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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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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Overview and How-to Tutorial Videos for Using NEWTS Data

Overview and How-to Tutorial Videos for Using NEWTS Data Video 1: Overview of NEWTS Database Video 2: How-to tutorial for EPA Flue Gas Desulfurization (FGD) Effluent NEWTS dataset Video 3: How-to tutorial for USGS Produced Waters NEWTS dataset Video 4: How-to tutorial for EPA Ash NEWTS dataset Video 5: How-to tutorial for Quillinan, et al 2018 DOE Geothermal Technology Office REE dataset Video 6: Tutorial video on navigating the NEWTS Dashboard, with an overview of NEWTS and navigating between the NEWTS Dashboard and Datasets (https://netl-doe.maps.arcgis.com/apps/dashboards/a5fa4192f7c6478dab3d6180d9c30b84) Video 7: Additional tutorial video on navigating the NEWTS Dashboard and investigating specific data points in the Dashboard and Datasets Video 8: Re-record of recent webinar giving an overview of the NEWTS Database and Dashboard, including interacting with the NEWTS Dashboard, locating specific data points, and finding the relevant streams in the NEWTS Database and datasets on EDX. Includes overview of the datasets, case studies, and steps for taking stream data from the database and modeling stream data in OLI Studio and Geochemist's Workbench. Note: Video 3 tutorial is also applicable to the USGS Brackish Water NEWTS dataset.

Aqueous Chemistry↗

DOE OSTI2017

Community Earth System Model (CESM) Tutorial 2016 Final Report

For the 2016 tutorial, NCAR/CGD requested a total budget of $70,000 split equally between DOE and NSF. The funds were used to support student participation (travel, lodging, per diem, etc.). Lectures and practical session support was primarily provided by local participants at no additional cost (see list below). The seventh annual Community Earth System Model (CESM) tutorial (2016) for students and early career scientists was held 8 – 12 August 2016. As has been the case over the last few years, this event was extremely successful and there was greater demand than could be met. There was continued interest in support of the NSF’s EaSM Infrastructure awards, to train these awardees in the application of the CESM. Based on suggestions from previous tutorial participants, the 2016 tutorial experience again provided direct connection to Yellowstone for each individual participant (rather than pairs), and used the NCAR Mesa Library. The 2016 tutorial included lectures on simulating the climate system and practical sessions on running CESM, modifying components, and analyzing data. These were targeted to the graduate student level. In addition, specific talks (“Application” talks) were introduced this year to provide participants with some in-depth knowledge of some specific aspects of CESM.

58 GEOSCIENCES↗

DOE OSTI2014

2013 Community Earth System Model (CESM) Tutorial-Proposal to DOE

THE SAME REQUEST WILL BE SENT TO BOTH NSF AND DOE TO EACH SUPPORT $35K. The third annual Community Earth System Model (CESM) tutorial for students and early career scientists was held from 30 July to 3 August, 2012. This event was extremely successful and, as for the tutorials in previous years, there was a greater demand than could be met. This indicates a continuing need for a tutorial of this type and we anticipate that the 2013 tutorial will be well received. The tutorial will include lectures on simulating the climate system and practical sessions on running CESM, modifying components, and analyzing data. These will be targeted to the graduate student level. Attendance will be limited to a maximum of 80 students with financial support for up to 40 students. Attendees will be balanced across institutions.

Holland, Marika↗

DOE OSTI2025

PrOMMiS Tutorial

This is a tutorial on using parameter estimation and process optimization in PrOMMiS. The tutorials are publicly available in the GitHub repository. The presentation attached guides through the flow of the three different tutorials. The tutorials are presented as follows: i) Parameter estimation of oxalate precipitation, ii) Optimization of precipitation model, and iii) Optimization of full process.

critical minerals and materials↗

DOE OSTI2018

Tutorial on interpreting x-ray photoelectron spectroscopy survey spectra: Questions and answers on spectra from the atomic layer deposition of Al 2 O 3 on silicon

X-ray photoelectron spectroscopy (XPS) has become the most widely used method for chemically analyzing surfaces. In XPS, photoelectrons are generated by irradiating a surface with X-rays. As the importance and popularity of XPS have grown, it has drawn users without significant XPS experience, and incorrect and incomplete interpretations of XPS spectra regularly appear in the literature. This tutorial is designed as a tool to guide less experienced users in analyzing XPS survey spectra. Here, we examine a series of XPS survey spectra collected during the atomic layer deposition of Al2O3 from trimethylaluminum and water precursors. Prior to this, brief explanations of XPS and atomic layer deposition (ALD) are presented. This tutorial is structured as a series of questions and answers that the interested reader may choose to engage in. The XPS spectra are scrutinized to extract information about the elements present in the film, the presence of contamination, and the nature of the film growth process. The questions and answers in this tutorial address important fundamental issues common to the interpretation of many XPS survey spectra in the context of ALD.

