionskin Analysis Code (U)

This document is a guide to using R-code written for the purpose of analyzing onionskin experiments. We expect the user to be very familiar with statistical methods and the R programming language. For more details about onionskin experiments and the statistical methods mentioned in this document see Storlie, Fugate, et al. (2013). Engineers at LANL experiment with detonators and high explosives to assess performance. The experimental unit, called an onionskin, is a hemisphere consisting of a detonator and a booster pellet surrounded by explosive material. When the detonator explodes, a streak camera mounted above the pole of the hemisphere records when the shock wave arrives at the surface. The output from the camera is a two-dimensional image that is transformed into a curve that shows the arrival time as a function of polar angle. The statistical challenge is to characterize a baseline population of arrival time curves and to compare the baseline curves to curves from a new, so-called, test series. The hope is that the new test series of curves is statistically similar to the baseline population.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Transient Efficiency Flexibility and Reliability Optimization of Coal-Fired Power Plants: Model-Predictive Control Library Development (Report)

This document pertains to the reporting requirements of DOE contract FE-0031767. The document covers the development of a model predictive control (MPC) library for implementing MPC for a general dynamic system. The library is implemented in a standardized manner in Matlab/Simulink, where core functions on model prediction, linearization and formulation and solution of a quadratic programming (QP) optimization problem is done in the core library - independent of the specific application. The user can provide the application-specific dynamic model in continuous and discrete time, to rapidly implement and test the MPC performance in a desktop simulation. The MPC optimization objective and constraints are also easily configured via an Excel file to allow iterative refinement as needed. Finally, the MPC library enables a rapid deployment to a target environment through auto C-code generation and containerization. The MPC library works seamlessly with the model based estimation (MBE) library to obtain the overall output feedback control solution. In this program, the reduced order model (ROM) of a coal-fired power plant (CFPP) is used to implement and test the MPC solution.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

European Contributions to Fermilab Accelerator Upgrades and Facilities for the DUNE Experiment

The Proton Improvement Plan (PIP-II) to the FNAL accelerator chain and the Long-Baseline Neutrino Facility (LBNF) will provide the world's most intense neutrino beam to the Deep Underground Neutrino Experiment (DUNE) enabling a wide-ranging physics program. This document outlines the significant contributions made by European national laboratories and institutes towards realizing the first phase of the project with a 1.2 MW neutrino beam. Construction of this first phase is well underway. For DUNE Phase II, this will be closely followed by an upgrade of the beam power to > 2 MW, for which the European groups again have a key role and which will require the continued support of the European community for machine aspects of neutrino physics. Beyond the neutrino beam aspects, LBNF is also responsible for providing unique infrastructure to install and operate the DUNE neutrino detectors at FNAL and at the Sanford Underground Research Facility (SURF). The cryostats for the first two Liquid Argon Time Projection Chamber detector modules at SURF, a contribution of CERN to LBNF, are central to the success of the ongoing execution of DUNE Phase I. Likewise, successful and timely procurement of cryostats for two additional detector modules at SURF will be critical to the success of DUNE Phase II and the overall physics program. The DUNE Collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This paper is being submitted to the 'Accelerator technologies' and 'Projects and Large Experiments' streams. Additional inputs related to the DUNE science program, DUNE detector technologies and R&D, and DUNE software and computing, are also being submitted to other streams.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks

This document serves as user manual for HydraGNN, a scalable graph neural network (GNN) architecture that allows for a simultaneous prediction of multiple target properties using multi-task learning (MTL). The HydraGNN architecture is constructed by successive superposition of three different sets of layers. The first set is made of message-passing layers to exchange information across nodes in the graph and use this to update the nodal features. The second set is made of global pooling layers that aggregate information from all the nodes in the graph and map it into a scalar, and is needed only for global target properties that are related to the entire graph. The third set of layers is dedicated to the implementation of MTL, which is enabled by forking of the architecture into separate heads, each one of them dedicated to the predictive task of one specific target property. Through an object-oriented programming paradigm, HydraGNN is templated over different message-passing policies, which allows for a user-friendly hyperparameter study to assess the sensitivity of the predictive performance of the HydraGNN architecture on a specific dataset with respect to the choice of the message-passing policy. The object-oriented paradigm used by HydraGNN also allows for a user-friendly inclusion of newly developed message passing policies within the existing framework. HydraGNN supports distributed computing capabilities for scalable data reading and scalable training on leadership-class supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Application of Performance Analysis Tools on SNL ASC Codes

