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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

WOMBAT Tutorial

This workshop will provide a comprehensive overview to Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT), NREL's first wind O&M software model. The session will discuss how to utilize WOMBAT to answer O&M research questions covering land-based, offshore, and distributed wind farms. It will offer an introduction to the model and how it operates. The team will then offer a demonstration of the various modeling parameters by converting the CORE Wind Floating Wind Strategies O&M Assessment assumptions into an analysis-ready configuration, diving into the results, and exploring further model variations.

discrete event simulation↗
DOE OSTI2023

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗
DOE OSTI2016

Tutorial: Crystal orientations and EBSD — Or which way is up?

Electron backscatter diffraction (EBSD) is an automated technique that can measure the orientation of crystals in a sample very rapidly. There are many sophisticated software packages that present measured data. Unfortunately, due to crystal symmetry and differences in the set-up of microscope and EBSD software, there may be accuracy issues when linking the crystal orientation to a particular microstructural feature. In this paper we outline a series of conventions used to describe crystal orientations and coordinate systems. These conventions have been used to successfully demonstrate that a consistent frame of reference is used in the sample, unit cell, pole figure and diffraction pattern frames of reference. We establish a coordinate system rooted in measurement of the diffraction pattern and subsequently link this to all other coordinate systems. A fundamental outcome of this analysis is to note that the beamshift coordinate system needs to be precisely defined for consistent 3D microstructure analysis. This is supported through a series of case studies examining particular features of the microscope settings and/or unambiguous crystallographic features. These case studies can be generated easily in most laboratories and represent an opportunity to demonstrate confidence in use of recorded orientation data. Finally, we include a simple software tool, written in both MATLAB® and Python, which the reader can use to compare consistency with their own microscope set-up and which may act as a springboard for further offline analysis. - Highlights: • Presentation of conventions used to describe crystal orientations • Three case studies that outline how conventions are consistent • Demonstrates a pathway for calibration and validation of EBSD based orientation measurements • EBSD computer code supplied for validation by the reader.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep inverse photonic design: A tutorial

Deep learning (DL) has been used to design deep neural networks (DNNs) which have recently been applied to solving inverse problems in artificial electromagnetic materials (AEMs). Although inverse problems are often ill-posed, and therefore are difficult to solve, deep inverse models (DIMs) have achieved impressive results often surpassing capabilities possible with other approaches. We overview the process of deep inverse learning applied to AEM problems, including the building of data sets, design of a forward model, and comparison of inverse approaches including limitations. We conclude by detailing some important outstanding issues of deep inverse design of AEMs, and present an outlook of this exciting field.

42 ENGINEERING↗
DOE OSTI2021

A tutorial on radio frequency sheath physics for magnetically confined fusion devices

Radio frequency (RF) sheaths occur under a wide variety of conditions when RF waves, material surfaces and plasma coexist. RF sheaths are of special importance in describing the interaction of ion cyclotron range of frequency (ICRF) waves with the boundary plasma in tokamaks, stellarators and other magnetic confinement devices. In this article the basic physics of RF sheaths is discussed in the context of magnetic fusion research. Techniques for modelling RF sheaths, their interaction with RF wave fields and the resulting consequences are highlighted. The article is intended as a guide for the early-career ICRF researcher, but it may equally well serve to provide an overview of basic RF sheath concepts and modelling directions for any interested fusion scientist.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Periodic orbit analysis of a system with continuous symmetry—A tutorial

Dynamical systems with translational or rotational symmetry arise frequently in studies of spatially extended physical systems, such as Navier-Stokes flows on periodic domains. In these cases, it is natural to express the state of the fluid in terms of a Fourier series truncated to a finite number of modes. Here, we study a 4-dimensional model with chaotic dynamics and SO(2) symmetry similar to those that appear in fluid dynamics problems. A crucial step in the analysis of such a system is symmetry reduction. We use the model to illustrate different symmetry-reduction techniques. The system's relative equilibria are conveniently determined by rewriting the dynamics in terms of a symmetry-invariant polynomial basis. However, for the analysis of its chaotic dynamics, the “method of slices,” which is applicable to very high-dimensional problems, is preferable. We show that a Poincaré section taken on the 'slice' can be used to further reduce this flow to what is for all practical purposes a unimodal map. This enables us to systematically determine all relative periodic orbits and their symbolic dynamics up to any desired period. We then present cycle averaging formulas adequate for systems with continuous symmetry and use them to compute dynamical averages using relative periodic orbits. The convergence of such computations is discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Tutorial: Understanding residual stress in polycrystalline thin films through real-time measurements and physical models

Residual stress is a long-standing issue in thin film growth. Better understanding and control of film stress would lead to enhanced performance and reduced failures. In this work, we review how thin film stress is measured and interpreted. The results are used to describe a comprehensive picture that is emerging of what controls stress evolution. Examples from multiple studies are discussed to illustrate how the stress depends on key parameters (e.g., growth rate, material type, temperature, grain size, morphology, etc.). The corresponding stress-generating mechanisms that have been proposed to explain the data are also described. To develop a fuller understanding, we consider the kinetic factors that determine how much each of these processes contributes to the overall stress under different conditions. This leads to a kinetic model that can predict the dependence of the stress on multiple parameters. The model results are compared with the experiments to show how this approach can explain many features of stress evolution.

Chason, Eric↗