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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 469 records · Page 26

A model for a space shuttle safing and failure-detection expert

The safing and failure-detection expert (SAFE) is a prototype for a malfunction detection, diagnosis, and safing system for the atmospheric revitalization subsystem (ARS) in the Space Shuttle orbiter. SAFE, whose knowledge was extracted from expert-provided heuristics and documented procedures, automatically manages all phases of failure handling: detection, diagnosis, testing procedures, and recovery instructions. The SAFE architecture allows it to handle correctly sensor failures and multiple malfunctions. Since SAFE is highly interactive, it was used as a test bed for the evaluation of various advanced human-computer interface (HCI) techniques. The use of such expert systems in the next generation of space vehicles would increase their reliability and autonomy to levels not achievable before.

Zeilingold, Daphna↗

Advanced NSTS propulsion system verification study

The merits of propulsion system development testing are discussed. The existing data base of technical reports and specialists is utilized in this investigation. The study encompassed a review of all available test reports of propulsion system development testing for the Saturn stages, the Titan stages, and the Space Shuttle main propulsion system. The knowledge on propulsion system development and system testing available from specialists and managers was also 'tapped' for inclusion.

Wood, Charles↗

Ask Pete, software planning and estimation through project characterization

Ask Pete, was developed by NASA to provide a tool for integrating the estimation and planning activities for a software development effort. It incorporates COCOMO II estimating with NASA's software development practices and IV&V criteria to characterize a project. This characterization is then used to generate estimates and tailored planning documents.

Project characterization↗

NASA's Space Launch System Progress Report

Exploration beyond Earth orbit will be an enduring legacy for future generations, as it provides a platform for science and exploration that will define new knowledge and redefine known boundaries. NASA s Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is responsible for designing and developing the first exploration-class rocket since the Apollo Program s Saturn V that sent Americans to the Moon in the 1960s and 1970s. The SLS offers a flexible design that may be configured for the Orion Multi-Purpose Crew Vehicle with associated life-support equipment and provisions for long journeys or may be outfitted with a payload fairing that will accommodate flagship science instruments and a variety of high-priority experiments. Building on legacy systems, facilities, and expertise, the SLS will have an initial lift capability of 70 tonnes (t) in 2017 and will be evolvable to 130 t after 2021. While commercial launch vehicle providers service the International Space Station market, this capability will surpass all vehicles, past and present, providing the means to do entirely new missions, such as human exploration of Mars. Building on the foundation laid by over 50 years of human and scientific space flight and on the lessons learned from the Apollo, Space Shuttle, and Constellation Programs the SLS team is delivering both technical trade studies and business case analyses to ensure that the SLS architecture will be safe, affordable, reliable, and sustainable. This panel will address the planning and progress being made by NASA s SLS Program.

May, Todd A.↗

NASA Talks: Space Shuttle Columbia - Lessons Learned: Columbia Launch and Recovery

On February 25, 2026, this NASA Talk was held regarding the Space Shuttle Columbia (Columbia Launch & Recovery). Crew & Thermal System Division (EC) partnered with Kennedy Space Center (KSC) and Engineering Directorate (EA) to bring this talk to Johnson Space Center (JSC) centering on what led up to and transpired after the Space Shuttle Columbia accident. Some recovered debris was on exhibit during the talk. Retired NASA Columbia Vehicle Manager Scott Thurston presented this talk. Mr. Thurston brings a wealth of knowledge from his mission experience. This talk was timely and a solemn reminder of the critical importance of diligence, safety, and excellence in our work. EA Director Julie Kramer White welcomed the 400+ center-wide in-person audience, and EC Division Chief Rubik Sheth introduced the speakers. This event was held in the JSC Teague Auditorium, recorded, and executed by the EC in-house STAR Productions team. This record includes an mp4 video presentation with a run time of 36 min. 20 sec.; in color; with sound.

NASA Talks↗

Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)

Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.