This milestone 1) exercised a broad set of performance profiling and analysis tools, including tools whose development has been promoted by the ASC program; 2) exercised the tools on two different SNL ASC codes, one Sierra code (Sierra/Aria, a C++ codebase) and one RAMSES code (ITS, a Fortran codebase); and 3) exercised the tools on multiple platforms, including the CTS-1 (e.g., Serrano) and ATS-1 Trinity (e.g., Mutrino) platforms. The milestone generated a plethora of strong and weak scaling, trend and profile data for multiple versions and problem cases for each of the two codes. A wealth of experience was gained with the various tools that included identification of problems, an improved understanding of feature sets, enhanced usage documentation, and insights for future tool-development. Results are provided from a large number and variety of performance analysis runs with the target codes, together with instructions for how to make use of the tools with the codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Critical Experiment Design Phase 2 Report for Integral Request 304

This report documents the second phase of the critical experiment design process (CED-2) conducted as part of integral experiment request (IER) 304. The purpose of IER-304 is to develop a temperature-dependent critical experiment capability at the Sandia National Laboratories critical experiment facility using low-enriched uranium oxide fuel. Only a few benchmark quality critical experiments are currently available to validate criticality safety computational methods at temperatures other than room temperature. Recent advancements in computational techniques such as implementation of on-the-fly Doppler broadening techniques and advanced treatment of thermal scattering data necessitate the development of additional experimental capabilities in this area. The work described in this report developed representative configurations using 7uPCX and BUCCX fuels, analyzed the physics exercised by the different arrays, and analyzed the estimated experimental uncertainties. This report also scopes the information necessary for performing modifications to the reactor to accommodate operation at elevated and reduced temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Status of New Models Hosted on the Virtual Test Bed (VTB) in 2024

The National Reactor Innovation Center (NRIC) mission is to support deployment of novel reactor concepts. This is achieved by providing physical and virtual spaces for building and testing various components, systems, and complete pilot plants. The Virtual Test Bed (VTB) represents the virtual counterpart to the physical test bed. It is in development in collaboration with the Department of Energy’s (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The mission of the VTB is to accelerate the deployment and licensing of advanced reactors by leveraging state-of-the-art modeling and simulation (M&S) tools developed by the DOE NEAMS program. This is accomplished by three primary means: (1) openly hosting simulations that showcase analysis capabilities, (2) continuously testing the models hosted against code updates to avoid deprecation, and (3) filling key M&S gaps that are relevant for the physical NRIC test beds. The VTB repository consists of two sub-entities: 1. A documentation website detailing the models (https://mooseframework.inl.gov/virtual_test_bed). 2. A GitHub repository that hosts the corresponding files (https://github.com/idaholab/virtual_test_bed). Previous documentation on the models hosted in the VTB can be found in [1,2,3,4]. These references also include additional background information on the various NEAMS codes showcased in the VTB (which is omitted here for brevity). This paper primarily provides a status update of the most recent additions to the repository.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Google/Makani Energy Kite Modeling (CRADA CRD-18-00569 Final Report)

In support of Makani’s energy kite development program, Makani engaged with NREL to develop, verify, and document a multiphysics engineering model of a megawatt-scale tethered energy kite (named KiteFAST). After the original development of KiteFAST was completed, Makani further engaged NREL to develop a software enhancement of KiteFAST to enable the dynamics modeling of an energy kite tethered to a floating offshore platform (named KiteFAST-OS). Before this project, the capability to model the aero-hydro-servo-elastic dynamics of an airborne wind energy (AWE) system with electricity generation on the flying device (fly gen) and crosswind flight operation did not exist. Such physics-based modeling capability is needed for loads analysis, structural design, and certification of energy kites. Throughout the project, an exclusive license kept KiteFAST and KiteFAST-OS available only to Makani, NREL, and its counterparts. With the ending of Makani, the KiteFAST and KiteFAST-OS software were merged into a single code base (named KiteFAST), the exclusive license was terminated, and the source code, documentation, and example models were released publicly.

