Vapor-Cell Based Atomic Metrology: Fundamentals and Innovations
These are the slides for an invited talk in UFFC-JS 2024 tutorial session
Jau, Yuan-Yu↗
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Results for “tutorials”
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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From Waste to Resource: Systems Framework for Unconventional Critical Material Feedstocks
Tutorial on unconventional critical material feedstocks at the 2025 Innovations in Critical Minerals and Materials Symposium at TechConnect in Austin, TX from June 9-11.
critical minerals↗
EVI-LOCATE User Manual
One of the longest stages in the deployment of electric vehicle supply equipment (EVSE) is the initial planning of the infrastructure itself. Engineers and fleet experts from the National Renewable Energy Laboratory (NREL) have supported dozens of charging infrastructure site plans over the past couple decades, including the generation of site schematics, determinations of electric capacity, and estimates for likely costs. As the market for electric vehicles (EVs) has matured, this approach should no longer require a time and personnel intensive process. In order to shorten the time taken to develop site plans and cost estimates, NREL developed a tool that fleet managers, facility managers, electricians, EVSE installers, and members of the public can use to develop initial schematics and ballpark pricing for charging station installations. The Electric Vehicle Infrastructure - Locally Optimized Charger Assessment Tool and Estimator (EVI-LOCATE) provides a structured and consistent way for users to enter information about their planned EVSE project in a relatively simple web-based format. EVI-LOCATE then calculates electrical equipment capacity, wiring runs, and project costs. It produces a site diagram optimized around surface characteristics with differential trenching costs for softscape such as grass compared to hardscape such as asphalt that can be adjusted by users in the tool. It also stores the resulting site plans and costs in a dashboard for access at a later date, including plan revisions if necessary. This document guides users through the EVI-LOCATE screens and associated questions. It contains tip text boxes throughout on how best to interface with the tool and find additional information or context. The appendices contain the assumptions and calculations underpinning the tool. Much of the information for EVI-LOCATE was gathered through industry engagements with EVSE installers, invoices from completed EVSE installations, Gordian's RS Means construction data, and the General Services Administration blanket purchase agreement for EVSE. For a visual tutorial of the tool, users can watch EVI-LOCATE Step-by-Step Video. The tool itself is available at https://evi-locate.nrel.gov.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Cleanroom Assembly and Cavity Testing
A tutorial about the cleanroom assembly and cavity testing. The presentation discusses how-tos, best practices, and lessons learned.
Wu, Genfa [Fermilab]↗
Uncertainty Quantification in Computational Science: from Physical Models to Neural Networks
Tutorial at the upcoming BIRS workshop https://www.birs.ca/events/2025/5-day-workshops/25w5381/schedule
Sargsyan, Khachik [Sandia National Laboratories (S↗
Sharing is Caring: A Practical Guide to FAIR(ER) Open Data Release
This is a two hour version of the FAIR(ER) tutorial we released at Barcelona 9/24. SAND2024-12152C. The only modifications were largely deletions, which don't require additional review. The one key difference that actually has changed material is in the Language section for Equitable Accessibility, which is almost word for word the same as previously approved SAND2025-04087W which is the website version of the presentation.
Henriksen, Amelia [Sandia National Laboratories (S↗
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, {PIML} models can be trained from additional information obtained by enforcing physical laws such as energy and mass conservation. More broadly, {PIML} models can include abstract properties and conditions such as stability, convexity, or invariance. The basic premise of {PIML} is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in {PIML} for dynamical system modeling and control. Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of {PIML} models; and 4) physics-informed digital twins. The paper is concluded with a perspective on open challenges and future research opportunities.
Nghiem, Truong↗
2020 Multiscale Microbial Dynamics Modeling Course
The 2020 Multiscale Microbial Dynamics course is adapted from the virtual 2020 Mutliscale Microbial Dynamics Summer School that was hosted by Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) science user facility located on the Pacific Northwest National Laboratory (PNNL) campus, in collaboration with the Joint Genome Institute (JGI) and the DOE Systems Biology Knowledgebase (KBase). The course course covers how to incorporate microbial metagenomic and environmental metabolite data from watershed ecosystems into metabolic and community modeling using computational frameworks, such as KBase and PFLOTRAN. The curriculum includes lectures and software and data analysis tutorials. All materials are freely accessible to the community as part of the 2020 Microbial Dynamics Summer School Organization in KBase.
54 ENVIRONMENTAL SCIENCES↗
Data Mining of Groundwater to Identify MAGs with Methane, Propane and Toluene Monooxygenases
Whole genome sequencing datasets, involving more than 600 groundwater samples, from nine countries, were analyzed to identify metagenome assembled genomes (MAGs) containing full operons for propane monooxygenase, soluble methane monooxygease, toluene monooxygenase and particulate ammonia/methane monooxygenase. The enzymes encoded by these genes are a focus of interest because of their ability to degrade common groundwater contaminants. Due to the large amount of data, sequence analyses involved more than 80 individual KBase narratives. The approach followed the KBase tutorial called "Metagenome-Assembled Genome Extraction from a Compost Microbiome Enrichment" The generated MAGs were exported from each individual narrative into separate summary KBase narratives for each monooxygenase. Three KBase narratives were generated for particulate ammonia/methane monooxygenase, due to the large number of MAGs identified.
