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At least 199 records · Page 11

All sources NASA NTRS DOE OSTI

NASA NTRS2013

Model-Based Control of a Nonlinear Aircraft Engine Simulation using an Optimal Tuner Kalman Filter Approach

This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.

Control Theory↗

NASA NTRS2013

Modeling Complex Cross-Systems Software Interfaces Using SysML

The complex flight and ground systems for NASA human space exploration are designed, built, operated and managed as separate programs and projects. However, each system relies on one or more of the other systems in order to accomplish specific mission objectives, creating a complex, tightly coupled architecture. Thus, there is a fundamental need to understand how each system interacts with the other. To determine if a model-based system engineering approach could be utilized to assist with understanding the complex system interactions, the NASA Engineering and Safety Center (NESC) sponsored a task to develop an approach for performing cross-system behavior modeling. This paper presents the results of applying Model Based Systems Engineering (MBSE) principles using the System Modeling Language (SysML) to define cross-system behaviors and how they map to crosssystem software interfaces documented in system-level Interface Control Documents (ICDs).

Earth orbit↗

NASA NTRS2022

SWS TechTalk Systems Modelling for SWS Safety Demonstrator 1 (SD-1): Wildfire Response Operations

Build out a System Safety Model for System-Wide Safety’s first Technical Challenge 5 (TC5) Safety Demonstrator (SD-1), which will be a demonstration of an In-Time Aviation Safety Management System in emerging wildfire response operations. Supporting the Systems Engineer and using Model-Based Systems Engineering (MBSE) practices, languages, and tools. Summer 2022 SD1 milestones: TC5-03-01 Initial Stakeholder Engagement Concept of Operations Development milestone and the upcoming TC5-03-02 Identification of key gaps and prioritization of NASA, SWS milestone.

model-based systems engineering↗

NASA NTRS2013

Systems Engineering Interfaces: A Model Based Approach

Currently: Ops Rev developed and maintains a framework that includes interface-specific language, patterns, and Viewpoints. Ops Rev implements the framework to design MOS 2.0 and its 5 Mission Services. Implementation de-couples interfaces and instances of interaction Future: A Mission MOSE implements the approach and uses the model based artifacts for reviews. The framework extends further into the ground data layers and provides a unified methodology.

Advanced Multi-Mission Operations System (AMMOS)↗

NASA NTRS2022

UAM Airspace Research Roadmap - Rev. 1.2

The UAM Airspace research roadmap is being developed as a new System Engineering methodology leveraging Model Based System Engineering (MBSE) capabilities to help organize, integrate, and communicate NASA's UAM airspace research, with the goal of evolving UAM airspace to UML-4. It provides a basis for prioritizing and coordinating research efforts, and for integrating results that build towards NASA’s research goals. Version 1.2 is a development version of the roadmap, shared publicly to serve as a focal point for discussion and feedback leading to a future baselined version (v2.0). This version supersedes earlier publications, and will be superseded itself by later versions.

UAM↗

NASA NTRSDate not supplied

Digital Assistance for System Requirement Discovery and Analysis using Machine Learning Natural Language Processing Algorithm

NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

ATM↗

NASA NTRS2023

UAM Airspace Research Roadmap - Rev. 2.0

The UAM Airspace research roadmap is being developed as a new System Engineering methodology leveraging Model Based System Engineering (MBSE) capabilities to help organize, integrate, and communicate NASA's UAM airspace research, with the goal of evolving UAM airspace to UML-4. It provides a basis for prioritizing and coordinating research efforts, and for integrating results that build towards NASA’s research goals. Version 2.0 is a baseline version of the roadmap, shared publicly to serve as a focal point for discussion and feedback. This version supersedes earlier publications, and will be superseded itself by later versions.

