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DOE OSTI2013

EMTA THERMAL CONDUCTIVITY PREDICTIONS FOR UNIRRADIATED AND IRRADIATED SIC/SIC COMPOSITES

The objective of this work is to achieve a predictive engineering tool to assess and tailor the thermophysical properties of unirradiated and irradiated SiC/SiC composites. Towards this objective, first, PNNL’s EMTA (Eshelby-Mori-Tanaka Approach) software was successfully applied to predict the thermal conductivity of unirradiated 2D SiC/SiC composites [1]. Next, we have extended the EMTA model reported in [1] to predict the thermal conductivity of these composites subjected to neutron irradiation at elevated temperatures and irradiation doses leading to defect saturation [2]. As EMTA thermal conductivity predictions compared well with the experimental results [1-2], in the future, a unified EMTA for SiC/SiC composites will be developed that addresses both thermal and mechanical properties.

Nguyen, Ba Nghiep↗

DOE OSTI2020

Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. In this study, we analyze both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.

42 ENGINEERING↗

DOE OSTI2015

Methodology for assessing the safety of Hydrogen Systems: HyRAM 1.0 technical reference manual

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a standardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. Department of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.0. HyRAM 1.0 includes generic probabilities for hydrogen equipment failures, probabilistic models for the impact of heat flux on humans and structures, and computationally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.0 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.

08 HYDROGEN↗

DOE OSTI2018

Defining and Characterizing Methods, Tools, and Computing Resources to Support Pseudo Exhaustive Testability of Software Based I&C Devices

Under the Department of Energy’s Light Water Reactor Sustainability Program, within the Plant Modernization research pathway, the Digital I&C Qualification Project is identifying new methods that would be beneficial in qualifying digital I&C systems and devices for safety-related usage. One such method that would be useful in qualifying field components such as sensors and actuators is the concept of testability. The Nuclear Regulatory Commission (NRC) considers testability to be one of two design attributes sufficient to eliminate consideration of software-based or software logic-based common cause failure (the other being diversity). The NRC defines acceptable “testability” as follows: Testability – A system is sufficiently simple such that every possible combination of inputs and every possible sequence of device states are tested and all outputs are verified for every case (100% tested). [NUREG 0800, Chapter 7, Branch Technical Position (BTP) 7-19] This qualification method has never proven to be practical in view of the very large number of combinations of inputs and sequences of device states for a typical I&C device. However, many of these combinations are not unique in the sense that they represent the same state space or in that that they represent state space that would not affect the critical design basis functions of the device. Therefore, the state space of interest might possibly be reduced to a manageable dimension through such analysis. This project focuses on a representative I&C device similar in design, function, and complexity to the types of devices that would likely be deployed in nuclear power plants as digital or software based sensors and actuators (e.g. Smart Sensors). Analysis will be conducted to determine the feasibility of testing this device in a manner consistent with the NRC definition. This report describes acceptable test methods, needed tools (existing or new), and computing resources, all based on engineering and computer science principles. This information will then be used in a later phase of this project to develop a test specification for I&C device testability that can be used in a future phase of this project to demonstrate digital qualification with respect to common cause failure. Following that, testing will be conducted and the results analyzed to determine if the methods employed are sufficient to eliminate consideration of software common cause failure in accordance with the concept of testability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Methodology for assessing the safety of Hydrogen Systems: HyRAM 1.1 technical reference manual

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a stan- dardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. De- partment of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.1. HyRAM 1.1 includes generic probabilities for hydrogen equipment fail- ures, probabilistic models for the impact of heat flux on humans and structures, and computa- tionally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.1 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Enabling Data Exchange and Data Integration with the Common Information Model: An Introduction for Power Systems Engineers and Application Developers

