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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Improved Axisymmetric and High Temperature Material Structural Modeling in MOOSE and NEML

This report describes improvements made to the solid mechanics formulation in the MOOSE open source finite element simulation environment and the open source Nuclear Engineering Material model Library (NEML) for mechanical constitutive models. The focus of these improvements is to improve the usability and performance of simulations involving one or both pieces of software. Specifically, this work completes a new system for solid mechanics simulations in the MOOSE ecosystem providing exact linearizations and optimal (quadratic) convergence, for a variety of coordinate systems and material types, including large deformation simulations. This work then provides users a framework to build highly efficient mechanical simulations of structures or materials or to couple in additional MOOSE physics modules to build complex, scalable multiphysics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Summary of Technical Peer Review on the Risk Assessment Framework proposed in Report INL/RPT-22-68656 for Digital Instrumentation and Control Systems

This report summarizes the peer review activities initiated by Idaho National Laboratory (INL) during fiscal year (FY) 2023 for the evaluation and improvement of the methodology developed under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. The R&D efforts of this project from FY 2019 through FY 2022 were focused on methodology improvement and demonstration of the proposed framework for the risk assessment and design optimization of safety-critical DI&C systems. Collaborations with the nuclear industry have been initiated to support the reliability and risk assessment of their DI&C systems by using the proposed framework. In FY 2023, the framework has reached to a point for a technical peer review and obtain stakeholder feedback. This peer review activity includes coordination of the reviews performed by a group of industry stakeholders, documentation of the peer review suggestions, providing resolutions and responses to the peer review comments. The objective of this technical peer review is to obtain representative feedback on the proposed framework to improve the technical qualities of its methodology and readiness for deployment to the industry. Feedback may identify potential areas for improvement and further development. The Subject Matter experts were invited to review the latest project report documenting the methodology developed in the project and provide evaluations of the technical qualities of the proposed framework and relevant methods. The reviewed project report is “An Integrated Framework for Risk Assessment of High Safety-significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration” INL/RPT-22-68656 (short as “INL/RPT-22-68656” in this report). This peer review report documents the technical questions provided for technical peer review and introduces the technical peer reviewers from the stakeholders including nuclear utilities, regulators, and universities. Comments from technical peer reviewers and the resolutions and responses to these comments are outlined. Insights and lessons learned from the technical peer review are summarized in conclusions and future work. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

HyRAM (Hydrogen Risk Assessment Models) v. 1.0

HyRAM is a software toolkit for conducting quantitative risk assessment (QRA) and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM contains validated, simplified hydrogen behavior models, a standardized QRA approach, and engineering models and generic data relevant to hydrogen installations. HyRAM 1.0 was developed by Sandia National Laboratories for the U. S. Department of Energy to increase access to technical data about hydrogen and to enable the use of that data to support development and revision of national and international codes and standards with risk assessment and consequence modeling. The HyRAM toolkit integrates analytical and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing the impact of hydrogen hazards (including thermal effects from jet fires and overpressure effects from deflagrations in confined areas) on people and structures. HyRAM incorporates generic probabilities for equipment failures for nine types of components, and probabilistic models for the impact of heat flux on humans and structures, with computationally and experimentally validated analytical first order models of hydrogen release and flame physics. Version 1 can be used to quantify the risk and consequences associated with gaseous hydrogen releases from user-defined hydrogen installations.

Groth, Katrina M.↗
DOE OSTI2018

Performance study of atomic tally methods for GPU-accelerated Monte Carlo dose calculation - 25352

