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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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This tutorial introduces typical hardware and software characteristics of extant and emerging supercomputing platforms, and presents issues and solutions in executing large-scale parallel discrete-event simulation scenarios on such high performance computing systems. Covered topics include synchronization, model organization, example applications, and observed performance from illustrative large-scale runs.
Through-silicon vias (TSVs) are a critical technology for three-dimensional integrated circuit technology. These through-substrate interconnects allow electronic devices to be stacked vertically for a broad range of applications and performance improvements such as increased bandwidth, reduced signal delay, improved power management, and smaller form-factors. There are many interdependent processing steps involved in the successful integration of TSVs. This article provides a tutorial style review of the following semiconductor fabrication process steps that are commonly used in forming TSVs: deep etching of silicon to form the via, thin film deposition to provide insulation, barrier, and seed layers, electroplating of copper for the conductive metal, and wafer thinning to reveal the TSVs. Recent work in copper electrochemical deposition is highlighted, analyzing the effect of accelerator and suppressor additives in the electrolyte to enable void-free bottom-up filling from a conformally lined seed metal.
Repository and website for tutorials on 3d Euclidean equivariant neural networks.
Tutorial on graph neural network (GNN) for the digital twin (DT) team at ORNL.
The software-tutorial was developed within the 2024 Remote Sensing and Fluxes Upscaling for Real-world Impact workshop as part of the hands-on session. The workshop was supported by AmeriFlux, National Ecological Observatory Network (NEON) and CarbonDew. The software provides basic tools to perform the following tasks: - gather remote sensing images using Google Earth Engine API; - gather flux data; - perform basic functions, such as plotting time-series, perform QA of the data, compute vegetation indices; - perform correlation analysis between flux data and remote sensing data; - perform flux predictions based on remote sensing data integrated in different modalities.
Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.
OCHRE user tutorial via a browser extension.
This presentation is a tutorial for the purposes of both predicting and assessing the external dose hazards from beta particles, as well as methods for controlling the external dose and performing various confirmatory measurements. It includes discussions of beta-ray spectral types and characteristics, tissue dose estimation from fundamental first physical principles, skin dose rates on contact with intense beta sources and at 30 cm in air from such sources, instruments to measure such doses, beta particle ranges in air and common shielding plastics, assessment of beta dose from skin contamination of various aerial extent, and instruments to perform such measurements.
This report details the work accomplished during my 2015 SULI summer internship at Sandia National Laboratories in Livermore, CA. During this internship, I worked on multiple tasks with the common goal of making uncertainty quantification (UQ) methods more accessible to the general scientific community. As part of my work, I created a comprehensive numerical integration example to incorporate into the user manual of a UQ software package. Further, I developed examples involving heat transfer through a window to incorporate into tutorial lectures that serve as an introduction to UQ methods.
This tutorial is an introduction to the classical cyclotron, with some hints at spin dynamics, hands-on: by numerical simulation. It begins with a brief reminder of the historic context, and then introduces the theoretical material needed for the simulation exercises, which follow. Basic charge particle optics and acceleration concepts are addressed, including closed orbit in a cyclic accelerator, weak focusing in a dipole magnet, periodic transverse motion, revolution period and isochronism, voltage gap and resonant acceleration, the cyclotron equation.
ReEDS is a publicly available model developed at NREL that can be used to analyze the potential evolution of the U.S. electric power system into the future. ReEDS is formulated as a linear program, written in GAMS, and solved using a linear programming solver (e.g., CPLEX). This tutorial provides context and understanding for the implications of using an open-source solver for ReEDS.
OpenSesame is a program for generating tabular equations of state (EOS), with capabilities for multiphase EOS construction. In this tutorial, we provide an overview of how to run OpenSesame to construct a multiphase EOS. We discuss some general features of OpenSesame, followed by a description of sample input files required for multiphase EOS construction. We also discuss how to extract data from EOS tables in order to compare to experimental data, with an example using the OpenSesame GUI. Lastly, we provide a description of how to generate ASCII-formatted EOS tables most often used by hydro code users.
The uncertainty quantification toolkit (UQTk) is a collection of c++ libraries that assess the confidence of numerical models. Surrogate approximations, often polynomial chaos expansions (PCEs), lessen the computational cost of these assessments. I developed a Python interface in UQTk for regression and Bayesian compressive sensing to add to the existing Galerkin projection method. These methods receive an object containing the polynomial basis information and NumPy arrays of sample points, call c++ methods, and return the PCE coefficients in a NumPy array. To demonstrate these methods, I wrote a tutorial in which I use them to construct surrogates for Genz functions and calculate the resulting error.
The following sample problem write-ups are designed as a tutorial for generating correlated random samples (e.g., replica multi-group cross section data) for use in propagation of uncertainty problems. These problems were set up and solved in MATLAB, but any programming environment with basic statistical functions can be used to generate similar results. Since linear sample problems were chosen, helpful comparisons to the “sandwich” rule propagation of uncertainty are available and were used.
This is part of the tutorial on markets and operations in US and Europe.
This manuscript describes the Waveform Simulation Framework (WSF), a Python-based framework that provides a unified, programmable interface for generating synthetic seismograms for applications such as seismic array design, method development, and special event analysis. WSF standardizes how users define sources, receivers, and velocity models while abstracting simulator-specific configuration details, enabling workflows that are largely independent of the underlying numerical engine. The document provides installation guidance and tutorial-driven examples for three WSF simulator wrappers—WSF PyFK, WSF SW4, and WSF SPECFEM2D—illustrating end-to-end workflows from forward waveform simulation to common post-processing tasks (e.g., visualization and backprojection) using consistent data products (e.g., ObsPy Stream objects and SAC files).
Two day SST tutorial for IPDPS
This is a tutorial dataset for AutoMicroED software ( https://github.com/pnnl/AutoMicroED )