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Use of Soft Computing Technologies For Rocket Engine Control

The problem to be addressed in this paper is to explore how the use of Soft Computing Technologies (SCT) could be employed to further improve overall engine system reliability and performance. Specifically, this will be presented by enhancing rocket engine control and engine health management (EHM) using SCT coupled with conventional control technologies, and sound software engineering practices used in Marshall s Flight Software Group. The principle goals are to improve software management, software development time and maintenance, processor execution, fault tolerance and mitigation, and nonlinear control in power level transitions. The intent is not to discuss any shortcomings of existing engine control and EHM methodologies, but to provide alternative design choices for control, EHM, implementation, performance, and sustaining engineering. The approaches outlined in this paper will require knowledge in the fields of rocket engine propulsion, software engineering for embedded systems, and soft computing technologies (i.e., neural networks, fuzzy logic, and Bayesian belief networks), much of which is presented in this paper. The first targeted demonstration rocket engine platform is the MC-1 (formerly FASTRAC Engine) which is simulated with hardware and software in the Marshall Avionics & Software Testbed laboratory that

Trevino, Luis C.↗

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗

Development of the Spacecraft Materials Selector Expert System

A specific knowledge base to evaluate the on-orbit performance of selected materials on spacecraft is being developed under contract to the NASA SEE program. An artificial intelligence software package, the Boeing Expert System Tool (BEST), contains an inference engine used to operate knowledge bases constructed to selectively recall and distribute information about materials performance in space applications. This same system is used to make estimates of the environmental exposures expected for a given space flight. The performance capabilities of the Spacecraft Materials Selector (SMS) knowledge base are described in this paper. A case history for a planned flight experiment on ISS is shown as an example of the use of the SMS, and capabilities and limitations of the knowledge base are discussed.

Pippin, H. G.↗

Rocket University at KSC

"Rocket University" is an exciting new initiative at Kennedy Space Center led by NASA's Engineering and Technology Directorate. This hands‐on experience has been established to develop, refine & maintain targeted flight engineering skills to enable the Agency and KSC strategic goals. Through "RocketU", KSC is developing a nimble, rapid flight engineering life cycle systems knowledge base. Ongoing activities in RocketU develop and test new technologies and potential customer systems through small scale vehicles, build and maintain flight experience through balloon and small‐scale rocket missions, and enable a revolving fresh perspective of engineers with hands on expertise back into the large scale NASA programs, providing a more experienced multi‐disciplined set of systems engineers. This overview will define the Program, highlight aspects of the training curriculum, and identify recent accomplishments and activities.

Training↗

NASA/DoD Aerospace Knowledge Diffusion Research Project. XXXIII - Technical communications practices and the use of information technologies as reported by Dutch and U.S. aerospace engineers

As part of Phase 4 of the NASA/DOD Aerospace Knowledge Diffusion Research Project, two studies were conducted that investigated the technical communications practices of Dutch and U.S. aerospace engineers and scientists. A self-administered questionnaire was distributed to aerospace engineers and scientists at the National Aerospace Laboratory (The Netherlands), and NASA Ames Research Center (U.S.), and the NASA Langley Research Center (U.S.). This paper presents responses of the Dutch and U.S. participants to selected questions about four of the seven project objectives: determining the importance of technical communications to aerospace engineering professionals, investigating the production of technical communications, examining the use and importance of computer and information technology, and exploring the use of electronic networks.

