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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Towards a Decision Support System for Space Flight Operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The discrepancy of the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the three increments of this vision. These increments include (1) hardware and software system components and interfaces with the NASA ground system, (2) uncertainty management and (3) re-planning and automated execution. Each of these increments provide value independently; but some may also enable building of a subsequent increment.

Meshkat, Leila↗
NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗
NASA NTRSDate not supplied

2023 Artemis Crew Health and Performance System Model Development

While the NASA Human Research Program (HRP) utilizes a Crew Health and Performance (CHP) System to represent all the Agency’s efforts to ensure the health and performance of NASA astronauts, there is no shared mental model of a CHP system at NASA. Some groups may consider a CHP system to be only a medical kit, while others may not be using the concept at all. To facilitate the integration of functions and capabilities to ensure astronaut health and performance during vehicle development, HRP has proposed a CHP Shared Mental Model derived from the NASA Human Health, Medical, and Performance Spaceflight Standards (NASA-STD-3001 Vol.1/Vol.2). [1] Even though many vehicle, ground, and communication systems as well as mission operations are modeled for the Artemis Campaigns, no mission level CHP system model was created to achieve the intent of the HRP CHP Shared Mental Model. The lack of this model renders it difficult to visualize and understand how the many programs work together to provide the necessary cross program functions and capabilities to ensure the health and performance of the crew throughout an Artemis mission. For this purpose, the Exploration Medical Capability (ExMC) element of HRP developed a CHP system model for the Artemis III and IV missions to provide a view of how each program contributes to and interacts with the overall CHP system. To develop the 2023 Artemis CHP system model, ExMC leveraged existing data and models from the Moon to Mars Program Office, the Office of the Chief Health and Medical Officer (OCHMO) and the Orion, Gateway, Extravehicular Activity and Human Surface Mobility (EHP) and Human Landing System (HLS) programs. By using a Model-Based Systems Engineering (MBSE) approach, existing requirements, functions, and concepts of operations were combined to create a single system model focused on representing CHP from the launch to the return to Earth segments of the Artemis III and IV missions. Additionally, by incorporating the HRP Systems Platform for Aggregating and Relating Capabilities, or SPARC tool, the data from the programs was also related back to the 2nd volume of the NASA Human Health, Medical, and Performance Spaceflight Standard (NASA-STD-3001, Vol.2) and the human system risks identified by the Human System Risk Board (HSRB). The first version of the 2023 Artemis CHP system model was baselined in Fall of 2023 after the model was demonstrated to be a potentially useful tool for systems engineers integrating CHP capabilities in vehicle development as well as members of the Health and Medical Technical Authority providing oversight of those programs. The model may also be useful to any stakeholder of astronaut health and performance by providing insights on how an Artemis mission satisfies the NASA Human Health, Medical, and Performance Spaceflight Standards as well as how they mitigate the HSRB Human System Risks. This presentation highlights how the model was developed and the possible benefits of the model. [1] NASA HRP (2022), Crew Health and Performance System Whitepaper

Systems engineering↗
NASA NTRS2024

2023 Artemis Crew Health and Performance (CHP) System Model Development

While the NASA Human Research Program (HRP) utilizes a Crew Health and Performance (CHP) System to represent all the Agency’s efforts to ensure the health and performance of NASA astronauts, there is no shared mental model of a CHP system at NASA. Some groups may consider a CHP system to be only a medical kit, while others may not be using the concept at all. To facilitate the integration of functions and capabilities to ensure astronaut health and performance during vehicle development, HRP has proposed a CHP Shared Mental Model derived from the NASA Human Health, Medical, and Performance Spaceflight Standards (NASA-STD-3001 Vol.1/Vol.2). [1] Even though many vehicle, ground, and communication systems as well as mission operations are modeled for the Artemis Campaigns, no mission level CHP system model was created to achieve the intent of the HRP CHP Shared Mental Model. The lack of this model renders it difficult to visualize and understand how the many programs work together to provide the necessary cross program functions and capabilities to ensure the health and performance of the crew throughout an Artemis mission. For this purpose, the Exploration Medical Capability (ExMC) element of HRP developed a CHP system model for the Artemis III and IV missions to provide a view of how each program contributes to and interacts with the overall CHP system. To develop the 2023 Artemis CHP system model, ExMC leveraged existing data and models from the Moon to Mars Program Office, the Office of the Chief Health and Medical Officer (OCHMO) and the Orion, Gateway, Extravehicular Activity and Human Surface Mobility (EHP) and Human Landing System (HLS) programs. By using a Model-Based Systems Engineering (MBSE) approach, existing requirements, functions, and concepts of operations were combined to create a single system model focused on representing CHP from the launch to the return to Earth segments of the Artemis III and IV missions. Additionally, by incorporating the HRP Systems Platform for Aggregating and Relating Capabilities, or SPARC tool, the data from the programs was also related back to the 2nd volume of the NASA Human Health, Medical, and Performance Spaceflight Standard (NASA-STD-3001, Vol.2) and the human system risks identified by the Human System Risk Board (HSRB). The first version of the 2023 Artemis CHP system model was baselined in Fall of 2023 after the model was demonstrated to be a potentially useful tool for systems engineers integrating CHP capabilities in vehicle development as well as members of the Health and Medical Technical Authority providing oversight of those programs. The model may also be useful to any stakeholder of astronaut health and performance by providing insights on how an Artemis mission satisfies the NASA Human Health, Medical, and Performance Spaceflight Standards as well as how they mitigate the HSRB Human System Risks. This presentation highlights how the model was developed and the possible benefits of the model. [1] NASA HRP (2022), Crew Health and Performance System Whitepaper

