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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 sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Domain-Specific Languages and Diagram Customization for a Concurrent Engineering Environment

A major open question for advocates of Model-Based Systems Engineering (MBSE) is the question of how system and subsystem engineers will work together. The Systems Modeling Language (SysML), like any language intended for a large audience, is in tension between the desires for simplicity and for expressiveness. In order to be more expressive, many specialized language elements may be introduced, which will unfortunately make a complete understanding of the language a more daunting task. While this may be acceptable for systems modelers, it will increase the challenge of including subsystem engineers in the modeling effort. One possible answer to this situation is the use of Domain-Specific Languages (DSL), which are fully supported by the Unified Modeling Language (UML). SysML is in fact a DSL for systems engineering. The expressive power of a DSL can be enhanced through the use of diagram customization. Various domains have already developed their own schematic vocabularies. Within the space engineering community, two excellent examples are the propulsion and telecommunication subsystems. A return to simple box-and-line diagrams (e.g., the SysML Internal Block Diagram) are in many ways a step backward. In order allow subsystem engineers to contribute directly to the model, it is necessary to make a system modeling tool at least approximate in accessibility to drawing tools like Microsoft PowerPoint and Visio. The challenge is made more extreme in a concurrent engineering environment, where designs must often be drafted in an hour or two. In the case of the Jet Propulsion Laboratory's Team X concurrent design team, a subsystem is specified using a combination of PowerPoint for drawing and Excel for calculation. A pilot has been undertaken in order to meld the drawing portion and the production of master equipment lists (MELs) via a SysML authoring tool, MagicDraw. Team X currently interacts with its customers in a process of sharing presentations. There are several inefficiencies that arise from this situation. The first is that a customer team must wait two weeks to a month (which is 2-4 times the duration of most Team X studies themselves) for a finalized, detailed design description. Another is that this information must be re-entered by hand into the set of engineering artifacts and design tools that the mission concept team uses after a study is complete. Further, there is no persistent connection to Team X or institutionally shared formulation design tools and data after a given study, again reducing the direct reuse of designs created in a Team X study. This paper presents the underpinnings of subsystem DSLs as they were developed for this pilot. This includes specialized semantics for different domains as well as the process by which major categories of objects were derived in support of defining the DSLs. The feedback given to us by the domain experts on usability, along with a pilot study with the partial inclusion of these tools is also discussed.

Cole, Bjorn↗
NASA NTRS2011

Model-Based Systems Engineering for Capturing Mission Architecture System Processes with an Application Case Study - Orion Flight Test 1

Model-based Systems Engineering (MBSE) is an emerging methodology that can be leveraged to enhance many system development processes. MBSE allows for the centralization of an architecture description that would otherwise be stored in various locations and formats, thus simplifying communication among the project stakeholders, inducing commonality in representation, and expediting report generation. This paper outlines the MBSE approach taken to capture the processes of two different, but related, architectures by employing the Systems Modeling Language (SysML) as a standard for architecture description and the modeling tool MagicDraw. The overarching goal of this study was to demonstrate the effectiveness of MBSE as a means of capturing and designing a mission systems architecture. The first portion of the project focused on capturing the necessary system engineering activities that occur when designing, developing, and deploying a mission systems architecture for a space mission. The second part applies activities from the first to an application problem - the system engineering of the Orion Flight Test 1 (OFT-1) End-to-End Information System (EEIS). By modeling the activities required to create a space mission architecture and then implementing those activities in an application problem, the utility of MBSE as an approach to systems engineering can be demonstrated.

Orion Flight Test 1 (OFT-1)↗
DOE OSTI2025

Progress on the MARVEL Cybersecurity by Design Model-Based Systems Engineering Project

Formal model-based systems engineering (MBSE) combines a model, systems thinking, and systems engineering to visually depict the boundaries, context, and behavior of interconnected systems, facilitating effective design, development, and utilization of engineered systems throughout the systems engineering lifecycle. Although nuclear reactor vendors employ these tools to integrate functionality, performance, and safety, they are not yet addressing digital risk concerns introduced by use of operational technology, such as digital instrumentation and control systems. To accomplish this objective, the Microreactor Applications Research Validation and EvaLuation (MARVEL) microreactor was used as an MBSE case study. This real-world application provides a first-of-a-kind opportunity to demonstrate the benefits of integrating digital risk and cybersecurity into the MBSE design process of a nuclear reactor. This paper provides an update of the ongoing MARVEL Cyber MBSE project as it specifically relates to the integration of digital risk management and cybersecurity by design.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