The Common Information Model (CIM) is an open-source information model that is used to model an electrical network and the various equipment used on the network. CIM is widely used for data exchange of bulk transmission power systems and is finding increasing use for distribution systems. Use of a non-proprietary information model (such as CIM) that has been agreed upon and adopted by numerous utilities, vendors, and researchers allows significant reduction in the effort and cost of data integration. Likewise, adoption of open data platforms built around the CIM increases available functionalities for managing and optimizing the smart grid of the future. This report is intended as an introduction to CIM for utility engineers, power systems researchers, and application developers, providing a broad view of the CIM and how particular profiles can be adapted for various use cases. Unlike most other CIM introduction documents and the International Electrotechnical Commission (IEC) standards (which are mostly targeted to an audience of data scientists, enterprise database managers, and platform developers), this report is intended for users of traditional power systems analysis software and other readers without any prior experience with canonical information models, data profiles, or UML modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

SU-F-R-12: Prediction of TrueBeam Hardware Issues Using Trajectory Log Analysis

Purpose: To predict potential failures of hardware within the Varian TrueBeam linear accelerator in order to proactively replace parts and decrease machine downtime. Methods: Machine downtime is a problem for all radiation oncology departments and vendors. Most often it is the result of unexpected equipment failure, and increased due to lack of in-house clinical engineering support. Preventative maintenance attempts to assuage downtime, but often is ineffective at preemptively preventing many failure modes such as MLC motor failures, the need to tighten a gantry chain, or the replacement of a jaw motor, among other things. To attempt to alleviate downtime, software was developed in house that determines the maximum value of each axis enumerated in the Truebeam trajectory log files. After patient treatments, this data is stored in a SQL database. Microsoft Power BI is used to plot the average maximum error of each day of each machine as a function of time. The results are then correlated with actual faults that occurred at the machine with the help of Varian service engineers. Results: Over the course of six months, 76,312 trajectory logs have been written into the database and plotted in Power BI. Throughout the course of analysis MLC motors have been replaced on three machines due to the early warning of the trajectory log analysis. The service engineers have also been alerted to possible gantry issues on one occasion due to the aforementioned analysis. Conclusion: Analyzing the trajectory log data is a viable and effective early warning system for potential failures of the TrueBeam linear accelerator. With further analysis and tightening of the tolerance values used to determine a possible imminent failure, it should be possible to pinpoint future issues more thoroughly and for more axes of motion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Software Verification and Validation Guidelines for Non-Linear Soil-Structure Interaction Analysis

Seismic analysis of structures, systems, and components (SSCs), including the consideration of soil-structure interaction (SSI) effects, is an important and required step in the design and licensing of nuclear power plant SSCs important to safety. Historically, the SSI analysis of nuclear structures has been performed using equivalent linear methods. However, there has been considerable industry investment in alternative seismic design and analysis approaches to reduce the construction cost of new reactors. Toward that goal and in alignment with the Licensing Modernization Project (LMP) framework, the Nuclear Regulatory Commission (US NRC) has proposed a risk-informed, performance-based approach to seismic design that allows inelastic response in those nuclear plant structures not required for confinement. As a complementary effort, reactor designers are exploring nonlinear seismic analysis methods to optimize structural designs and reduce construction costs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Enhancing Monte Carlo Workflows for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo codes are essential components of many reactor physics simulation workflows as high-fidelity continuous-energy neutron transport solvers. Among Monte Carlo radiation transport codes, MCNP is particularly notable due to its diverse simulation capabilities, large user base, and long validation history. Despite being a powerful simulation tool, MCNP provides limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions that limit its compatibility with modern workflows. Here, to better integrate MCNP into a modern scientific workflow, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named MCNPy, which provides a specialized set of classes for MCNP input development. Moreover, to guarantee that our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable), we have adopted a strategy of model-driven software development in which a generalized model of the MCNP input format has been created. From this generalized model, or “metamodel,” problem-specific implementations such as an engine for input validation or a codebase for programmatic operations may be automatically generated. Since MCNPy primarily acts as a Python front-end to the underlying Java API that directly interfaces with the metamodel, it is intrinsically linked to the metamodel and thus remains maintainable. With MCNPy, users can programmatically read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like design optimization and model translation for nuclear systems. As examples, this work demonstrates the use of MCNPy to find the critical radius of a plutonium sphere and to translate a 9000+ line MCNP input file into a corresponding OpenMC model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

AERCalc v1.2.10

AERCalc is a software tool for evaluating the energy performance of window attachments (e.g., shading systems, attached window panels, etc.). All the annual energy modeling is performed by the EnergyPlus OS engine. The program incorporates a user-friendly GUI.