Barclay, Rebecca O.↗

NASA Software Engineering Benchmarking Study

To identify best practices for the improvement of software engineering on projects, NASA's Offices of Chief Engineer (OCE) and Safety and Mission Assurance (OSMA) formed a team led by Heather Rarick and Sally Godfrey to conduct this benchmarking study. The primary goals of the study are to identify best practices that: Improve the management and technical development of software intensive systems; Have a track record of successful deployment by aerospace industries, universities [including research and development (R&D) laboratories], and defense services, as well as NASA's own component Centers; and Identify candidate solutions for NASA's software issues. Beginning in the late fall of 2010, focus topics were chosen and interview questions were developed, based on the NASA top software challenges. Between February 2011 and November 2011, the Benchmark Team interviewed a total of 18 organizations, consisting of five NASA Centers, five industry organizations, four defense services organizations, and four university or university R and D laboratory organizations. A software assurance representative also participated in each of the interviews to focus on assurance and software safety best practices. Interviewees provided a wealth of information on each topic area that included: software policy, software acquisition, software assurance, testing, training, maintaining rigor in small projects, metrics, and use of the Capability Maturity Model Integration (CMMI) framework, as well as a number of special topics that came up in the discussions. NASA's software engineering practices compared favorably with the external organizations in most benchmark areas, but in every topic, there were ways in which NASA could improve its practices. Compared to defense services organizations and some of the industry organizations, one of NASA's notable weaknesses involved communication with contractors regarding its policies and requirements for acquired software. One of NASA's strengths was its software assurance practices, which seemed to rate well in comparison to the other organizational groups and also seemed to include a larger scope of activities. An unexpected benefit of the software benchmarking study was the identification of many opportunities for collaboration in areas including metrics, training, sharing of CMMI experiences and resources such as instructors and CMMI Lead Appraisers, and even sharing of assets such as documented processes. A further unexpected benefit of the study was the feedback on NASA practices that was received from some of the organizations interviewed. From that feedback, other potential areas where NASA could improve were highlighted, such as accuracy of software cost estimation and budgetary practices. The detailed report contains discussion of the practices noted in each of the topic areas, as well as a summary of observations and recommendations from each of the topic areas. The resulting 24 recommendations from the topic areas were then consolidated to eliminate duplication and culled into a set of 14 suggested actionable recommendations. This final set of actionable recommendations, listed below, are items that can be implemented to improve NASA's software engineering practices and to help address many of the items that were listed in the NASA top software engineering issues. 1. Develop and implement standard contract language for software procurements. 2. Advance accurate and trusted software cost estimates for both procured and in-house software and improve the capture of actual cost data to facilitate further improvements. 3. Establish a consistent set of objectives and expectations, specifically types of metrics at the Agency level, so key trends and models can be identified and used to continuously improve software processes and each software development effort. 4. Maintain the CMMI Maturity Level requirement for critical NASA projects and use CMMI to measure organizations developing software for NASA. 5.onsolidate, collect and, if needed, develop common processes principles and other assets across the Agency in order to provide more consistency in software development and acquisition practices and to reduce the overall cost of maintaining or increasing current NASA CMMI maturity levels. 6. Provide additional support for small projects that includes: (a) guidance for appropriate tailoring of requirements for small projects, (b) availability of suitable tools, including support tool set-up and training, and (c) training for small project personnel, assurance personnel and technical authorities on the acceptable options for tailoring requirements and performing assurance on small projects. 7. Develop software training classes for the more experienced software engineers using on-line training, videos, or small separate modules of training that can be accommodated as needed throughout a project. 8. Create guidelines to structure non-classroom training opportunities such as mentoring, peer reviews, lessons learned sessions, and on-the-job training. 9. Develop a set of predictive software defect data and a process for assessing software testing metric data against it. 10. Assess Agency-wide licenses for commonly used software tools. 11. Fill the knowledge gap in common software engineering practices for new hires and co-ops.12. Work through the Science, Technology, Engineering and Mathematics (STEM) program with universities in strengthening education in the use of common software engineering practices and standards. 13. Follow up this benchmark study with a deeper look into what both internal and external organizations perceive as the scope of software assurance, the value they expect to obtain from it, and the shortcomings they experience in the current practice. 14. Continue interactions with external software engineering environment through collaborations, knowledge sharing, and benchmarking.

Rarick, Heather L.↗

Executive control systems in the engineering design environment

An executive control system (ECS) is a software structure for unifying various applications codes into a comprehensive system. It provides a library of applications, a uniform access method through a cental user interface, and a data management facility. A survey of twenty-four executive control systems designed to unify various CAD/CAE applications for use in diverse engineering design environments within government and industry was conducted. The goals of this research were to establish system requirements to survey state-of-the-art architectural design approaches, and to provide an overview of the historical evolution of these systems. Foundations for design are presented and include environmental settings, system requirements, major architectural components, and a system classification scheme based on knowledge of the supported engineering domain(s). An overview of the design approaches used in developing the major architectural components of an ECS is presented with examples taken from the surveyed systems. Attention is drawn to four major areas of ECS development: interdisciplinary usage; standardization; knowledge utilization; and computer science technology transfer.