UAM↗

NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

- NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. - (see https://www.nasa.gov/uam-overview/ for more information) - The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). - The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and artificial intelligence capabilities. This presentation gives an overview of the Knowledge Graph and ChatGPT applications within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

systems engineering↗

NASA NTRSDate not supplied

Systems Engineering Tutorial with Case Studies

Due to the ever-growing number of complex technical problems facing our world, a Systems Engineering approach is needed to assist in successful design, build and implementation of solutions. The interdisciplinary technical structure of current systems, technical processes representing System Design, Technical Management and Product Realization are instrumental in the development and integration of new technologies into mainstream applications. This tutorial will demonstrate the application of Systems Engineering tools to these types of problems. Learning objectives include, understanding how to develop and engineer a conceptual system; understand the importance of teams, communication, requirements definition, interface control, and focus on deliverables; useful tools and techniques for each aspect of the System Engineering process including Model Based Systems Engineering which will be touched upon lightly. Some hands-on problems will be pursued especially in requirements development which is always key to a successful project. Case studies will be presented to assist in the learning process.

Systems Engineering↗

DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS2010

Re-Engineering Complex Legacy Systems at NASA

The Flight Production Process (FPP) Re-engineering project has established a Model-Based Systems Engineering (MBSE) methodology and the technological infrastructure for the design and development of a reference, product-line architecture as well as an integrated workflow model for the Mission Operations System (MOS) for human space exploration missions at NASA Johnson Space Center. The design and architectural artifacts have been developed based on the expertise and knowledge of numerous Subject Matter Experts (SMEs). The technological infrastructure developed by the FPP Re-engineering project has enabled the structured collection and integration of this knowledge and further provides simulation and analysis capabilities for optimization purposes. A key strength of this strategy has been the judicious combination of COTS products with custom coding. The lean management approach that has led to the success of this project is based on having a strong vision for the whole lifecycle of the project and its progress over time, a goal-based design and development approach, a small team of highly specialized people in areas that are critical to the project, and an interactive approach for infusing new technologies into existing processes. This project, which has had a relatively small amount of funding, is on the cutting edge with respect to the utilization of model-based design and systems engineering. An overarching challenge that was overcome by this project was to convince upper management of the needs and merits of giving up more conventional design methodologies (such as paper-based documents and unwieldy and unstructured flow diagrams and schedules) in favor of advanced model-based systems engineering approaches.

Ruszkowski, James↗

NASA NTRSDate not supplied

Model-Based Systems Analysis and Engineering for the Sustainable Flight National Partnership

NASA is committed to supporting the U.S. climate goal of achieving net-zero greenhouse gas emissions from the aviation sector by 2050, as outlined in the U.S. 2021 Aviation Climate Action Plan. Through the Sustainable Flight National Partnership (SFNP), NASA is leading federal agencies and industry partners to accelerate the development of technologies capable of supporting the nation's aviation sustainability goals for products targeting an entry-into-service of 2035. To support the SFNP, NASA is developing a Model-Based Systems Analysis & Engineering (MBSA&E) framework which will serve to digitally integrate the work across numerous projects and demonstrations to the systems-level for relevant vision vehicle concepts. This presentation summarizes the MBSA&E motivation, development efforts, and future work.

Model Based Engineering↗

NASA NTRS2013

Shock Position Control for Mode Transition in a Turbine Based Combined Cycle Engine Inlet Model

A dual flow-path inlet for a turbine based combined cycle (TBCC) propulsion system is to be tested in order to evaluate methodologies for performing a controlled inlet mode transition. Prior to experimental testing, simulation models are used to test, debug, and validate potential control algorithms which are designed to maintain shock position during inlet disturbances. One simulation package being used for testing is the High Mach Transient Engine Cycle Code simulation, known as HiTECC. This paper discusses the development of a mode transition schedule for the HiTECC simulation that is analogous to the development of inlet performance maps. Inlet performance maps, derived through experimental means, describe the performance and operability of the inlet as the splitter closes, switching power production from the turbine engine to the Dual Mode Scram Jet. With knowledge of the operability and performance tradeoffs, a closed loop system can be designed to optimize the performance of the inlet. This paper demonstrates the design of the closed loop control system and benefit with the implementation of a Proportional-Integral controller, an H-Infinity based controller, and a disturbance observer based controller; all of which avoid inlet unstart during a mode transition with a simulated disturbance that would lead to inlet unstart without closed loop control.