XPS spectrum, Tutorial, Atomic layer deposition↗

DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗

DOE OSTI2025

Decoding diffraction and spectroscopy data with machine learning: A tutorial

This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

36 MATERIALS SCIENCE↗

DOE OSTI2014

The MueLu Tutorial

The MueLu tutorial is written as a hands-on tutorial for MueLu, the next generation multigrid framework in Trilinos. It covers the whole spectrum from absolute beginners’ topics to expert level. Since the focus of this tutorial is on practical and technical aspects of multigrid methods in general and MueLu in particular, the reader is expected to have a basic understanding of multigrid methods and its general underlying concepts. Please refer to multigrid textbooks (e.g. [1]) for the theoretical background.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

Contingency Contractor Optimization Tool Prototype (Phase 3 Extension- Analyst Tutorial)

This tutorial walks the user through analysis examples using the Contingency Contractor Optimization Tool Prototype. The examples are designed to showcase key capabilities of the tool. The main goal of this tutorial is to provide examples of how to use the tool to perform analyses to those users acting in the analyst role. All examples and locations used in the prototype are fictional, but are intended to be realistic. Users reading this manual are expected to have a basic understanding and familiarity with the Contingency Contractor Optimization Tool Prototype. This tutorial includes scenarios for the occurrence of two wars, Prussia and New Granada.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

A Tutorial Set to Prepare for Science with the Vera C. Rubin Observatory

In this poster the Rubin Observatory's Community Science team (CST) presents its current suite of tutorials, which are designed to help people make use of simulated data sets in preparation for the upcoming Legacy Survey of Space and Time (LSST). We will show examples of the tutorial contents, provide custom learning modules for different astronomical fields, and describe the online environment for data analysis (the Rubin Science Platform; RSP). We will also supply a checklist for how to obtain an RSP account and access the tutorials. All are welcome to drop by the poster or the Rubin booth in the exhibit hall with questions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

A Tutorial Set to Prepare for Science with the Vera C. Rubin Observatory

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

UNIFI's Grid-Forming (GFM) Inverter Reference Design: A Tutorial on Modeling, Control, and Experimental Implementation of GFM Inverters

The UNIFI Consortium's tutorial on grid-forming (GFM) inverters provides a comprehensive guide to the modeling, control, and experimental implementation of GFM inverters. As the integration of renewable energy accelerates, the transition from traditional grid following (GFL) to GFM inverters is crucial to ensure stable and sustainable power systems. This document outlines a reference design for three-phase and single-phase GFM inverters developed at the University of Texas at Austin. The tutorial also provides step-by-step guidance for accessing and using UNIFI’s GitHub repository, enabling users to design, build, and test GFM inverters efficiently. By fostering collaboration and equipping users with accessible resources, this initiative aims to drive widespread adoption of GFM technology across academia, utilities, and industries.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Challenges and opportunities in understanding microbial communities with metagenome assembly (accompanied by IPython Notebook tutorial)

Metagenomic investigations hold great promise for informing the genetics, physiology, and ecology of environmental microorganisms. Current challenges for metagenomic analysis are related to our ability to connect the dots between sequencing reads, their population of origin, and their encoding functions. Assembly-based methods reduce dataset size by extending overlapping reads into larger contiguous sequences (contigs), providing contextual information for genetic sequences that does not rely on existing references. These methods, however, tend to be computationally intensive and are again challenged by sequencing errors as well as by genomic repeats. While numerous tools have been developed based on these methodological concepts, they present confounding choices and training requirements to metagenomic investigators. To help with accessibility to assembly tools, this review also includes an IPython Notebook metagenomic assembly tutorial. This tutorial has instructions for execution any operating system using Amazon Elastic Cloud Compute and guides users through downloading, assembly, and mapping reads to contigs of a mock microbiome metagenome. Despite its challenges, metagenomic analysis has already revealed novel insights into many environments on Earth. As software, training, and data continue to emerge, metagenomic data access and its discoveries will to grow.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Transport of intensity equation: a tutorial

When it comes to “phase measurement” or “quantitative phase imaging”, many people will automatically connect them with “laser” and “interferometry”. Indeed, conventional quantitative phase imaging and phase measurement techniques generally rely on the superposition of two beams with a high degree of coherence: complex interferometric configurations, stringent requirements on the environmental stabilities, and associated laser speckle noise severely limit their applications in optical imaging and microscopy. On a different note, as one of the most well-known phase retrieval approaches, the transport of intensity equation (TIE) provides a new non-interferometric way to access quantitative phase information through intensity only measurement. Despite the insufficiency for interferometry, TIE is applicable under partially coherent illuminations (like the Köhler’s illumination in a conventional microscope), permitting optimum spatial resolution, higher signal-to-noise ratio, and better image quality. In this tutorial, we give an overview of the basic principle, research fields, and representative applications of TIE, focus particularly on optical imaging, metrology, and microscopy. The purpose of this tutorial is twofold. It should serve as a self-contained introduction to TIE for readers with little or no knowledge of TIE. On the other hand, it attempts to give an overview of recent developments in this field. These results highlight a new era in which strict coherence and interferometry are no longer prerequisites for quantitative phase imaging and diffraction tomography, paving the way toward new generation label-free three-dimensional microscopy, with applications in all branches of biomedicine.

