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At least 1,441 records · Page 80

Validation (not just verification) of Deep Space Missions

ion & Validation (V&V) is a widely recognized and critical systems engineering function. However, the often used definition 'Verification proves the design is right; validation proves it is the right design' is rather vague. And while Verification is a reasonably well standardized systems engineering process, Validation is a far more abstract concept and the rigor and scope applied to it varies widely between organizations and individuals. This is reflected in the findings in recent Mishap Reports for several NASA missions, in which shortfalls in Validation (not just Verification) were cited as root- or contributing-factors in catastrophic mission loss. Furthermore, although there is strong agreement in the community that Test is the preferred method for V&V, many people equate 'V&V' with 'Test', such that Analysis and Modeling aren't given comparable attention. Another strong motivator is a realization that the rapid growth in complexity of deep-space missions (particularly Planetary Landers and Space Observatories given their inherent unknowns) is placing greater demands on systems engineers to 'get it right' with Validation.

verification↗

Developments in REDES: The Rocket Engine Design Expert System

The Rocket Engine Design Expert System (REDES) was developed at NASA-Lewis to collect, automate, and perpetuate the existing expertise of performing a comprehensive rocket engine analysis and design. Currently, REDES uses the rigorous JANNAF methodology to analyze the performance of the thrust chamber and perform computational studies of liquid rocket engine problems. The following computer codes were included in REDES: a gas properties program named GASP; a nozzle design program named RAO; a regenerative cooling channel performance evaluation code named RTE; and the JANNAF standard liquid rocket engine performance prediction code TDK (including performance evaluation modules ODE, ODK, TDE, TDK, and BLM). Computational analyses are being conducted by REDES to provide solutions to liquid rocket engine thrust chamber problems. REDES was built in the Knowledge Engineering Environment (KEE) expert system shell and runs on a Sun 4/110 computer.

Davidian, Kenneth O.↗

Developments in REDES: The rocket engine design expert system

The Rocket Engine Design Expert System (REDES) is being developed at the NASA-Lewis to collect, automate, and perpetuate the existing expertise of performing a comprehensive rocket engine analysis and design. Currently, REDES uses the rigorous JANNAF methodology to analyze the performance of the thrust chamber and perform computational studies of liquid rocket engine problems. The following computer codes were included in REDES: a gas properties program named GASP, a nozzle design program named RAO, a regenerative cooling channel performance evaluation code named RTE, and the JANNAF standard liquid rocket engine performance prediction code TDK (including performance evaluation modules ODE, ODK, TDE, TDK, and BLM). Computational analyses are being conducted by REDES to provide solutions to liquid rocket engine thrust chamber problems. REDES is built in the Knowledge Engineering Environment (KEE) expert system shell and runs on a Sun 4/110 computer.

Davidian, Kenneth O.↗

Test Planning Approach and Lessons

As NASA began technology risk reduction activities and planning for the next generation launch vehicle under the Space Launch Initiative (SLI), now the Next Generation Launch Technology (NGLT) Program, a review of past large liquid rocket engine development programs was performed. The intent of the review was to identify any significant lessons from the development testing programs that could be applied to current and future engine development programs. Because the primary prototype engine in design at the time of this study was the Boeing-Rocketdyne RS-84, the study was slightly biased towards LOX/RP-1 liquid propellant engines. However, the significant lessons identified are universal. It is anticipated that these lessons will serve as a reference for test planning in the Engine Systems Group at Marshall Space Flight Center (MSFC). Towards the end of F-1 and J-2 engine development testing, NASA/MSFC asked Rocketdyne to review those test programs. The result was a document titled, Study to Accelerate Development by Test of a Rocket Engine (R-8099). The "intent (of this study) is to apply this thinking and learning to more efficiently develop rocket engines to high reliability with improved cost effectivenes" Additionally, several other engine programs were reviewed - such as SSME, NSTS, STME, MC-1, and RS-83- to support or refute the R-8099. R-8099 revealed two primary lessons for test planning, which were supported by the other engine development programs. First, engine development programs can benefit from arranging the test program for engine system testing as early as feasible. The best test for determining environments is at the system level, the closest to the operational flight environment. Secondly, the component testing, which tends to be elaborate, should instead be geared towards reducing risk to enable system test. Technical risk can be reduced at the component level, but the design can only be truly verified and validated after engine system testing.

Parkinson, Douglas A.↗

Instantiating R&M in the Digital Engineering Eco System

Provide an overview of OSMA related activities to help instantiate both Safety & Mission Assurance (SMA) and Reliability & Maintainability (R&M) activities as part of a Digital Engineering Eco System.