Hurst, P. W.↗

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 16: A comparison of the technical communications practices of Russian and US aerospace engineers and scientists

As part of Phase 4 of the NASA/DOD Aerospace Knowledge Diffusion Project, two studies were conducted that investigated the technical communications practices of Russian and U.S. aerospace engineers and scientists. Both studies have the same five objectives: first, to solicit the opinions of aerospace engineers and scientists regarding the importance of technical communications to their profession; second, to determine the use and production of technical communications by aerospace engineers and scientists; third, to seek their views about the appropriate content of an undergraduate course in technical communications; fourth, to determine aerospace engineers' and scientists' use of libraries, technical information centers, and on-line data bases; and fifth, to determine the use and importance of computer and information technology to them. A self-administered questionnaire was distributed to aerospace engineers and scientists at the Central Aero-Hydrodynamic Institute (TsAGI), NASA ARC, and NASA LaRC. The completion rates for the Russian and U.S. surveys were 64 and 61 percent, respectively. The responses of the Russian and U.S. participants, to selected questions, are presented in this report.

Thomas E Pinelli↗

NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 17: A comparison of the technical communication practices of Dutch and US aerospace engineers and scientists

As part of Phase 4 of the NASA/DoD Aerospace Knowledge Diffusion Research Project, two studies were conducted that investigated the technical communications practices of Dutch and U.S. aerospace engineers and scientists. Both studies have the same seven objectives: first, to solicit the opinions of aerospace engineers and scientists regarding the importance of technical communications to their profession; second, to determine the use and production of technical communications by aerospace engineers and scientists; third, to seek their views about the appropriate content of an undergraduate course in technical communications; fourth, to determine aerospace engineers' and scientists' use of libraries, technical information centers, and on-line data bases; fifth, to determine the use and importance of computer and information technology to them; sixth, to determine their use of electronic networks; and seventh, to determine their use of foreign and domestically produced technical reports. A self-administered questionnaire was distributed to aerospace engineers and scientists at the National Aerospace Laboratory (NLR), and NASA Ames Research Center, and the NASA Langley Research Center. The completion rates for the Dutch and U.S. surveys were 55 and 61 percent, respectively. Responses of the Dutch and U.S. participants to selected questions are presented.

Barclay, Rebecca O.↗

NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 29: A comparison of the technical communications practices of Japanese and US aerospace engineers and scientists

As part of Phase 4 of the NASA/DoD Aerospace Knowledge Diffusion Research Project, two studies were conducted that investigated the technical communications practices of Japanese and U.S. aerospace engineers and scientists. Both studies have the same seven objectives: first, to solicit the opinions of aerospace engineers and scientists regarding the importance of technical communications to their profession; second, to determine the use and production of technical communications by aerospace engineers and scientists; third; to seek their views about the appropriate content of an undergraduate course in technical communications; fourth, to determine aerospace engineers' and scientists' use of libraries, technical information centers, and on-line data bases; fifth, to determine the use and importance of computer and information technology to them; sixth, to determine their use of electronic networks; and seventh, to determine their use of foreign and domestically produced technical reports. A self-administered questionnaire was distributed to aerospace engineers and scientists in Japan and at the NASA Ames Research Center and the NASA Langley Research Center. The completion rates for the Japanese and U.S. surveys were 85 and 61 percent, respectively. Responses of the Japanese and U.S. participants to selected questions are presented in this report.

Pinelli, Thomas E.↗

Stirling laboratory research engine survey report

As one step in expanding the knowledge relative to and accelerating the development of Stirling engines, NASA, through the Jet Propulsion Laboratory (JPL), is sponsoring a program which will lead to a versatile Stirling Laboratory Research Engine (SLRE). An objective of this program is to lay the groundwork for a commercial version of this engine. It is important to consider, at an early stage in the engine's development, the needs of the potential users so that the SLRE can support the requirements of educators and researchers in academic, industrial, and government laboratories. For this reason, a survey was performed, the results of which are described.

Anderson, J. W.↗

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 33: Technical communications practices and the use of information technologies as reported by Dutch and US aerospace engineers

As part of Phase 4 of the NASA/DOD Aerospace Knowledge Diffusion Research Project, two studies were conducted that investigated the technical communications practices of Dutch and U.S. aerospace engineers and scientists. A self-administered questionnaire was distributed to aerospace engineers and scientists at the National Aerospace Laboratory (The Netherlands), and NASA ARC (U.S.), and NASA LaRC (U.S.). This paper presents responses of the Dutch and U.S. participants to selected questions concerning four of the seven project objectives: determining the importance of technical communications to aerospace engineering professionals, investigating the production of technical communications, examining the use and importance of computer and information technology, and exploring the use of electronic networks.