Csank, Jeffrey T.↗

NASA NTRS2016

The Value of Successful MBSE Adoption

The value of successful adoption of Model Based System Engineering (MBSE) practices is hard to quantify. Most engineers and project managers look at the success in terms of cost. But there are other ways to quantify the value of MBSE and the steps necessary to achieve adoption. The Glenn Research Center (GRC) has been doing Model-Based Engineering (design, structural, etc.) for years, but the system engineering side has not. Since 2010, GRC has been moving from documents centric to MBSE/SysML. Project adoption of MBSE has been slow, but is steadily increasing in both MBSE usage and complexity of generated products. Sharing of knowledge of lessons learned in the implementation of MBSE/SysML is key for others who want to be successful. Along with GRC's implementation, NASA is working hard to increase the successful implementation of MBSE across all the other centers by developing guidelines, templates and libraries for projects to utilize. This presentation will provide insight into recent GRC and NASA adoption efforts, lessons learned and best practices.

MBSE↗

DOE OSTI2024

The Digital Engineering Vision for DOME: Facilitating Design, Deployment, and Operations [Poster]

DOME is a planned microreactor test facility at INL’s Materials and Fuels Complex. It is a complex system with several interdependent sub-systems such as the reactor (up to 20 MWth), radioactive confinement, temperature and pressure regulation system, ventilation system, etc. The engineering design process for such a system traditionally involves several documents from various sources and the system information is scattered across these documents. Digital engineering represents a paradigm shift through which systems are designed using digital models and integrated data. The digital engineering vision for DOME utilizes a model-based systems engineering (MBSE) approach. The system architecture, physical components, control logic, and verification experiments are all designed using MathWorks MATLAB and Simulink. This hierarchical model can combine data from multiple sources at various levels of abstraction. It can be used to simulate the facility’s operations and to test the system using different sets of parameters. Its capabilities can be expanded by interfacing it with high-fidelity multi-physics models, risk analysis tools, etc. The same model can evolve into a digital twin that can monitor operations and conduct predictive analysis using real-time sensor data from the facility. The eventual goal of this effort is to transform the end-to-end engineering of nuclear facilities in every phase of their lifecycle, including design, deployment, and operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

SinhaRoy_TechPresentation_2024 [Slides]

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRSDate not supplied

How ExMC Communicates a System Model to Non-Modelers

NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) Element adopted Systems Engineering (SE) principles and Model Based Systems Engineering (MBSE) tools to capture the system functions, system architecture, requirements, interfaces, and clinical capabilities for a future exploration medical system. There are many different stakeholders who may use the information in the model: systems engineers, clinicians (physicians, nurses, and pharmacists), scientists, and mission planners. Many of these stakeholders have neither access to MBSE modeling tools nor experience with SE modeling techniques. The challenge faced by ExMC SE team was how to present the content in the model to non-modelers in a way they would understand the content with limited training in MBSE and without using the modeling tool. ExMC SE team created a Hypertext Markup Language (HTML) report that shows key model content and is accessible to anyone with a browser. When creating the HTML report, the ExMC SE received stakeholder feedback on what content they wanted and how to display this content. Incorporating this feedback, the report arranges the content in a way that directs readers through the SE process taken to derive the requirements and helps them to understand the fundamental steps in an SE approach. The report includes links to source information (e.g., NASA documentation that describes levels of care) and other SE products (e.g., Concept of Operations). These links were provided to aid in the understanding of how the team created this content through a methodical SE approach. This presentation outlines the process used to develop the model, the data chosen to share with stakeholders, many of the model elements used in the report, the review process stakeholders followed, the comments received from the stakeholders, and the lessons ExMC learned through producing this HTML report.

J. Cohen↗