Wide-Bandgap Power Electronics Reliability: Device Physics to Converter Performance
Abstract not provided.
Publications
Search results
Jump to search filtersNo tImplementing transition--edge sensors in a tabletop edge sensors in a tabletop xx--ray CT system for imaging applicationsray CT system for imaging applicationsitle
Abstract not provided.
Chemical and Physical Aging in Epoxies
Abstract not provided.
An introduction to developing GitLab/Jacamar runner analyst centric workflows at Sandia
This document provides very basic background information and initial enabling guidance for computational analysts to develop and utilize GitOps practices within the Common Engineering Environment (CEE) and High Performance Computing (HPC) computational environment at Sandia National Laboratories through GitLab/Jacamar runner based workflows.
Iron-and Bipyridine-based Charge Carriers for Non-Aqueous Flow Batteries
Abstract not provided.
Debris risks and mitigation strategies for hydrokinetic turbines
Abstract not provided.
RANGERS Design and Performance Assessment of Engineered Barrier Systems in a Salt Repository for HLW/SNF
Abstract not provided.
MISSISSIPPI RIVER CHARACTERIZATION FOR CURRENT ENERGY CONVERTER SYSTEMS
Abstract not provided.
A hub-mounted SpinnerLidar for the RAAW Experiment: Design, progress, and anticipated analyses
Abstract not provided.
Sandia National Labs: Effects of Stability on Blockage in LES Simulations
Abstract not provided.
Applications of Autonomous Sensor Task Planner for Intelligence, Surveillance and Reconnaissance
Abstract not provided.
Sierra/SD - How To Manual - 5.10
The How To Manual supplements the User’s Manual and the Theory Manual. The goal of the How To Manual is to reduce learning time for complex end to end analyses. These documents are intended to be used together. See the User’s Manual for a complete list of the options for a solution case. All the examples are part of the Sierra/SD test suite. Each runs as is. The organization is similar to the other documents: How to run, Commands, Solution cases, Materials, Elements, Boundary conditions, and then Contact. The table of contents and index are indispensable. The Geometric Rigid Body Modes section is shared with the Users Manual.
Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2022)
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign.
Effects of Discrete Fracture Network Modeling Choices on Repository Performance Characteristics
Abstract not provided.
Microstructure-Sensitive UQ for Materials Constitutive Models in Crystal Plasticity Finite Element Method
Abstract not provided.
H2+ Quasi Molecular Line Shape Profiles in Stellar Atmospheres
Abstract not provided.
Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations
Abstract not provided.
Depolymerization of LDPE and Lignin for Bioconversion through Sulfonation Followed by Fenton Reaction
Abstract not provided.
Downhole Smart Collar Technology for Wireless Real-Time Fluid Monitoring
Abstract not provided.
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference
Computer Methods in Applied Mechanics and Engineering
In this work we employ an encoder–decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities. The process we model is complex, with a progression from initial void nucleation, to saturation, and ultimately failure. The objective of predicting failure locations presents an extreme case of class imbalance since most of the material in the specimens does not fail. In response to this challenge, we develop and demonstrate the effectiveness of data- and loss-based regularization methods. Since there is considerable sensitivity of the failure location to the particular configuration of voids, we also use variational inference to provide uncertainties for the neural network predictions. We connect the deterministic and Bayesian convolutional neural network formulations to explain how variational inference regularizes the training and predictions. We demonstrate that the resulting predicted variances are effective in ranking the locations that are most likely to fail in any given specimen.
How to do Business with Sandia National Laboratories
Abstract not provided.
Informing Missing Physics with Model Form Error and Model Selection
Abstract not provided.
Phase Transitions in SiC in the Interiors of Carbon-Rich Exoplanets
Abstract not provided.
Toward Automatic Test Synthesis for Performance Portable Programs
Abstract not provided.
The role of oxygen vacancies on DC lifetime and TSDC in Bi(Zn,Ti)O3-BaTiO3 (BZT-BT)
Abstract not provided.
Results 376–400 of 2,394