Over the past several years, the graphics processing unit (GPU) technology has rapidly gained ground in scientific computing due to its outstanding performance and programmability. GPU implementation of Monte Carlo radiation transport for dose calculations has been reported by many investigators. The majority of these studies adopted single-precision floating point format because of the higher peak floating point operations per second (FLOPS) the GPUs can deliver than double-precision. It has been known that calculation using single-precision is more prone to numerical round-off errors, especially when a single tally data is accumulated 'atomically' and repeatedly by thousands of GPU threads. To mitigate this problem, the least intrusive solution in theory is to replace the single-precision atomic-add tally function with a double-precision version. However, the complexity lies in the fact that some GPUs (Nvidia GPUs prior to the Pascal generation; all current AMD GPUs) do not readily offer such double-precision function at hardware level, and that software emulation is too slow to use if not optimized properly. This paper discusses several atomic-add tally methods with reduced numerical errors used throughout ARCHER development. The original software-based compare-and-swap method (CAS) was shown to be inefficient due to high intra-warp thread contention, whereas the improved software-based warp-aggregated method (WAG) and Kahan summation method (KAS) eliminated the thread contention and performed very well on Kepler and Maxwell GPUs, being more than 13 times faster than CAS in our tests. The hardware-based (HB) double-precision atomic-add feature available on Pascal and Volta GPUs exceeded software emulation and offered the best performance universally, so did WIB, a combination of WAG and HB methods. It was also shown that in a single-precision dose engine, KAS managed to maintain high reliability when the single-precision atomic-add tally underestimated the dose by 25%. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Modeling and Simulation of Compression Molding Process for Sheet Molding Compound (SMC) of Chopped Carbon Fiber Composites

Compression molded SMC composed of chopped carbon fiber and resin polymer which balances the mechanical performance and manufacturing cost presents a promising solution for vehicle lightweight strategy. However, the performance of the SMC molded parts highly depends on the compression molding process and local microstructure, which greatly increases the cost for the part level performance testing and elongates the design cycle. ICME (Integrated Computational Material Engineering) approaches are thus necessary tools to reduce the number of experiments required during part design and speed up the deployment of the SMC materials. As the fundamental stage of the ICME workflow, commercial software packages for SMC compression molding exist yet remain not fully validated especially for chopped fiber systems. In this study, SMC plaques are prepared through compression molding process. The corresponding simulation models are built in Autodesk Moldflow with the same part geometry and processing conditions as in the molding tests. The output variables of the compression molding simulations, including press force history and fiber orientation of the part, are compared with experimental data. Influence of the processing conditions to the fiber orientation of the SMC plaque is also discussed. It is found that generally Autodesk Moldflow can achieve a good simulation of the compression molding process for chopped carbon fiber SMC, yet quantitative discrepancies still remain between predicted variables and experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SD-WAN Evaluation Criteria for a Defense Information Systems Network Expeditionary Customer Edge

The Defense Information Systems Agency (DISA) has identified a service provision gap midway between the capacity and capability of a Defense Information Systems Network (DISN) transport Edge Points of Presence (POP) and U.S. Department of Defense (DoD) Enterprise Classified Travel Kit (DECTK). Some deployed force 100-user Tactical Operations Centers are in field locations far removed from the DISN transport core, in challenging environmental conditions with limitations on space, weight, power, and cooling for network equipment. As part of a multi-phase project, Pacific Northwest National Laboratory (PNNL) will gather and analyze requirements, design, develop, prototype, and test a miniaturized and ruggedized DISN Customer Edge POP scaled to support approximately 100 users in a field Tactical Operations Center. An Expeditionary Customer Edge (ECE) will be more suitable for field deployment than a DISN transport Edge POP, using ruggedized hardware and network function virtualization (NFV) to operate in challenging field environmental conditions, plus reduce weight, power, and cooling requirements. A key enabling technology for ECE is Software Defined Wide Area Networking (SD-WAN). This document provides: • A brief overview of SD-WAN use cases and how they apply to ECE • How SD-WAN technology compares to existing networks like DISN that use Optical Transport Networking (OTN) and Multiprotocol Label Switching (MPLS) • SD-WAN functional requirements relevant to ECE • A description of an ECE Virtual Prototype, including simulated wide area network paths and a DISN-representative implementation of MPLS, in Cisco Modeling Labs • Detailed network flow walkthroughs • Evaluation criteria based on ECE-relevant SD-WAN functional requirements Finally, an appendix provides an informal evaluation of Speedify—a commercial retail Virtual Private Network service—against the ECE SD-WAN evaluation criteria.