42 ENGINEERING↗

DOE OSTI2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. In conclusion, two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Tutorial review on structure – dendrite growth relations in metal battery anode supports

This tutorial review explains the emerging understanding of the surface and bulk chemistry – electrochemical performance relations in anode supports (aka secondary current collectors, substrates, templates, hosts) for lithium, sodium and potassium metal batteries (LMBs, SMBs or NMBs, and KMBs or PMBs). In relation to each section, the possible future research directions that may yield both new insight and improved cycling behavior are explored. Representative case studies from Li, Na and K metal anode literature are discussed. The tutorial starts with an overview of the solid electrolyte interphase (SEI), covering both the “classic” understanding of the SEI structure and the “modern” insights obtained by site-specific cryogenic stage TEM analysis. Next, the multiple roles of supports in promoting cycling stability are detailed. Without an optimized support architecture, the metal–electrolyte interface becomes geometrically unstable at a lower current density and cycle number. Taking into consideration the available literature on LMBs, SMBs and KMBs, it is concluded that effective architectures are geometrically complex and electrochemically lithiophilic, sodiophilic or potassiophilic, so as to promote conformal electrochemical wetting of the metal during plating/stripping. One way that philicity is achieved is through support oxygen surface chemistry, which yields a reversibly reactive metal–support interface. Examples of this include the well-known oxygen–carbon moieties in reduced graphene oxide (rGO), as well as classic ion battery reversible conversion reaction oxides such as SnO 2 . Unreactive surfaces lead to dewetted island growth of the metal, which is a precursor to dendrites, and possibly to non-uniform dissolution. Surveying the literature on various Li, Na and K metal supports, it is concluded that the key bulk thermodynamic property that will predict electrochemical wetting behavior is the enthalpy of infinite solution (Δ sol H ∞ ) of the metal (solute) into the support (solvent). Large and negative Δ sol H ∞ promotes uniform metal wetting on the support surface, corresponding to relatively low plating overpotential. Positive Δ sol H ∞ promotes dewetted islands and a relatively high overpotential. This simple rule explains a broad range of studies on Li, Na and K metal – support interactions, including the previously reported correlation between mutual solubility and wetting.

25 ENERGY STORAGE↗

DOE OSTI2017

Tutorial: Reactive high power impulse magnetron sputtering (R-HiPIMS)

High Power Impulse Magnetron Sputtering (HiPIMS) is a coating technology that combines magnetron sputtering with pulsed power concepts. Furthermore, by applying power in pulses of high amplitude and a relatively low duty cycle, large fractions of sputtered atoms and near-target gases are ionized. In contrast to conventional magnetron sputtering, HiPIMS is characterized by self-sputtering or repeated gas recycling for high and low sputter yield materials, respectively, and both for most intermediate materials. The dense plasma in front of the target has the dual function of sustaining the discharge and providing plasma-assistance to film growth, affecting the microstructure of growing films. Many technologically interesting thin films are compound films, which are composed of one or more metals and a reactive gas, most often oxygen or nitrogen. When reactive gas is added, non-trivial consequences arise for the system because the target may become “poisoned,” i.e., a compound layer forms on the target surface affecting the sputtering yield and the yield of secondary electron emission and thereby all other parameters. It is emphasized that the target state depends not only on the reactive gas' partial pressure (balanced via gas flow and pumping) but also on the ion flux to the target, which can be controlled by pulse parameters. This is a critical technological opportunity for reactive HiPIMS (R-HiPIMS). The scope of this tutorial is focused on plasma processes and mechanisms of operation and only briefly touches upon film properties. It introduces R-HiPIMS in a systematic, step-by-step approach by covering sputtering, magnetron sputtering, reactive magnetron sputtering, pulsed reactive magnetron sputtering, HiPIMS, and finally R-HiPIMS. The tutorial is concluded by considering variations of R-HiPIMS known as modulated pulsed power magnetron sputtering and deep-oscillation magnetron sputtering and combinations of R-HiPIMS with superimposed dc magnetron sputtering.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

A Tutorial on Bayesian analysis of linear shock compression data

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure–volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this Tutorial shows how to sample multiple Hugoniot curves in the pressure–volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine–Hugoniot equations to yield Hugoniot curves in the pressure–volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a Tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and make all code and data available at github.com/llnl/BALSCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