CSP-Plant Modeling Guidelines and Compliance of the System Advisor Model (SAM): Preprint

Accurately modeling risks, costs, and electricity output is essential to the financing and advancement of concentrating solar power projects. To address this need, a group of CSP experts created a guideline document, titled SolarPACES Guideline for Bankable STE Yield Assessment (1). To make this information more accessible and allow stakeholders to test specific models against the recommendations, the guidelines have been condensed into a spreadsheet-based checklist. The checklist was applied to NREL's System Advisory Model (SAM) CSP modeling software, providing useful feedback to both the checklist group and the SAM development team. This study showed a strong agreement between SAM and the guidelines, demonstrated the use of the guidelines in model validation, and resulted in several recommended improvements to SAM.

14 SOLAR ENERGY↗
NASA NTRS2002

Modeling Reaction Control System Effects on Mars Odyssey

During the Mars 2001 Odyssey aerobraking mission, NASA Langley Research Center performed 6 degree of freedom (6-DOF) simulations to determine rotational motion of the spacecraft. The main objective of this study was to assess the reaction control system models and their effects on the atmospheric flight of Odyssey. Based on these models, a comparison was made between data derived from flight measurements to simulated rotational motion of the spacecraft during aerobraking at Mars. The differences between the simulation and flight derived Odyssey data were then used to adjust the aerodynamic parameters to achieve a better correlation.

Hanna, Jill L.↗
DOE OSTI2019

CSP-Plant Modeling Guidelines and Compliance of the System Advisor Model (SAM)

Accurately modeling risks, costs, and electricity output is essential to the financing and advancement of concentrating solar power projects. To address this need, a group of CSP experts created a guideline document, titled SolarPACES Guideline for Bankable STE Yield Assessment [1]. To make this information more accessible and allow stakeholders to test specific models against the recommendations, the guidelines have been condensed into a spreadsheet-based checklist. The checklist was applied to NREL's System Advisor Model (SAM) software, providing useful feedback to both the checklist group and the SAM development team. This study showed strong agreement between SAM and the guidelines, demonstrated the use of the guidelines in model validation, and resulted in several recommended improvements to SAM.

14 SOLAR ENERGY↗
DOE OSTI2013

System Advisor Model: Flat Plate Photovoltaic Performance Modeling Validation Report

The System Advisor Model (SAM) is a free software tool that performs detailed analysis of both system performance and system financing for a variety of renewable energy technologies. This report provides detailed validation of the SAM flat plate photovoltaic performance model by comparing SAM-modeled PV system generation data to actual measured production data for nine PV systems ranging from 75 kW to greater than 25 MW in size. The results show strong agreement between SAM predictions and field data, with annualized prediction error below 3% for all fixed tilt cases and below 8% for all one axis tracked cases. The analysis concludes that snow cover and system outages are the primary sources of disagreement, and other deviations resulting from seasonal biases in the irradiation models and one axis tracking issues are discussed in detail.

14 SOLAR ENERGY↗
NASA NTRS2004

Overview of the GRC Stirling Convertor System Dynamic Model

A Stirling Convertor System Dynamic Model has been developed at the Glenn Research Center for controls, dynamics, and systems development of free-piston convertor power systems. It models the Stirling cycle thermodynamics, heat flow, gas, mechanical, and mounting dynamics, the linear alternator, and the controller. The model's scope extends from the thermal energy input to thermal, mechanical dynamics, and electrical energy out, allowing one to study complex system interactions among subsystems. The model is a non-linear time-domain model containing sub-cycle dynamics, allowing it to simulate transient and dynamic phenomena that other models cannot. The model details and capability are discussed.