Barclay, Rebecca O.↗

NASA Engineering and Safety Center Technical Bulletins 2007-2023

An NESC Technical Bulletin captures critical knowledge in the form of new engineering information or best practices in a one-page format that have resulted from NESC independent testing, analysis, and assessments. This document contains all NESC Technical Bulletins from 2007 through 2023.

additive manufacturing, battery, braycote, capacit↗

An overview of expert systems

An expert system is defined and its basic structure is discussed. The knowledge base, the inference engine, and uses of expert systems are discussed. Architecture is considered, including choice of solution direction, reasoning in the presence of uncertainty, searching small and large search spaces, handling large search spaces by transforming them and by developing alternative or additional spaces, and dealing with time. Existing expert systems are reviewed. Tools for building such systems, construction, and knowledge acquisition and learning are discussed. Centers of research and funding sources are listed. The state-of-the-art, current problems, required research, and future trends are summarized.

Gevarter, W. B.↗

Candidate Formulary Development for Exploration Missions

An interdisciplinary team of clinician, pharmacist, and system engineer subject matter experts (SMEs) collaborated to match pharmaceutical resources to the medical conditions anticipated to occur during exploration class missions. This effort began by using a SME generated Exploration Medical Conditions List and the associated medical system capabilities necessary to treat them. Using the systems engineering software MagicDraw™ and knowledge of pharmaceutical use and efficacy in spaceflight, the team traced appropriate medications to the medical conditions. These traces were used to pilot the process for generating a candidate formulary for level of care 4 and level of care 5 medical systems. This presentation will discuss the collaborative efforts of the team as they developed the content, detail the challenges of utilizing systems engineering software to describe clinical resources, and provide an overview of how the candidate medication formulary for exploration class missions was developed. It will also discuss how the lessons learned from this pilot effort can be applied to streamline future work.

S. Kurian↗

Roles, uses, and benefits of general aviation aircraft in aerospace engineering education

Many colleges and universities throughout the United States offer outstanding programs in aerospace engineering. In addition to the fundamentals of aerodynamics, propulsion, flight dynamics, and air vehicle design, many of the best programs have in the past provided students the opportunity to design and fly airborne experiments on board various types of aircraft. Sadly, however, the number of institutions offering such 'airborne laboratories' has dwindled in recent years. As a result, opportunities for students to apply their classroom knowledge, analytical skills, and engineering judgement to the development and management of flight experiments on an actual aircraft are indeed rare. One major reason for the elimination of flight programs by some institutions, particularly the smaller colleges, is the prohibitive cost of operating and maintaining an aircraft as a flying laboratory. The purpose of this paper is to discuss simple, low-cost, relevant flight experiments that can be performed using readily available general aviation aircraft. This paper examines flight experiments that have been successfully conducted on board the NASA Lewis Research Center's T-34B aircraft, as part of the NASA/AIAA/University Flight Experiment Program for Students (NAUFEPS) and discusses how similar experiments could be inexpensively performed on other general aviation aircraft.

Odonoghue, Dennis P.↗

NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 35: The use of computer networks in aerospace engineering

This research used survey research to explore and describe the use of computer networks by aerospace engineers. The study population included 2000 randomly selected U.S. aerospace engineers and scientists who subscribed to Aerospace Engineering. A total of 950 usable questionnaires were received by the cutoff date of July 1994. Study results contribute to existing knowledge about both computer network use and the nature of engineering work and communication. We found that 74 percent of mail survey respondents personally used computer networks. Electronic mail, file transfer, and remote login were the most widely used applications. Networks were used less often than face-to-face interactions in performing work tasks, but about equally with reading and telephone conversations, and more often than mail or fax. Network use was associated with a range of technical, organizational, and personal factors: lack of compatibility across systems, cost, inadequate access and training, and unwillingness to embrace new technologies and modes of work appear to discourage network use. The greatest positive impacts from networking appear to be increases in the amount of accurate and timely information available, better exchange of ideas across organizational boundaries, and enhanced work flexibility, efficiency, and quality. Involvement with classified or proprietary data and type of organizational structure did not distinguish network users from nonusers. The findings can be used by people involved in the design and implementation of networks in engineering communities to inform the development of more effective networking systems, services, and policies.

Bishop, Ann P.↗

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