Mitchell, Robin↗

DOE OSTI2022

ROMNet: Neural-Network-Based Surrogates for Reduced-Order Model Dynamics

SAND2023-05829O ROMNet is a toolbox built on the Tensorflow library for training reduced-order, neural operator surrogate models. The software is used for training reduced order neural operator surrogate models for dynamical systems applications. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2024

A set of test problems for nonlinear optimization

The software is a set of test problems for nonlinear optimization algorithms, including subroutines such as linear algebra routines and automatic differentiation algorithms. The test problems come from chemical engineering open literature, and describe optimization tasks related to the design and operation of processes such as carbon capture, Hydrogen production, heat exchange, and distillation.

Parker, Robert↗

DOE OSTI2024

Constitutive Models via Automatic Differentiation v.1.0.0

SAND2024-00905O Constitutive Models via Automatic Differentiation (CMAD) provides a software framework for solving constitutive or material model calibration problems. It relies on JAX's automatic differentiation capabilities to compute the derivatives needed for both forward and adjoint sensitivity analyses. The software can be used to implement constitutive models and calibrate the parameters from experimental data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Seidl, Daniel↗

DOE OSTI2024

spot_bt v.0.1.1

SAND2024-08430O spot_bt allows users to easily construct complex, autonomous behaviors without having to overly rely on Boston Dynamic’s Spot robot software development kit. Using a form-of-state machine called a "behavior tree," the software develops and applies different "leaves" or actions to the robot. Attaching multiple actions in sequence creates a "branch," and a series of these make up the "tree." The software works on any x86 or aarch64 hardware but must run on a Linux OS. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

Kakish, Zahi↗

DOE OSTI2025

Binder-benchmarking

SAND2025-07593O Binder-benchmarking evaluates the speed and memory impacts of C++, Python, and Matlab code binders. As a repository, it provides a way to locally run computation-based and memory-based benchmark suites on pybind11 and nanobind-based code in a Docker image. The software runs simple-speed and memory benchmarks on primitive navigation and integration exemplar algorithms. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗

DOE OSTI2016

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We also demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Advanced Scientific Computing Research Exascale Requirements Review. An Office of Science review sponsored by Advanced Scientific Computing Research, September 27-29, 2016, Rockville, Maryland

The widespread use of computing in the American economy would not be possible without a thoughtful, exploratory research and development (R&D) community pushing the performance edge of operating systems, computer languages, and software libraries. These are the tools and building blocks — the hammers, chisels, bricks, and mortar — of the smartphone, the cloud, and the computing services on which we rely. Engineers and scientists need ever-more specialized computing tools to discover new material properties for manufacturing, make energy generation safer and more efficient, and provide insight into the fundamentals of the universe, for example. The research division of the U.S. Department of Energy’s (DOE’s) Office of Advanced Scientific Computing and Research (ASCR Research) ensures that these tools and building blocks are being developed and honed to meet the extreme needs of modern science. See also http://exascaleage.org/ascr/ for additional information.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

SNL ATDM Visualization (ECP ST Capability Assessment Report)

The SNL ATDM Data and Visualization work consolidates existing ATDM activities in scalable data management and visualization. Part of the responsibilities of the SNL ATDM Data and Visualization Project is the maintenance and development of visualization resources for ATDM applications on Exascale platforms. The ATDM Scalable Visualization project provides visualization and analysis required to satisfy the needs of the ASC/ATDM applications on next-generation, many-core platforms. This involves many activities including the re-engineering of visualization algorithms, services, and tools that enable ASC customers to carry out data analysis on ASC systems and ACES platforms. Current tools include scalable data analysis software released open source through ParaView, VTK, and Catalyst. We are also both leveraging and contributing to VTK-m, a many-core visualization library, to satisfy our visualization needs on advanced architectures. The scope of the Scalable Visualization under ATDM at SNL is R&D for the programming model and implementation of visualization code for ASC/ATDM projects and ASC/ATDM application support.

97 MATHEMATICS AND COMPUTING↗