59 BASIC BIOLOGICAL SCIENCES↗
KBase Silver Case Study: Determining Media Formulation Requirements for Isolation of Microbiome Constituents
KBase has powerful tools for extracting microbial genomes from metagenomes and performing phylogenomic analysis and metabolic modeling. These tools can be used to predict key media ingredients for isolating uncultured members of microbiomes. Essential to this process are high-quality genomes extracted from metagenomic assemblies, and Kbase has a tool for assessing the quality of genomes too. Below we identify growth factors for a myxobacteria ("slime bacteria") yet to be isolated from the rhizosphere of Miscanthus xgiganteus (hybrid of "Silvergrass"), cultivated at the Kellogg Biological Station in Michigan. Data was transferred with Globus from JGI-IMG. This narrative and the "KBase Gold Case Study: Can you find Delftia?" make up the Silver and Gold Narrative Set for teaching metagenomics concepts to students in the BIT 477/577 course at North Carolina State University. This tutorial will guide the user through the process of extracting and annotating high-quality genomes from a metagenomic data, performing phylogenomic analysis, building a metabolic model, and using these to predict nutrient requirments for growth and isolation of corresponding microbes.
59 BASIC BIOLOGICAL SCIENCES↗
Computational Optimization of A 3D Printed Collimator
This contribution describes the computational methodology behind an optimization procedure for a scattered beam collimator. The workflow includes producing a file that can be manufactured via additive methods. A conical collimator, optimized for neutron diffraction experiments in a high pressure clamp cell, is presented as an example. In such a case the scattering from the sample is much smaller than that of the pressure cell. Monte Carlo Ray tracing in MCViNE was used to model scattering from a Si powder sample and the cell. A collimator was inserted into the simulation and the number and size of channels were optimized to maximize the rejection of the parasitic signal coming from the complex sample environment. Constraints, provided by the additive manufacturing process as well as a specific neutron diffractometer, were also included in the optimization. The source code and the tutorials are available in c3dp (Islam (2019)).
74 ATOMIC AND MOLECULAR PHYSICS↗
The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending
We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit.
79 ASTRONOMY AND ASTROPHYSICS↗
Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering
Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains.
09 BIOMASS FUELS↗
Electrothermal Phenomena in Ferroelectrics
Physical properties of lead-zirconate-titanate (PZT) ceramics change according to the initial electric poling process and electrical boundary conditions. This paper reports the electrothermal, piezothermal, and piezoelectric coupling phenomena in ferroelectrics from thermodynamics viewpoints, in particular, thermal property differences between unpoled and poled PZT’s in the poling direction for open circuit and short circuit conditions. We propose a new terminology, “secondary electrothermal” coupling factor kλ, which is analogous to the electromechanical coupling factor k, relating the elastic compliances under short- and open-circuit conditions, in order to explain the fact that the short-circuit condition exhibited the larger thermal diffusivity than the open-circuit condition. On the other hand, the unpoled specimen exhibits the lowest thermal diffusivity. This tutorial paper was authored for providing comprehensive knowledge on equilibrium and time-dependent thermodynamics in ferroelectrics.
Uchino, Kenji (ORCID:0000000316517912)↗
PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python
Dynamic data assimilation offers a suite of algorithms that merge measurement data with numerical simulations to predict accurate state trajectories. Meteorological centers rely heavily on data assimilation to achieve trustworthy weather forecast. With the advance in measurement systems, as well as the reduction in sensor prices, data assimilation (DA) techniques are applicable to various fields, other than meteorology. However, beginners usually face hardships digesting the core ideas from the available sophisticated resources requiring a steep learning curve. In this tutorial, we lay out the mathematical principles behind DA with easy-to-follow Python module implementations so that this group of newcomers can quickly feel the essence of DA algorithms. We explore a series of common variational, and sequential techniques, and highlight major differences and potential extensions. We demonstrate the presented approaches using an array of fluid flow applications with varying levels of complexity.
97 MATHEMATICS AND COMPUTING↗
Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation
The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO
The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages.
79 ASTRONOMY AND ASTROPHYSICS↗
A simplified, efficient approach to hybrid wind and solar plant site optimization
Abstract. Wind plant layout optimization is a difficult, complex problem with a large number of variables and many local minima. Layout optimization only becomes more difficult with the addition of solar generation. In this paper, we propose a parameterized approach to wind and solar hybrid power plant layout optimization that greatly reduces problem dimensionality while guaranteeing that the generated layouts have a desirable regular structure. Thus far, hybrid power plant optimization research has focused on system sizing. We go beyond sizing and present a practical approach to optimizing the physical layout of a wind–solar hybrid power plant. We argue that the evolution strategy class of derivative-free optimization methods is well-suited to the parameterized hybrid layout problem, and we demonstrate how hard layout constraints (e.g., placement restrictions) can be transformed into soft constraints that are amenable to optimization using evolution strategies. Next, we present experimental results on four test sites, demonstrating the viability, reliability, and effectiveness of the parameterized evolution strategy approach for generating optimized hybrid plant layouts. Completing the tool kit for parameterized layout generation, we include a brief tutorial describing how the parameterized evolutionary approach can be inspected, understood, and debugged when applied to hybrid plant layouts.
14 SOLAR ENERGY↗