Pattern Recognition↗

Second Generation RLV Space Vehicle Concept

NASA has a long history of conducting development programs and projects in a consistent fashion. Systems Engineering within those programs and projects has also followed a given method outlined by such documents as the NASA Systems Engineering Handbook. The relatively new NASA Space Launch Initiative (SLI) is taking a new approach to developing a space vehicle, with innovative management methods as well as new Systems Engineering processes. With the program less than a year into its life cycle, the efficacy of these new processes has yet to be proven or disproven. At $776M for phase 1, SLI represents a major portion of the NASA focus; however, the new processes being incorporated are not reflected in the training provided by NASA to its engineers. The NASA Academy of Program and Project Leadership (APPL) offers core classes in program and project management and systems engineering to NASA employees with the purpose of creating a "knowledge community where ideas, skills, and experiences are exchanged to increase each other's capacity for strong leadership". The SLI program is, in one sense, a combination of a conceptual design program and a technology program. The program as a whole doesn't map into the generic systems engineering project cycle as currently, and for some time, taught. For example, the NASA APPL Systems Engineering training course teaches that the "first step in developing an architecture is to define the external boundaries of the system", which will require definition of the interfaces with other systems and the next step will be to "define all the components that make up the next lower level of the system hierarchy" where fundamental requirements are allocated to each component. Whereas, the SLI technology risk reduction approach develops architecture subsystem technologies prior to developing architectures. The higher level architecture requirements are not allowed to fully develop and undergo decomposition and allocation down to the subsystems before the subsystems must develop allocated requirements based on the highest level of requirements. In the vernacular of the project cycles prior to the mid 1990's, the architecture definition portion of the program appears to be at a generic Phase A stage, while the subsystems are operating at Phase B. Even the management structure of the SLI program is innovative in its approach to Systems Engineering and is not reflected in the APPL training modules. The SLI program has established a Systems Engineering office as an office separate from the architecture development or the subsystem technology development, while that office does have representatives within these other offices. The distributed resources of the Systems Engineering Office are co-located with the respective Project Offices. This template is intended to provide systems engineering as an integrated function at the Program Level. the program management of SLI and the MAT agree that "program/project managers and the systems engineering team must work closely together towards the single objective of delivering quality products that meet the customer needs". This paper will explore the differences between the methods being taught by NASA, which represent decades of ideas, and those currently in practice in SLI. Time will tell if the innovation employed by SLI will prove to be the model of the future. For now, it is suggested that the training of the present exercise the flexibility of recognizing the new processes employed by a major new NASA program.

Bailey, Michelle↗

Guidelines for development of NASA (National Aeronautics and Space Administration) computer security training programs

The report presents guidance for the NASA Computer Security Program Manager and the NASA Center Computer Security Officials as they develop training requirements and implement computer security training programs. NASA audiences are categorized based on the computer security knowledge required to accomplish identified job functions. Training requirements, in terms of training subject areas, are presented for both computer security program management personnel and computer resource providers and users. Sources of computer security training are identified.

Tompkins, F. G.↗

A Model-Based Prognostics Approach Applied to Pneumatic Valves

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

Daigle, Matthew J.↗

Performance measurement: A tool for program control

Performance measurement is a management tool for planning, monitoring, and controlling as aspects of program and project management--cost, schedule, and technical requirements. It is a means (concept and approach) to a desired end (effective program planning and control). To reach the desired end, however, performance measurement must be applied and used appropriately, with full knowledge and recognition of its power and of its limitations--what it can and cannot do for the project manager. What is the potential of this management tool? What does performance measurement do that a traditional plan vs. actual technique cannot do? Performance measurement provides an improvement over the customary comparison of how much money was spent (actual cost) vs. how much was planned to be spent based on a schedule of activities (work planned). This commonly used plan vs. actual comparison does not allow one to know from the numerical data if the actual cost incurred was for work intended to be done.

Abell, Nancy↗

Development and Implementation of A Small Satellite Systems Engineering Webinar Series: A Collaboration Between the United Nations Office for Outer Space Affairs and the National Aeronautics and Space Administration

The United Nations Office for Outer Space Affairs (UNOOSA) in collaboration with the National Aeronautics and Space Administration (NASA) established a webinar series on NASA systems engineering standards and practices for the purpose of sharing knowledge in this area. UNOOSA’s “Access to Space for All” initiative provides capacity-building opportunities in space science, technology, and space applications for United Nations member states. Due to the cooperation among established space actors, the United Nations, and emerging space entities, the initiative enables students from developing countries from all over the world to carry out projects using technologies and space applications. Through NASA’s Small Spacecraft Systems Virtual Institute (S3VI), the four-part webinar series was designed to cover basic systems engineering and project management skills that are fundamental to planning, developing, and implementing an experiment or a space project and that serve as critical knowledge for those engaged in space activities, whether as a designer, builder, or manager of space infrastructure and services. The first series of webinars was convened over the course of November 2023 through February 2024.

Systems Engineering↗

Using CLIPS to represent knowledge in a VR simulation

Virtual reality (VR) is an exciting use of advanced hardware and software technologies to achieve an immersive simulation. Until recently, the majority of virtual environments were merely 'fly-throughs' in which a user could freely explore a 3-dimensional world or a visualized dataset. Now that the underlying technologies are reaching a level of maturity, programmers are seeking ways to increase the complexity and interactivity of immersive simulations. In most cases, interactivity in a virtual environment can be specified in the form 'whenever such-and-such happens to object X, it reacts in the following manner.' CLIPS and COOL provide a simple and elegant framework for representing this knowledge-base in an efficient manner that can be extended incrementally. The complexity of a detailed simulation becomes more manageable when the control flow is governed by CLIPS' rule-based inference engine as opposed to by traditional procedural mechanisms. Examples in this paper will illustrate an effective way to represent VR information in CLIPS, and to tie this knowledge base to the input and output C routines of a typical virtual environment.