17 WIND ENERGY↗
DOE OSTI2016

Silicon Damage Response Function Derivation and Verification: Assessment of Impact on ASTM Standard E722

Unsuccessful attempts by members of the radiation effects community to independently derive the Norgett-Robinson-Torrens (NRT) damage energy factors for silicon in ASTM standard E722-14 led to an investigation of the software coding and data that produced those damage energy factors. The ad hoc collaboration to discover the reason for lack of agreement revealed a coding error and resulted in a report documenting the methodology to produce the response function for the standard. The recommended changes in the NRT damage energy factors for silicon are shown to have significant impact for a narrow energy region of the 1-MeV(Si) equivalent fluence response function. However, when evaluating integral metrics over all neutrons energies in various spectra important to the SNL electronics testing community, the change in the response results in a small decrease in the total 1- MeV(Si) equivalent fluence of ~0.6% compared to the E722-14 response. Response functions based on the newly recommended NRT damage energy factors have been produced and are available for users of both the NuGET and MCNP codes.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Software Quality Assurance and Verification for the MPACT Library Generation Process

This report fulfills the requirements for the Consortium for the Advanced Simulation of Light-Water Reactors (CASL) milestone L2:RTM.P14.02, “SQA and Verification for MPACT Library Generation,” by documenting the current status of the software quality, verification, and acceptance testing of nuclear data libraries for MPACT. It provides a brief overview of the library generation process, from general-purpose evaluated nuclear data files (ENDF/B) to a problem-dependent cross section library for modeling of light-water reactors (LWRs). The software quality assurance (SQA) programs associated with each of the software used to generate the nuclear data libraries are discussed; specific tests within the SCALE/AMPX and VERA/XSTools repositories are described. The methods and associated tests to verify the quality of the library during the generation process are described in detail. The library generation process has been automated to a degree to (1) ensure that it can be run without user intervention and (2) to ensure that the library can be reproduced. Finally, the acceptance testing process that will be performed by representatives from the Radiation Transport Methods (RTM) Focus Area prior to the production library’s release is described in detail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Commercial Building Prototypes Based on ANSI/ASHRAE/IES Standard 90.1-2019 Appendix G PRM: Technical Support Document

The two paths for documenting compliance with ANSI/ASHRAE/IES Standard 90.1 are the prescriptive path and the performance path. Beyond code programs and rating systems (for example, USGBC-LEED ) are primarily known to use a third path – the Appendix G Performance Rating Method. An update in the 2016 edition of Standard 90.1 approved the Appendix G Performance Rating System for code compliance, extending its application and allowing for greater consistency of modeling rules for code and beyond code building energy modeling. The Appendix G PRM provides rules for the development of whole building energy models of baseline and proposed models for calculating the “performance cost index target” value using the simulated energy results of the baseline and proposed models and the building performance factors published in Table 4.2.1.1 of the Standard. This report documents (1) the methodology used for development of the baseline and proposed energy models of the Pacific Northwest National Laboratory and U.S. Department of Energy commercial building prototypes using the Appendix G Performance Rating Method; and (2) the building performance factors that were calculated using those models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Gaussian Process Optimization of Sensitivity-Based Similarity Metrics between New Nuclear Applications and New/Existing Benchmarks

Confidently designing safe, new nuclear criticality experiments requires expert judgement, which could take years of experience. Sensitivity/uncertainty (S/U) analysis can be utilized by less experienced individuals to conservatively estimate uncertainties in important parameters, such as k eff , in newly proposed nuclear applications. This type of analysis relies on matching new nuclear applications with existing benchmark experiments. The Whisper-1.1 software package included in MCNP6.2 ®1 contains more than 1,100 International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks. These benchmarks however rarely match new nuclear applications. The number of benchmarks available to match a given set of materials or geometric configurations varies significantly. Furthermore, recently performed benchmark experiments may not have had enough time to be properly documented and published. Benchmarks are vital for determining the accuracy of nuclear data and can assist nuclear physics and evaluators in improving nuclear data libraries. Exhaustively exploring the parameter space using simulations with software such as MCNP is too computationally expensive. In this work, Gaussian process optimization was implemented to reduce the number of simulations needed for optimization over multiple parameters. This optimization scheme was designed to selectively generate new benchmarks with high sensitivity-based similarity metrics to user-defined nuclear applications. Two benchmark models of spherically nested shells containing plutonium, uranium, tantalum, and water were used in the optimization to match an application containing plutonium plates, stacked in a tantalum reflector, surrounded by water.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

6th Annual Midwest Conference for Undergraduate Women in Physics, January 18-20, 2013, Urbana, Illinois