Anthony John DiVenti Jr.↗

Knowledge-based decision support for Space Station assembly sequence planning

A complete Personal Analysis Assistant (PAA) for Space Station Freedom (SSF) assembly sequence planning consists of three software components: the system infrastructure, intra-flight value added, and inter-flight value added. The system infrastructure is the substrate on which software elements providing inter-flight and intra-flight value-added functionality are built. It provides the capability for building representations of assembly sequence plans and specification of constraints and analysis options. Intra-flight value-added provides functionality that will, given the manifest for each flight, define cargo elements, place them in the National Space Transportation System (NSTS) cargo bay, compute performance measure values, and identify violated constraints. Inter-flight value-added provides functionality that will, given major milestone dates and capability requirements, determine the number and dates of required flights and develop a manifest for each flight. The current project is Phase 1 of a projected two phase program and delivers the system infrastructure. Intra- and inter-flight value-added were to be developed in Phase 2, which has not been funded. Based on experience derived from hundreds of projects conducted over the past seven years, ISX developed an Intelligent Systems Engineering (ISE) methodology that combines the methods of systems engineering and knowledge engineering to meet the special systems development requirements posed by intelligent systems, systems that blend artificial intelligence and other advanced technologies with more conventional computing technologies. The ISE methodology defines a phased program process that begins with an application assessment designed to provide a preliminary determination of the relative technical risks and payoffs associated with a potential application, and then moves through requirements analysis, system design, and development.

Source record↗

Requirement Assurance: A Verification Process

Requirement Assurance is an act of requirement verification which assures the stakeholder or customer that a product requirement has produced its "as realized product" and has been verified with conclusive evidence. Product requirement verification answers the question, "did the product meet the stated specification, performance, or design documentation?". In order to ensure the system was built correctly, the practicing system engineer must verify each product requirement using verification methods of inspection, analysis, demonstration, or test. The products of these methods are the "verification artifacts" or "closure artifacts" which are the objective evidence needed to prove the product requirements meet the verification success criteria. Institutional direction is given to the System Engineer in NPR 7123.1A NASA Systems Engineering Processes and Requirements with regards to the requirement verification process. In response, the verification methodology offered in this report meets both the institutional process and requirement verification best practices.

Alexander, Michael G.↗

Engine Structures Modeling Software System (ESMOSS)

Engine Structures Modeling Software System (ESMOSS) is the development of a specialized software system for the construction of geometric descriptive and discrete analytical models of engine parts, components, and substructures which can be transferred to finite element analysis programs such as NASTRAN. The NASA Lewis Engine Structures Program is concerned with the development of technology for the rational structural design and analysis of advanced gas turbine engines with emphasis on advanced structural analysis, structural dynamics, structural aspects of aeroelasticity, and life prediction. Fundamental and common to all of these developments is the need for geometric and analytical model descriptions at various engine assembly levels which are generated using ESMOSS.

Source record↗

A Systems Approach to Developing an Affordable Space Ground Transportation Architecture using a Commonality Approach

This paper presents a structured approach for achieving a compatible Ground System (GS) and Flight System (FS) architecture that is affordable, productive and sustainable. This paper is an extension of the paper titled "Approach to an Affordable and Productive Space Transportation System" by McCleskey et al. This paper integrates systems engineering concepts and operationally efficient propulsion system concepts into a structured framework for achieving GS and FS compatibility in the mid-term and long-term time frames. It also presents a functional and quantitative relationship for assessing system compatibility called the Architecture Complexity Index (ACI). This paper: (1) focuses on systems engineering fundamentals as it applies to improving GS and FS compatibility; (2) establishes mid-term and long-term spaceport goals; (3) presents an overview of transitioning a spaceport to an airport model; (4) establishes a framework for defining a ground system architecture; (5) presents the ACI concept; (6) demonstrates the approach by presenting a comparison of different GS architectures; and (7) presents a discussion on the benefits of using this approach with a focus on commonality.

Garcia, Jerry L.↗

How Should Life Support Be Modeled and Simulated?

Why do most space life support research groups build and investigate large models for systems simulation? The need for them seems accepted, but are we asking the right questions and solving the real problems? The modeling results leave many questions unanswered. How then should space life support be modeled and simulated? Life support system research and development uses modeling and simulation to study dynamic behavior as part of systems engineering and analysis. It is used to size material flows and buffers and plan contingent operations. A DoD sponsored study used the systems engineering approach to define a set of best practices for modeling and simulation. These best practices describe a systems engineering process of developing and validating requirements, defining and analyzing the model concept, and designing and testing the model. Other general principles for modeling and simulation are presented. Some specific additional advice includes performing a static analysis before developing a dynamic simulation, applying the mass and energy conservation laws, modeling on the appropriate system level, using simplified subsystem representations, designing the model to solve a specific problem, and testing the model on several different problems. Modeling and simulation is necessary in life support design but many problems are outside its scope.