Systems engineering↗
NASA NTRS2016

Effort to Accelerate MBSE Adoption and Usage at JSC

This paper describes the authors' experience in adopting Model Based System Engineering (MBSE) at the NASA/Johnson Space Center (JSC). Since 2009, NASA/JSC has been applying MBSE using the Systems Modeling Language (SysML) to a number of advanced projects. Models integrate views of the system from multiple perspectives, capturing the system design information for multiple stakeholders. This method has allowed engineers to better control changes, improve traceability from requirements to design and manage the numerous interactions between components. As the project progresses, the models become the official source of information and used by multiple stakeholders. Three major types of challenges that hamper the adoption of the MBSE technology are described. These challenges are addressed by a multipronged approach that includes educating the main stakeholders, implementing an organizational infrastructure that supports the adoption effort, defining a set of modeling guidelines to help engineers in their modeling effort, providing a toolset that support the generation of valuable products, and providing a library of reusable models. JSC project case studies are presented to illustrate how the proposed approach has been successfully applied.

Wang, Lui↗
NASA NTRS2012

Model-Based Verification and Validation of Spacecraft Avionics

Our simulation was able to mimic the results of 30 tests on the actual hardware. This shows that simulations have the potential to enable early design validation - well before actual hardware exists. Although simulations focused around data processing procedures at subsystem and device level, they can also be applied to system level analysis to simulate mission scenarios and consumable tracking (e.g. power, propellant, etc.). Simulation engine plug-in developments are continually improving the product, but handling time for time-sensitive operations (like those of the remote engineering unit and bus controller) can be cumbersome.

Systems Modeling Language (SysML↗
NASA NTRS2018

A Model-based Approach to Developing the Concept of Operations for Potential Mars Sample Return

Mars Sample Return (MSR) is a proposed multi-agency effort that would return soil and rock samples from the surface of Mars to Earth. Both the complexity of the potential missions, as well as the involvement of multiple geographically distributed organizations, presents a challenge from an information management perspective. In this paper, a Model-based Systems Engineering (MBSE) approach to developing the Concept of Operations of a potential Mars Sample Return effort using the System Modeling Language(SysML) is presented.

Muirhead, Brian↗
NASA NTRSDate not supplied

Developing the Foundations of an Exploration Class Medical System: Bridging the Capability Gap between LEO and Mars

NASA's overarching mission is to drive advances in science, technology, aeronautics, and space exploration to enhance knowledge, education, innovation, economic vitality, and Earth stewardship. Its Artemis Program encompasses the next steps in human space exploration, critical elements of which include the Orion vehicle and Gateway outpost in lunar orbit. Artemis is the first "exploration class" space travel system with the stated mission to establish sustainable exploration of the lunar surface and serve as a bridge to future human exploration of Mars. Success requires understanding both the commonalities and the differences between our experience to date, predominantly in Low-Earth-Orbit, and the challenges expected in long-duration exploration missions. In addition, these missions will incur significant constraints on both vehicle and human systems. To that end, NASA Exploration Medical Capability (ExMC) element clinicians, engineers, and scientists are developing the "foundation" for a fully vehicle-integrated exploration-class medical system using model-based systems engineering to trace and leverage clinical and engineering content to expand and refine the fidelity and confidence in the medical system itself.

MEDICAL SYSTEMS↗
NASA NTRS1987

Studies and analyses of the space shuttle main engine. Failure information propagation model data base and software

The failure information propagation model (FIPM) data base was developed to store and manipulate the large amount of information anticipated for the various Space Shuttle Main Engine (SSME) FIPMs. The organization and structure of the FIPM data base is described, including a summary of the data fields and key attributes associated with each FIPM data file. The menu-driven software developed to facilitate and control the entry, modification, and listing of data base records is also discussed. The transfer of the FIPM data base and software to the NASA Marshall Space Flight Center is described. Complete listings of all of the data base definition commands and software procedures are included in the appendixes.

