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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Tutorial: Models and Tools at the CCMC for Space Weather Research and Education

Community Coordinated Modeling Center (CCMC) is hosting an expanding collection of space weather models covering the entire domain from the solar corona to the Earth’s lower atmosphere. The tutorial will present examples of CCMC models utilization in different research projects and demonstrate how CCMC tools and systems can be utilized in research and education. The presentation will also include highlights of International Space Weather Action Teams Initiatives and discuss opportunities for early career scientists. The presentation will be followed by hands-on demo of CCMC tools and systems.

Maria Kuznetsova↗
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↗
NASA NTRSDate not supplied

CoCoSim Tutorial: Contract-based Compositional Verification of Simulink Models

This tutorial presents CoCoSim, a verification framework for MATLAB Simulink and Stateflow models. We demonstrate CoCoSim’s architecture, designed to be compatible with Lustre-based verification tools, as well as easily extensible to other candidate backends. We focus on CoCoSim’s powerful compositional verification scheme, which allows for scalable verification through the usage of abstractions of subsystems, express ed in the form of Assume-Guarantee Contracts. We show CoCoSim’s interconnection with NASA’s Formal Requirements Tool (FRET), that enables a seamless transition between authoring and formally verifying requirements for Simulink/Stateflow models. Finally, we discuss work in progress with regards to test case generation options in CoCoSim, demonstrating the generation of MC/DC tests for Simulink artifacts.

Formal Verification↗
DOE OSTI2026

Pre-metered coating flow models with Goma 7: Workflow Tutorial

Tutorials for modeling of slot-die and slide-die coating flows with Goma 7, an open source finite element code, are presented. The tutorials cover the workflow to attaining steady state solutions for these flows, and continuation strategies for navigating the operating windows. Advanced topics of coating window prediction, automated multiparameter continuation, non-Newtonian rheology, dynamic contact line modeling, and some more solution strategies are also covered.

08 HYDROGEN↗
NASA NTRS2005

Developing a Framework for Effective Network Capacity Planning

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Yaprak, Ece↗
NASA NTRS2018

ModelE Orbits & Calendars

The GISS climate model, ModelE, [an atmosphere-ocean model] has been extended to support the simulation of climates for exoplanets. As part of this process, a more general calendaring system has been implemented that is derived from orbital parameters. The calendar design attempts to preserve the basic notions of the 4 seasons as well as the time of day. These slides provide the basic underlying assumptions for the design and detail how to configure the model to use non-Earth orbital parameters. [One of a series of videos taken of the ROCKE-3D (Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics), a 3-Dimensional General Circulation Model (GCM) exoplanet modeling tutorial sessions, May 21-22, 2018, (this slide set was prepared for Day 2, Parts 1 and 2, May 22, 2018) on YouTube (https://www.youtube.com)]

Clune, Tom↗
NASA NTRSDate not supplied

Update and Status of the Mars Climate Modeling Center at NASA Ames Research Center

NASA Ames Research Center has a long-established history in the study and numerical modeling of Mars’ atmosphere and climate. Such studies began with the late Jim Pollack and have continued under the leaderships of Bob Haberle, Jeff Hollingsworth, and now Melinda Kahre. Our group has grown to more than 10 members, including civil servants, research scientists, postdocs, and students. While we still prioritize science and the model development that supports it, we have recently enhanced efforts to make our codes and output publicly available and boost community engagement through the hosting of modeling tutorials, etc. Here our goal is to present the status of the Ames Mars GCM, our ongoing science projects, the tools we have recently made publicly available, and our plans for releases and continued community engagement.

M. A. Kahre↗
NASA NTRSDate not supplied

fmdtools Tutorial: Intro to Resilience Modelling, Simulation, and Visualization in Python With fmdtools

This workshop will cover the basics of using the fmdtools package for the simulation of hazardous scenarios for resilience simulation. The fmdtools simulation package is an open-source python toolkit for simulating the dynamic response of a system to internal and externally-driven hazardous scenarios, including faults and environmental conditions, that can be used to analyze the risks related to these hazards. Prior to the development of fmdtools, researchers had to either adapt an (often limited) propriety toolkit or develop their own design/simulation/analysis codes to develop their models of hazardous events, a significant technical burden to both (1) leveraging resilience modeling methodologies and (2) extending these methodologies with their own contributions. The fmdtools package provides a number of model constructs and simulation and analysis methods to enable the designer to focus to solely on their modeling case-study while still enabling a significant degree of model expressiveness and adaptability via Python-based model definition. This tutorial will present the setup of fmdtools and a high-level overview of its use, as well as some simple examples for understanding how to leverage its modelling, simulation, and analysis capabilities. Familiarity with jupyter notebook and basic python will be assumed.