Jones, Harry W.↗

Human Systems Integration in Practice: Constellation Lessons Learned

NASA's Constellation program provided a unique testbed for Human Systems Integration (HSI) as a fundamental element of the Systems Engineering process. Constellation was the first major program to have HSI mandated by NASA's Human Rating document. Proper HSI is critical to the success of any project that relies on humans to function as operators, maintainers, or controllers of a system. HSI improves mission, system and human performance, significantly reduces lifecycle costs, lowers risk and minimizes re-design. Successful HSI begins with sufficient project schedule dedicated to the generation of human systems requirements, but is by no means solely a requirements management process. A top-down systems engineering process that recognizes throughout the organization, human factors as a technical discipline equal to traditional engineering disciplines with authority for the overall system. This partners with a bottoms-up mechanism for human-centered design and technical issue resolution. The Constellation Human Systems Integration Group (HSIG) was a part of the Systems Engineering and Integration (SE&I) organization within the program office, and existed alongside similar groups such as Flight Performance, Environments & Constraints, and Integrated Loads, Structures and Mechanisms. While the HSIG successfully managed, via influence leadership, a down-and-in Community of Practice to facilitate technical integration and issue resolution, it lacked parallel top-down authority to drive integrated design. This presentation will discuss how HSI was applied to Constellation, the lessons learned and best practices it revealed, and recommendations to future NASA program and project managers. This presentation will discuss how Human Systems Integration (HSI) was applied to NASA's Constellation program, the lessons learned and best practices it revealed, and recommendations to future NASA program and project managers on how to accomplish this critical function.

Zumbado, Jennifer Rochlis↗

Defining a Space Mission Architectural Framework: Guiding Robotic Space Mission Design and Development at NASA Goddard Spaceflight Center

Modern Systems Engineering activities for robotic science missions face increased complexity due to evolving measurement requirements, increased collaboration amongst stakeholders and increased collaboration between human and robotic systems. While NASA has employed standard process frameworks for project management and systems engineering for years, it has not yet established an architecture framework (AF) by which its mission systems are described. While an architecture framework is not a necessary component of an organization's operations, the value of a specified AF greatly enhances an organization's ability to define systems consistently and aid in communication across project and organizational boundaries. The current effort identifies an approach to establishing a Space Mission Architecture Framework (SMAF), an AF with roots in ISO 42010 (Systems and Software Engineering-Architecture Description), NASA Procedural Requirements (NPR) 7120.5 (NASA Space Flight Program and Project Management Requirements) and 7123.1B (NASA Systems Engineering Processes and Requirements). The heart of the AF lies in its viewpoints and work products, artifacts that represent a set of information from the viewpoint of a particular stakeholder. This effort articulates the needs, goals and objectives that the SMAF addresses, as well as the approach to creating the framework and establishing the various work products. A full set of work products, sufficient to satisfy the Mission System reporting requirements of NPR 7120/7123 at Key Decision Point (KDP) A is identified in this paper.

Architecture↗

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the discovery of anomalous behaviors or degradation trends, the identification of the possible causes behind such behaviors and trends, and the prediction of their direct consequences. This paper directly targets the generation of knowledge from ER data by putting “data into context”. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by identifying first which elements of the developed MBSE elements they are referring to. This task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process “knowledge extraction” where our methods to extract knowledge from textual data. Lastly, once numeric and textual ER data elements have been processed and “understood”, we discover possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if there is a temporal relation among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 MATHEMATICS AND COMPUTING↗

Understanding and Controlling Hybrid Electric Gas Turbine Engine Transient Dynamics

The electrification of the gas turbine engine is known to increase the flexibility of aircraft architectures by enabling the generation of electrical power to distribute to other electrically based, thrust producing subsystems. It also has potential for direct performance benefits in the gas turbine engine itself, both at steady state and dynamically. Although the design focus of the gas turbine engine performance is primarily at steady state, it is often the instabilities occurring during transients that cause disequilibrium and constrain performance improvements. Instabilities arise due to the disequilibrium of the energy storage mechanisms within the traditional engine system and likewise for the electrified engine system. The primary energy storage mechanisms in the traditional system are the rotational inertia, gas path volumes, the thermal masses that make up the mechanical structure, and now, the electrical power system will provide additional contributions. Understanding the effect each of these energy storage mechanisms has on the others and controlling them appropriately allows for the suppression of state changes within the turbomachinery components to the degree that the components remain near steady state, thus reducing the disequilibrium within the system. The ability to tightly regulate the state changes of turbomachinery components, such as the compressor, minimizes the excursion of the compressor operating point from the operating line (operability), allowing for higher performing, more efficient compressor designs by decreasing the amount of stall margin needed for safe engine power level changes. Preliminary studies with the electrification of the turbine engine have shown that this is possible, and this concept can lead to design trades benefitting engine performance, weight, and volume. This paper explores in detail the energy storage mechanisms and control approaches for coordinating their state changes, and ultimately proposes that a higher performing, more efficient compressor design can result.

transient dynamics↗