Tischer, A. E.↗
NASA NTRS2017

Navigating the Deployment and Downlink Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center (GSFC) is developing a pre-Phase A tool called Tradespace Analysis Tool for Constellations (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper will describe the different types of constellations that TAT-C’s Tradespace Search Iterator is capable of enumerating (homogeneous Walker, heterogeneous Walker, precessing type, ad-hoc) and their impact on key performance metrics such as revisit statistics, time to global access and coverage. We will also discuss the ability to simulate phased deployment of the given constellations, as a function of launch availabilities and/or vehicle capability, and show the impact on performance. All performance metrics are calculated by the Data Reduction and Metric Computation module within TAT-C, which issues specific requests and processes results from the Orbit and Coverage module. Our TSI is also capable of generating tradespaces for downlinking imaging data from the constellation, based on permutations of available ground station networks - known (default) or customized (by the user). We will show the impact of changing ground station options for any given constellation, on data latency and required communication bandwidth, which in turn determines the responsiveness of the space system.

Nag, Sreeja↗
NASA NTRSDate not supplied

System-Level Model-Based Risk Determination for Lunar Mission Design

Recent work has shown that human activities on the lunar surface have the potential to impact not only surface infrastructure, but also have long-term repercussions to lunar orbit infrastructure that is directly proportional to the frequency and scale of landings and impacts. Those assets that are present within the lunar environment, whether on the surface or in orbit, are thus not entirely isolated from one another but contribute to the overall induced environment. With that in mind, this project endeavors to model that system using Model Based Systems Engineering (MBSE), employing previously developed mathematical methodology. The product from this work is a flexible tool with which a user may model any number of assets or events and determine how the dust and debris generated by those events effects mission operations and overall projected.

Matthew Wittal↗
NASA NTRS2017

Extension of MBSE for Project Programmatics Management on the Asteroid Redirect Robotic Mission

Model-based Systems Engineering can be employed beyond management of the technical architecture development of a system to also manage the programmatics associated with Systems Engineering activities of a project. On NASA’s Asteroid Redirect Robotic Mission, MBSE has been successfully employed to manage, generate, and interact with the documentation-based deliverables associated with System Engineering activities. This has been involved in defining and tracking project document, milestone, and personnel metadata via the same modeling framework used for the technical architecture management. Additionally, it has focused on improving overall user experiences through linkage of documentation to technical content in the system model, automation of manually intensive tasks, and others stakeholderoriented features.

Mozafari, Tanaz↗
NASA NTRS2014

A Model-Based Approach to Developing Your Mission Operations System

Model-Based System Engineering (MBSE) is an increasingly popular methodology for designing complex engineering systems. As the use of MBSE has grown, it has begun to be applied to systems that are less hardware-based and more people- and process-based. We describe our approach to incorporating MBSE as a way to streamline development, and how to build a model consisting of core resources, such as requirements and interfaces, that can be adapted and used by new and upcoming projects. By comparing traditional Mission Operations System (MOS) system engineering with an MOS designed via a model, we will demonstrate the benefits to be obtained by incorporating MBSE in system engineering design processes.

mos↗
NASA NTRS2006

Rate-Based Model Predictive Control of Turbofan Engine Clearance

An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

DeCastro, Jonathan A.↗
NASA NTRS2016

Streamlining the Design Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center is developing a pre-Phase A tool called "Trade-space Analysis Tool for Constellations" (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper focuses on the tradespace search and how it can be streamlined by combining physical rules, as well as well-designed orbit and coverage computations, thus yielding significant speed-ups. Two use cases are shown as representative examples of the utility of TAT-C generated trades, and results are preliminarily validated against AGI's Systems Tool Kit.

Science Data Processing↗
NASA NTRS2002

The New NASA Orbital Debris Engineering Model ORDEM2000

The NASA Orbital Debris Program Office at Johnson Space Center has developed a new computer-based orbital debris engineering model, ORDEM2000, which describes the orbital debris environment in the low Earth orbit region between 200 and 2000 km altitude. The model is appropriate for those engineering solutions requiring knowledge and estimates of the orbital debris environment (debris spatial density, flux, etc.). ORDEM2000 can also be used as a benchmark for ground-based debris measurements and observations. We incorporated a large set of observational data, covering the object size range from 10 mm to 10 m, into the ORDEM2000 debris database, utilizing a maximum likelihood estimator to convert observations into debris population probability distribution functions. These functions then form the basis of debris populations. We developed a finite element model to process the debris populations to form the debris environment. A more capable input and output structure and a user-friendly graphical user interface are also implemented in the model. ORDEM2000 has been subjected to a significant verification and validation effort. This document describes ORDEM2000, which supersedes the previous model, ORDEM96. The availability of new sensor and in situ data, as well as new analytical techniques, has enabled the construction of this new model. Section 1 describes the general requirements and scope of an engineering model. Data analyses and the theoretical formulation of the model are described in Sections 2 and 3. Section 4 describes the verification and validation effort and the sensitivity and uncertainty analyses. Finally, Section 5 describes the graphical user interface, software installation, and test cases for the user.

Liou, Jer-Chyi↗