Lewandowski, Edward J.↗
DOE OSTI2018

Modeling Imperfect Generator Behavior in Power System Operation Models

A key component in power system operations is the use of computer models to quickly study and analyze different operating conditions and futures in an efficient manner. The output of these models are sensitive to the data used in them as well as the assumptions made during their execution. One typical assumption is that generators and load assets perfectly follow operator control signals. While this is a valid simulation assumption, generators may not always accurately follow control signals. This imperfect response of generators could impact cost and reliability metrics. This paper proposes a generator model that capture this imperfect behavior and examines its impact on production costs and reliability metrics using a steady-state power system operations model. Preliminary analysis shows that while costs remain relatively unchanged, there could be significant impacts on reliability metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2015

Model-Based Systems Engineering in Concurrent Engineering Centers

Concurrent Engineering Centers (CECs) are specialized facilities with a goal of generating and maturing engineering designs by enabling rapid design iterations. This is accomplished by co-locating a team of experts (either physically or virtually) in a room with a narrow design goal and a limited timeline of a week or less. The systems engineer uses a model of the system to capture the relevant interfaces and manage the overall architecture. A single model that integrates other design information and modeling allows the entire team to visualize the concurrent activity and identify conflicts more efficiently, potentially resulting in a systems model that will continue to be used throughout the project lifecycle. Performing systems engineering using such a system model is the definition of model-based systems engineering (MBSE); therefore, CECs evolving their approach to incorporate advances in MBSE are more successful in reducing time and cost needed to meet study goals. This paper surveys space mission CECs that are in the middle of this evolution, and the authors share their experiences in order to promote discussion within the community.

MODEL↗
DOE OSTI2017

A Phase-Field Model for the U-Pu-Zr System

Modeling nuclear materials is a multi-scale, multi-physics endeavor. Researchers are currently investigating how bulk material properties are affected by the evolution of microscopic material structures, i.e. microstructures. Development of tools that can mechanistically predict the effects of processes like diffusion and grain growth on properties like thermal conductivity will transform nuclear material design, manufacturing, and use. Intermediate-scale, i.e. mesoscale, modeling is among the most promising tools available for this task. Expedited development of mesoscale models, however, will require structured collaboration between modelers and experimentalists. Coupling these fields promises to improve both model accuracy and experiment design. With these ideas in mind, a mesoscale model of the U-Pu-Zr system is being developed. Its development will accompany a series of experiments involving irradiation of metallic fuel samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2016

Next Generation Agricultural System Data, Models and Knowledge Products: Introduction

Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30-40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a 'NextGen' study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation.

Next generation↗
DOE OSTI2018

Using both qualitative and quantitative data in parameter identification for systems biology models

Abstract In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2015

Model-Based Systems Engineering in Concurrent Engineering Centers

Concurrent Engineering Centers (CECs) are specialized facilities with a goal of generating and maturing engineering designs by enabling rapid design iterations. This is accomplished by co-locating a team of experts (either physically or virtually) in a room with a focused design goal and a limited timeline of a week or less. The systems engineer uses a model of the system to capture the relevant interfaces and manage the overall architecture. A single model that integrates other design information and modeling allows the entire team to visualize the concurrent activity and identify conflicts more efficiently, potentially resulting in a systems model that will continue to be used throughout the project lifecycle. Performing systems engineering using such a system model is the definition of model-based systems engineering (MBSE); therefore, CECs evolving their approach to incorporate advances in MBSE are more successful in reducing time and cost needed to meet study goals. This paper surveys space mission CECs that are in the middle of this evolution, and the authors share their experiences in order to promote discussion within the community.

systems engineering↗
NASA NTRS2015

Model-Based Systems Engineering in Concurrent Engineering Centers

Concurrent Engineering Centers (CECs) are specialized facilities with a goal of generating and maturing engineering designs by enabling rapid design iterations. This is accomplished by co-locating a team of experts (either physically or virtually) in a room with a narrow design goal and a limited timeline of a week or less. The systems engineer uses a model of the system to capture the relevant interfaces and manage the overall architecture. A single model that integrates other design information and modeling allows the entire team to visualize the concurrent activity and identify conflicts more efficiently, potentially resulting in a systems model that will continue to be used throughout the project lifecycle. Performing systems engineering using such a system model is the definition of model-based systems engineering (MBSE); therefore, CECs evolving their approach to incorporate advances in MBSE are more successful in reducing time and cost needed to meet study goals. This paper surveys space mission CECs that are in the middle of this evolution, and the authors share their experiences in order to promote discussion within the community.

Iwata, Curtis↗