Engelberg, Mark L.↗

Advanced Data Format (ADF) Software Library and Users Guide

The "CFD General Notation System" (CGNS) consists of a collection of conventions, and conforming software, for the storage and retrieval of Computational Fluid Dynamics (CFD) data. It facilitates the exchange of data between sites and applications, and helps stabilize the archiving of aerodynamic data. This effort was initiated in order to streamline the procedures in exchanging data and software between NASA and its customers, but the goal is to develop CGNS into a National Standard for the exchange of aerodynamic data. The CGNS development team is comprised of members from Boeing Commercial. Airplane Group, NASA-Ames, NASA-Langley, NASA-Lewis, McDonnell-Douglas Corporation (now Boeing-St. Louis), Air Force-Wright Lab., and ICEM-CFD Engineering. The elements of CGNS address all activities associated with the storage of data on external media and its movement to and from application programs. These elements include: 1) The Advanced Data Format (ADF) Database manager, consisting of both a file format specification and its 1/0 software, which handles the actual reading and writing of data from and to external storage media; 2) The Standard Interface Data Structures (SIDS), which specify the intellectual content of CFD data and the conventions governing naming and terminology; 3) The SIDS-to-ADF File Mapping conventions, which specify the exact location where the CFD data defined by the SIDS is to be stored within the ADF file(s); and 4) The CGNS Mid-level Library, which provides CFD-knowledgeable routines suitable for direct installation into application codes. The ADF is a generic database manager with minimal intrinsic capability. It was written for the purpose of storing large numerical datasets in an efficient, platform independent manner. To be effective, it must be used in conjunction with external agreements on how the data will be organized within the ADF database such defined by the SIDS. There are currently 34 user callable functions that comprise the ADF Core library and are described in the Users Guide. The library is written in C, but each function has a FORTRAN counterpart.

Smith, Matthew↗

Expert systems applied to spacecraft fire safety

Expert systems are problem-solving programs that combine a knowledge base and a reasoning mechanism to simulate a human expert. The development of an expert system to manage fire safety in spacecraft, in particular the NASA Space Station Freedom, is difficult but clearly advantageous in the long-term. Some needs in low-gravity flammability characteristics, ventilating-flow effects, fire detection, fire extinguishment, and decision models, all necessary to establish the knowledge base for an expert system, are discussed.

Smith, Richard L.↗

Technical facilities catalog, volume II

This Technical Facilities Catalog has been developed in response to an increasing demand for general and specific information on NASA technical facilities. The Catalog is intended to: a)provide NASA management, facilities managers, program and project managers, individual research engineers and scientists, and other agencies of the Government with knowledge of existing NASA research, development, test and evaluation facilities and b)provide the reader with a basic concept of the capabilities and functions of the wide variety of NASA technical facilities and the technological areas they support.

Wind tunnel↗

An approach to combining heuristic and qualitative reasoning in an expert system

An approach to combining the heuristic reasoning from shallow knowledge and the qualitative reasoning from deep knowledge is described. The shallow knowledge is represented in production rules and under the direct control of the inference engine. The deep knowledge is represented in frames, which may be put in a relational DataBase Management System. This approach takes advantage of both reasoning schemes and results in improved efficiency as well as expanded problem solving ability.

Jiang, Wei-Si↗

Analyst-centered models for systems design, analysis, and development

Much has been written about the possible use of Expert Systems (ES) technology for strategic defense system applications, particularly for battle management algorithms and mission planning. It is proposed that ES (or more accurately, Knowledge Based System (KBS)) technology can be used in situations for which no human expert exists, namely to create design and analysis environments that allow an analyst to rapidly pose many different possible problem resolutions in game like fashion and to then work through the solution space in search of the optimal solution. Portions of such an environment exist for expensive AI hardware/software combinations such as the Xerox LOOPS and Intellicorp KEE systems. Efforts are discussed to build an analyst centered model (ACM) using an ES programming environment, ExperOPS5 for a simple missile system tradeoff study. By analyst centered, it is meant that the focus of learning is for the benefit of the analyst, not the model. The model's environment allows the analyst to pose a variety of what if questions without resorting to programming changes. Although not an ES per se, the ACM would allow for a design and analysis environment that is much superior to that of current technologies.

Bukley, A. P.↗

Manual and computer-aided materials selection for industrial production: An exercise in decision making

Students are introduced to methods and concepts for systematic selection and evaluation of materials which are to be used to manufacture specific products in industry. For this laboratory exercise, students are asked to work in groups to identify and describe a product, then to proceed through the process to select a list of three candidates to make the item from. The exercise draws on knowledge of mechanical, physical, and chemical properties, common materials test techniques, and resource management skills in finding and assessing property data. A very important part of the exercise is the students' introduction to decision making algorithms, and learning how to apply them to a complex decision making process.

Bates, Seth P.↗