Daniel Hulse↗
DOE OSTI2025

TUTORIAL: A new custom metabolic model for iron-oxidizing bacteria

In this tutorial narrative, we introduce a novel template developed to enable the creation of stoichiometric genome-scale metabolic models for iron-oxidizing bacteria. We demonstrate the development of this template by applying it to Sideroxydans lithotrophicus ES-1, and validate our model using transcriptomic data (Published in Zhou et al., 2022 AEM). Below, we further show that our template facilitates the modeling of mixotrophic iron-oxidizing bacteria and metagenome-assembled genomes (MAGs), by applying our template to the MAG of the mixotrophic iron oxidizer Leptothrix ochracea (Published in Tothero et al, 2024). This work represents the first instance of a generalized and adaptable template for modeling diverse iron-oxidizing microbial systems, expanding the accessibility and applicability of metabolic modeling in this field.

genome-scale model↗
DOE OSTI2024

A tutorial review of machine learning-based model predictive control methods

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.

Wu, Zhe [Department of Chemical and Biomolecular E↗
NASA NTRSDate not supplied

Tutorial: Electrified Aircraft Propulsion Approaches for Modeling and Electrical Hardware-in-the-Loop Testing

This tutorial session outlines capabilities made available by the National Aeronautics and Space Administration for testing electrified aircraft propulsion (EAP) hardware and software prior to using turbomachinery. Removing these components from the experimentation process until necessary significantly reduces the development and testing costs and safety risks. Three facilities, the NASA Electric Aircraft Testbed (NEAT) and the Hybrid Propulsion Emulation Rig (HyPER) are unique facilities that provide the following capabilities: (i) the verification of megawatt-scale electrical and electromechanical system components at altitude, (ii) the verification of EAP control systems on sub-scale representative electromechanical architectures. The importance, operation, and specifications of each facility is described with detail. Provided examples of past testing showcase the abilities of each facility. Simple and complex methods for replicating the steady state and dynamical mechanical loading on the electrical power system are discussed

Electrified Aircraft Propulsion↗
NASA NTRS2002

Nuclear model calculations and their role in space radiation research

Proper assessments of spacecraft shielding requirements and concomitant estimates of risk to spacecraft crews from energetic space radiation requires accurate, quantitative methods of characterizing the compositional changes in these radiation fields as they pass through thick absorbers. These quantitative methods are also needed for characterizing accelerator beams used in space radiobiology studies. Because of the impracticality/impossibility of measuring these altered radiation fields inside critical internal body organs of biological test specimens and humans, computational methods rather than direct measurements must be used. Since composition changes in the fields arise from nuclear interaction processes (elastic, inelastic and breakup), knowledge of the appropriate cross sections and spectra must be available. Experiments alone cannot provide the necessary cross section and secondary particle (neutron and charged particle) spectral data because of the large number of nuclear species and wide range of energies involved in space radiation research. Hence, nuclear models are needed. In this paper current methods of predicting total and absorption cross sections and secondary particle (neutrons and ions) yields and spectra for space radiation protection analyses are reviewed. Model shortcomings are discussed and future needs presented. c2002 COSPAR. Published by Elsevier Science Ltd. All right reserved.

NASA Center JSC↗
NASA NTRS2002

Determinants affecting physical activity levels in animal models

Weight control is dependent on energy balance. Reduced energy expenditure (EE) associated with decreased physical activity is suggested to be a major underlying cause in the increasing prevalence of weight gain and obesity. Therefore, a better understanding of the biological determinants involved in the regulation of physical activity is essential. To facilitate interpretation in humans, it is helpful to consider the evidence from animal studies. This review focuses on animal studies examining the biological determinants influencing activity and potential implications to human. It appears that physical activity is influenced by a number of parameters. However, regardless of the parameter involved, body weight appears to play an underlying role in the regulation of activity. Furthermore, the regulation of activity associated with body weight appears to occur only after the animal achieves a critical weight. This suggests that activity levels are a consequence rather than a contributor to weight control. However, the existence of an inverse weight-activity relationship remains inconclusive. Confounding the results are the multifactorial nature of physical activity and the lack of appropriate measuring devices. Furthermore, many determinants of body weight are closely interlocked, making it difficult to determine whether a single, combination, or interaction of factors is important for the regulation of activity. For example, diet-induced obesity, aging, lesions to the ventral medial hypothalamus, and genetics all produce hypoactivity. Providing a better understanding of the biological determinants involved in the regulation of activity has important implications for the development of strategies for the prevention of weight gain leading to obesity and subsequent morbidity and mortality in the human population.

Review↗
NASA NTRS2003

Intelligent machines in the twenty-first century: foundations of inference and inquiry

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Review↗