This document is the program for the 6th Annual Midwest Conference for Undergraduate Women in Physics, which was held at the University of Illinois at Urbana-Champaign on January 18-20, 2013. The goals of the conference were to foster a culture in which undergraduate women are encouraged and supported to pursue, and also to succeed in, higher education in physics; to provide career information to students in physics and related fields; to give women the resources, motivation, and confidence to apply to graduate school and successfully complete a Ph.D. program in Physics; to provide information and dispel misconceptions about the application process for graduate school and the diverse employment opportunities in physics and related fields, enabling women to make more informed decisions about their goals and attain them; and to connect female physics students with successful female physicists to whom they can relate and who can act as inspirational role models and mentors.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Development of a utility computer-based training program on setpoint methodology for nuclear power plants - 129

The purpose of this paper is to provide an overview of the development of the Computer- Based Training (CBT) Program for utility engineers on Setpoint Methodology for nuclear power plants. This computer based training module was prepared by the Advanced Engineering Training initiative. The Advanced Engineering Training initiative is a nuclear industry effort to develop technical computer-based training modules for targeted areas of engineering expertise. The target audience is engineers at commercial nuclear power facilities in the U. S. These modules are being developed using the systematic approach to training and are being designed to maintain industry engineering expertise and to facilitate engineering knowledge transfer. The objective of this training effort is to support the development of the next generation of site technical experts for specific topic areas. The training is one part of three key components that would facilitate knowledge transfer to the future site Subject Matter Expert (SME). The following are the major focus areas covered in the Setpoint Methodology CBT: Chapter 1 provides an overview of the role instrument setpoints perform in the defense-in-depth safety analysis for the plant as well introducing key definitions related to setpoints. Chapter 2 provides the history and current regulatory requirements related to setpoint control including the laws and guidance provided by the U.S. NRC. Chapter 3 provides describes the setpoint methodology including terms, formulae, methods of combination, graded approach and methods for drift analysis. This is a major part of the training module in covering the incremental uncertainty terms that need to be considered and how they are combined. Chapter 4 provides and overview of the role of setpoints in Technical Specifications and associated documents. Chapter 5 describes how the regulatory process, related to setpoint control, has been implemented in NRC inspections and how the major elements of the 10 CFR 50.59 reviews apply to the performance of a new or revised setpoint calculation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Additive Manufacturing of Fuel Injectors

Additive manufacturing (AM), also known as 3D-printing, has been shifting from a novelty prototyping paradigm to a legitimate manufacturing tool capable of creating components for highly complex engineered products. An emerging AM technology for producing metal parts is the laser powder bed fusion (L-PBF) process; however, industry manufacturing specifications and component design practices for L-PBF have not yet been established. Solar Turbines Incorporated (Solar), an industrial gas turbine manufacturer, has been evaluating AM technology for development and production applications with the desire to enable accelerated product development cycle times, overall turbine efficiency improvements, and supply chain flexibility relative to conventional manufacturing processes (casting, brazing, welding). Accordingly, Solar teamed with EWI on a joint two-and-a-half-year project with the goal of developing a production L-PBF AM process capable of consistently producing high-nickel alloy material suitable for high temperature gas turbine engine fuel injector components. The project plan tasks were designed to understand the interaction of the process variables and their combined impact on the resultant AM material quality. The composition of the high-nickel alloy powders selected for this program met the conventional cast Hastelloy X compositional limits and were commercially available in different particle size distributions (PSD) from two suppliers. Solar produced all the test articles and both EWI and Solar shared responsibility for analyzing them. The effects of powder metal input stock, laser parameters, heat treatments, and post-finishing methods were evaluated. This process knowledge was then used to generate tensile, fatigue, and creep material properties data curves suitable for component design activities. The key process controls for ensuring consistent material properties were documented in AM powder and process specifications. The basic components of the project were: • Powder metal input stock: Powder characterization, dimensional accuracy, metallurgical characterization, and mechanical properties evaluation. • Process parameters: Laser parameter effects, post-printing heat-treatment development, mechanical properties evaluation, and post-finishing technique. • Material design curves: Room and elevated temperature tensiles, low cycle fatigue, and creep rupture properties curves generated. • AM specifications: Key metal powder characteristics, laser parameters, and heat-treatment controls identified.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Artificial Intelligence/Machine Learning Technology in Power System Applications

The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