42 ENGINEERING↗
DOE OSTI2020

Multi-objective Optimization Paradigm for Toroidal Inductors with Spatially Tuned Permeability

Spatially tuning core permeability of an electromagnetic device enables superior performance. A permeability profile can be heuristically selected to improve the flux distribution in a device with a given geometry, but in order to fully leverage the capacity of spatial dependent permeability engineering, the geometry and the permeability should be optimized simultaneously. The work herein presented sets forth a multi-physics design optimization paradigm that includes the permeability profile tuning in the context of an inductor design. This approach enables the determination of Pareto optimal fronts consisting of a set of optimal solutions against competing objectives (e.g. mass and loss) under imposed constraints. To this end, analytical solutions of the heat transfer and electromagnetic formulations are derived for toroidal inductors. The software implemented in Matlab 2018b is available online as an attachment to this paper.

42 ENGINEERING↗
DOE OSTI2015

Recovery Act--Class 8 Truck Freight Efficiency Improvement Project

Daimler Trucks North America completed a five year, $79.6M project to develop and demonstrate a concept vehicle with at least 50% freight efficiency improvement over a weighted average of several drive cycles relative to a 2009 best-in-class baseline vehicle. DTNA chose a very fuel efficient baseline vehicle, the 2009 Freightliner Cascadia with a DD15 engine, yet successfully demonstrated a 115% freight efficiency improvement. DTNA learned a great deal about the various technologies that were incorporated into Super Truck and those that, through down-selection, were discarded. Some of the technologies competed with each other for efficiency, and notably some of the technologies complemented each other. For example, we found that Super Truck’s improved aerodynamic drag resulted in improved fuel savings from eCoast, relative to a similar vehicle with worse aerodynamic drag. However, some technologies were in direct competition with each other, namely the predictive technologies which use GPS and 3D digital maps to efficiently manage the vehicles kinetic energy through controls and software, versus hybrid which is a much costlier technology that essentially targets the same inefficiency. Furthermore, the benefits of a comprehensive, integrated powertrain/vehicle approach was proven, in which vast improvements in vehicle efficiency (e.g. lower aero drag and driveline losses) enabled engine strategies such as downrating and downspeeding. The joint engine and vehicle developments proved to be a multiplier-effect which resulted in large freight efficiency improvements. Although a large number of technologies made the selection process and were used on the Super Truck demonstrator vehicle, some of the technologies proved not feasible for series production.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

National Rotor Testbed Requirements. Draft

This document serves both as a guide and a record for requirements management associated with design of retrofit rotors for the Sandia SWiFT turbines. The rotors will support a long-term experimental campaign. Data gathered during the campaign will support formal verification and validation of complex flow numerical models for prediction of metrics deemed important for wind energy plant efficiency. These integrated projects involve multiple years of effort, dozens of engineers and collaborators, and dozens of stakeholders in the form of the research community, National Lab staff, and DOE staff. Creating of formal, written requirements will ensure flow-down of activities from high-level goals, aid communication, and enable clear verification of activities. A custom requirements schema is created and described. Commercial requirements management software —IBM Rational DOORS—is used to organize the information and ensure traceability. This document serves as a record of the process as well as a record of the exported, detailed contents of the DOORS database.

17 WIND ENERGY↗
DOE OSTI2014

Proceedings of the National Renewable Energy Laboratory Wind Energy Systems Engineering Workshop

The second National Renewable Energy Laboratory (NREL) Wind Energy Systems Engineering Workshop was held in Broomfield, Colorado, from January 29 to February 1, 2013. The event included a day-and-a-half workshop exploring a wide variety of topics related to system modeling and design of wind turbines and plants. Following the workshop, 2 days of tutorials were held at NREL, showcasing software developed at Sandia National Laboratories, the National Aeronautics and Space Administration's Glenn Laboratories, and NREL. This document provides a brief summary of the various workshop activities and includes a review of the content and evaluation results from attendees.

