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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

COBRA System Engineering Processes to Achieve SLI Strategic Goals

The COBRA Prototype Main Engine Development Project was an endeavor conducted as a joint venture between Pratt & Whitney and Aerojet to conduct risk reduction in LOX/LH2 main engine technology for the NASA Space Launch Initiative (SLI). During the seventeen months of the project (April 2001 to September 2002), approximately seventy reviews were conducted, beginning with the Engine Systems Requirements Review (SRR) and ending with the Engine Systems Interim Design Review (IDR). This paper discusses some of the system engineering practices used to support the reviews and the overall engine development effort.

Ballard, Richard O.↗
NASA NTRS1993

A systems engineering initiative for NASA's space communications

In addition to but separate from the Red and Blue Teams commissioned by the NASA Administrator, NASA's Associate Administrator for Space Communications commissioned a Blue Team to review the Office of Space Communications (Code O) Core Program and determine how the program could be conducted faster, better, and cheaper, without compromising safety. Since there was no corresponding Red Team for the Code O Blue Team, the Blue Team assumed a Red Team independent attitude and challenged the status quo. The Blue Team process and results are summarized. The Associate Administrator for Space Communications subsequently convened a special management session to discuss the significance and implications of the Blue Team's report and to lay the groundwork and teamwork for the next steps, including the transition from engineering systems to systems engineering. The methodology and progress toward realizing the Code O Family vision and accomplishing the systems engineering initiative for NASA's space communications are presented.

Hornstein, Rhoda S.↗
NASA NTRS1993

The systems engineering overview and process (from the Systems Engineering Management Guide, 1990)

The past several decades have seen the rise of large, highly interactive systems that are on the forward edge of technology. As a result of this growth and the increased usage of digital systems (computers and software), the concept of systems engineering has gained increasing attention. Some of this attention is no doubt due to large program failures which possibly could have been avoided, or at least mitigated, through the use of systems engineering principles. The complexity of modern day weapon systems requires conscious application of systems engineering concepts to ensure producible, operable and supportable systems that satisfy mission requirements. Although many authors have traced the roots of systems engineering to earlier dates, the initial formalization of the systems engineering process for military development began to surface in the mid-1950s on the ballistic missile programs. These early ballistic missile development programs marked the emergence of engineering discipline 'specialists' which has since continued to grow. Each of these specialties not only has a need to take data from the overall development process, but also to supply data, in the form of requirements and analysis results, to the process. A number of technical instructions, military standards and specifications, and manuals were developed as a result of these development programs. In particular, MILSTD-499 was issued in 1969 to assist both government and contractor personnel in defining the systems engineering effort in support of defense acquisition programs. This standard was updated to MIL-STD499A in 1974, and formed the foundation for current application of systems engineering principles to military development programs.

Source record↗
NASA NTRS2005

Engineering Lessons Learned and Systems Engineering Applications

Systems Engineering is fundamental to good engineering, which in turn depends on the integration and application of engineering lessons learned and technical standards. Thus, good Systems Engineering also depends on systems engineering lessons learned from within the aerospace industry being documented and applied. About ten percent of the engineering lessons learned documented in the NASA Lessons Learned Information System are directly related to Systems Engineering. A key issue associated with lessons learned datasets is the communication and incorporation of this information into engineering processes. Systems Engineering has been defined (EINIS-632) as "an interdisciplinary approach encompassing the entire technical effort to evolve and verify an integrated and life-cycle balanced set of system people, product, and process solutions that satisfy customer needs". Designing reliable space-based systems has always been a goal for NASA, and many painful lessons have been learned along the way. One of the continuing functions of a system engineer is to compile development and operations "lessons learned" documents and ensure their integration into future systems development activities. They can produce insights and information for risk identification identification and characterization. on a new project. Lessons learned files from previous projects are especially valuable in risk

Gill, Paul S.↗
NASA NTRS2005

Engineering Lessons Learned and Systems Engineering Applications

Systems Engineering is fundamental to good engineering, which in turn depends on the integration and application of engineering lessons learned. Thus, good Systems Engineering also depends on systems engineering lessons learned from within the aerospace industry being documented and applied. About ten percent of the engineering lessons learned documented in the NASA Lessons Learned Information System are directly related to Systems Engineering. A key issue associated with lessons learned datasets is the communication and incorporation of this information into engineering processes. As part of the NASA Technical Standards Program activities, engineering lessons learned datasets have been identified from a number of sources. These are being searched and screened for those having a relation to Technical Standards. This paper will address some of these Systems Engineering Lessons Learned and how they are being related to Technical Standards within the NASA Technical Standards Program, including linking to the Agency's Interactive Engineering Discipline Training Courses and the life cycle for a flight vehicle development program.

Gill, Paul S.↗
NASA NTRS1987

A systems engineering view of the human in space

A model of the human as an 'engineered' system provides a starting point for determining human requirements and performance on an equivalent basis with technological systems. The human as an engineered system with performance requirements is defined to consist of four subsystems: cognitive, psychological, biomechanical, and biomedical. It is suggested that the treatment of the psychological subsystem as one that modulates the efficiency and quality of human performance offers a particular approach for examining and characterizing psychological effects.

Anderson, John L.↗
NASA NTRS2014

System Engineering of Photonic Systems for Space Application

The application of photonics in space systems requires tight integration with the spacecraft systems to ensure accurate operation. This requires some detailed and specific system engineering to properly incorporate the photonics into the spacecraft architecture and to guide the spacecraft architecture in supporting the photonics devices. Recent research in product focused, elegant system engineering has led to a system approach which provides a robust approach to this integration. Focusing on the mission application and the integration of the spacecraft system physics incorporation of the photonics can be efficiently and effectively accomplished. This requires a clear understanding of the driving physics properties of the photonics device to ensure proper integration with no unintended consequences. The driving physics considerations in terms of optical performance will be identified for their use in system integration. Keywords: System Engineering, Optical Transfer Function, Optical Physics, Photonics, Image Jitter, Launch Vehicle, System Integration, Organizational Interaction

Watson, Michael D.↗
DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗
DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.

42 - ENGINEERING↗
NASA NTRS1989

Suggested criteria for evaluating systems engineering methodologies

Systems engineering is the application of mathematical and scientific principles to practical ends in the life-cycle of a system. A methodology for systems engineering is a carefully developed, relatively complex procedure or process for applying these mathematical and scientific principles. There are many systems engineering methodologies (or possibly many versions of a few methodologies) currently in use in government and industry. These methodologies are usually designed to meet the needs of a particular organization. It has been observed, however, that many technical and non-technical problems arise when inadequate systems engineering methodologies are applied by organizations to their systems development projects. Various criteria for evaluating systems engineering methodologies are discussed. Such criteria are developed to assist methodology-users in identifying and selecting methodologies that best fit the needs of the organization.

Gates, Audrey↗
NASA NTRS2013

Unifying Human Centered Design and Systems Engineering for Human Systems Integration

Despite the holistic approach of systems engineering (SE), systems still fail, and sometimes spectacularly. Requirements, solutions and the world constantly evolve and are very difficult to keep current. SE requires more flexibility and new approaches to SE have to be developed to include creativity as an integral part and where the functions of people and technology are appropriately allocated within our highly interconnected complex organizations. Instead of disregarding complexity because it is too difficult to handle, we should take advantage of it, discovering behavioral attractors and the emerging properties that it generates. Human-centered design (HCD) provides the creativity factor that SE lacks. It promotes modeling and simulation from the early stages of design and throughout the life cycle of a product. Unifying HCD and SE will shape appropriate human-systems integration (HSI) and produce successful systems.

Boy, Guy A.↗