17 WIND ENERGY↗
DOE OSTI2025

Nanopolysaccharide Builder: A User-Friendly Tool for Atomistic Models of Polysaccharide-Based Nanostructures

Here, we introduce Nanopolysaccharide Builder (NPB), a user-friendly software tool designed to construct polysaccharide nanostructures─mainly those based on cellulose, chitin, and chitosan─using experimental data or user-defined parameters. NPB enables the generation of cellulose and chitin allomorphs with customizable biochemical topologies and also facilitates the construction of large bundles that replicate nanostructures found in biological support systems, including plant cell walls and arthropod cuticles. The software outputs atomic Cartesian coordinates in Protein Data Bank (PDB) format and also provides atom connectivity files in PSF and PARM formats, ensuring seamless integration with major molecular dynamics (MD) engines such as NAMD, CHARMM, GROMACS, AMBER, OpenMM, and LAMMPS. Built on an interactive visualization framework, NPB features a graphical user interface (GUI) and supports both macOS and Linux operating systems. By enabling detailed atomic-scale studies of polysaccharide evolution in extracellular matrices and cell walls of algae, bacteria, fungi, and plants, NPB is poised to advance AI-guided research in sustainable chemical development and biomass utilization.

Wan, Zhangmin [Univ. of British Columbia, Vancouve↗
DOE OSTI2019

Metalearners for estimating heterogeneous treatment effects using machine learning

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

42 ENGINEERING↗
DOE OSTI2019

Finite-Difference Simulation of Eddy Currents in Nonmagnetic Sheets via Electric Vector Potential

The magnetic excitation of eddy currents in thin metal sheets is a mathematically challenging problem with many practical applications. Since the eddy current density is a solenoidal field on a 2-D plane, we may express it as the curl of a separate vector field with only one scalar component. This new field is governed by a unique form of the vector-Poisson equation that is readily solved numerically through standard finite-difference techniques. For the special case of a weakly induced eddy current, the self-inductance terms can be neglected to produce a sparse system matrix that is easily inverted. For a strongly induced eddy current, self-inductance must be included at the cost of a more complex and dense system matrix. The method is validated against the CST EM Studio software suite by producing nearly identical results in only a tiny fraction of the computational time.

42 ENGINEERING↗
DOE OSTI2020

Differential Power Processing for Ultra-Efficient Data Storage

Here this paper presents the hardware, software, and power codesign of an ultra-efficient data storage server with differential power processing (DPP). DPP can reduce the power conversion stress, improve the efficiency, and enhance the functionality of modular power electronics systems. The power inputs of a large number of hard disk drives (HDDs) were connected in series and supported by a multiport ac-coupled differential power processing (MAC-DPP) converter through a multiwinding transformer. Methods for controlling the multi-input multi-output power flow in the multiwinding transformer while avoiding core saturation were investigated. A ten-port MAC-DPP prototype with 700-W/in 3 power density was built to support a 450-W HDD storage system with ten series-stacked voltage domains. The prototype was tested on a 50-HDD server testbench, and the overall system loss is below 1 W (99.77% system efficiency). The server was able to maintain high-speed reading and writing operation of all 50 HDDs against the worst hot-swapping scenarios. A variety of hardware/software configurations and many cloud storage techniques were tested on the fully functioning server. Experimental results show that the energy efficiency of large-scale information systems (CPU/GPU clusters, memory banks, HDD arrays, etc.) can be greatly improved by software, hardware, and power codesign.

42 ENGINEERING↗
DOE OSTI2020

Spawn [SWR-SWR-20-10]

Spawn is a next generation building simulation engine designed to build on the strengths of NREL's venerable EnergyPlus engine, and add new advanced HVAC and controls modeling capability by incorporating the Modelica equation based modeling language. In effect, Spawn is a hybrid of the existing EnergyPlus and Modelica software products, with some new software to provide glue between the components in order to provide a seamless end user experience.

Li, Yanfei↗
DOE OSTI2021

SSP Personality Programmer

The Personality Programmer software is used as means for entities to configure, deploy, and redeploy SSP devices. SAND2020-14337 O 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.

Hunt, TannerR.↗
DOE OSTI2020

BigDNA

SAND2020-13696 M BigDNA takes a user input fast DNA sequence and outputs overlapping long PCR primers for Gibson Assembly. The software can design primers that remove gene products, insert new gene products, or completely circularize long DNAs. 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.

Porter, Kelley↗