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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 NTRS1992

Advanced life support systems in lunar and Martian environments utilizing a higher plant based engineering paradigm

The paper describes a higher-plant-based engineering paradigm for advanced life support in a Controlled Ecological Life Support System (CELSS) on the surface of the moon or Mars, called the CELSS Breadboard Project, designed at John F. Kennedy Space Center. Such a higher-plant-based system would use the plants for a direct food source, gas exchange, water reclamation, and plant residuals in a complex biological resource recovery scheme. The CELSS Breadboard Project utilizes a 'breadboard' approach of developing independent systems that are evaluated autonomously and are later interconnected. Such a scheme will enable evaluation of life support system methodologies tested for their efficiency in a life support system for habitats on the moon or Mars.

Chamberland, Dennis↗
NASA NTRS1992

Knowledge modeling for software design

This paper develops a modeling framework for systems engineering that encompasses systems modeling, task modeling, and knowledge modeling, and allows knowledge engineering and software engineering to be seen as part of a unified developmental process. This framework is used to evaluate what novel contributions the 'knowledge engineering' paradigm has made and how these impact software engineering.

Shaw, Mildred L. G.↗
NASA NTRS2002

Integrating Theory and Practice: Applying the Quality Improvement Paradigm to Product Line Engineering

My assertion is that not only are product lines a relevant research topic, but that the tools used by empirical software engineering researchers can address observed practical problems. Our experience at NASA has been there are often externally proposed solutions available, but that we have had difficulties applying them in our particular context. We have also focused on return on investment issues when evaluating product lines, and while these are important, one can not attain objective data on success or failure until several applications from a product family have been deployed. The use of the Quality Improvement Paradigm (QIP) can address these issues: (1) Planning an adoption path from an organization's current state to a product line approach; (2) Constructing a development process to fit the organization's adoption path; (3) Evaluation of product line development processes as the project is being developed. The QIP consists of the following six steps: (1) Characterize the project and its environment; (2) Set quantifiable goals for successful project performance; (3) Choose the appropriate process models, supporting methods, and tools for the project; (4) Execute the process, analyze interim results, and provide real-time feedback for corrective action; (5) Analyze the results of completed projects and recommend improvements; and (6) Package the lessons learned as updated and refined process models. A figure shows the QIP in detail. The iterative nature of the QIP supports an incremental development approach to product lines, and the project learning and feedback provide the necessary early evaluations.

Stark, Michael↗
NASA NTRS2011

Dynamic Gate Product and Artifact Generation from System Models

Model Based Systems Engineering (MBSE) is gaining acceptance as a way to formalize systems engineering practice through the use of models. The traditional method of producing and managing a plethora of disjointed documents and presentations ("Power-Point Engineering") has proven both costly and limiting as a means to manage the complex and sophisticated specifications of modern space systems. We have developed a tool and method to produce sophisticated artifacts as views and by-products of integrated models, allowing us to minimize the practice of "Power-Point Engineering" from model-based projects and demonstrate the ability of MBSE to work within and supersede traditional engineering practices. This paper describes how we have created and successfully used model-based document generation techniques to extract paper artifacts from complex SysML and UML models in support of successful project reviews. Use of formal SysML and UML models for architecture and system design enables production of review documents, textual artifacts, and analyses that are consistent with one-another and require virtually no labor-intensive maintenance across small-scale design changes and multiple authors. This effort thus enables approaches that focus more on rigorous engineering work and less on "PowerPoint engineering" and production of paper-based documents or their "office-productivity" file equivalents.

engineering paradigm↗
NASA NTRSDate not supplied

A Model-Based Systems Engineering Journey to Developing a Concept of Operations

Starting in 2017, NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) element began a systems engineering transition from traditional, document-centric development to model-centric development when defining its foundation medical systems. These foundation medical systems define a Concept of Operations (ConOps) and identify the generic requirements for a medical system based on assumptions about a generic crew and mission environments and guidance from NASA standards (e.g., Medical “Levels of Care”). By making the transition, ExMC intends to improve communication among stakeholders about foundation medical system requirements and content. In addition, this transition will enable ExMC to lower both development and crew treatment risks for future, mission-specific medical systems. ExMC followed a Model Based Systems Engineering (MBSE) paradigm when developing the foundation medical systems. A model-based approach provides several advantages over a traditional, document-centric approach. First, when Systems Engineers (SE) develop diagrams in a model using a standard modeling language, they produce information dense pictures that facilitate understanding much more efficiently with less room for misinterpretation than text. Second, due to the evolving nature of projects, documentation becomes out of date the minute it is published. This can result in people making decisions based on information that is no longer current, especially if they are referencing a locally-stored copy of a document. A model, on the other hand, is always up to date with the latest approved changes and information. It serves as a single point of truth. Third, a model-centric approach centralizes all important information in one place. Rather than having to flip through separate ConOps documents, design specifications, requirements specifications, and the like to coordinate information, a model captures the content in one, integrated spot. This integration makes tracing information from end-to-end easier with greater reliability. The ExMC Systems Engineering Lifecycle follows a well-defined process. ExMC Systems Engineers perform all major steps of the process, regardless of the development methodology. One of the first steps in the process is developing the ConOps that describes the operation of the system from the point of view of the users. It includes a list of the users and their needs, the goals of the medical system, key assumptions about the system, and definitions of the medical system’s operational environments. For this development effort, ExMC chose to replace the traditional text-based ConOps document with a model. While the decision to change the development workflow was not difficult, implementing the structural and organizational workflows were. It required showing ExMC’s users, most of whom are not Systems Engineers, how the information they require would be presented in the model and to gain their acceptance of this approach. This paper documents key lessons learned during the ConOps transformation by focusing on how the model represents information, the agile workflow used by SEs when developing the model and how it integrates into a project plan, how leadership influenced key users to accept the transformation, and how the users interact with the model information.

Jeffrey Robert Cohen↗
NASA NTRS2003

Tracking and Control of Gas Turbine Engine Component Damage/Life

This paper describes damage mechanisms and the methods of controlling damages to extend the on-wing life of critical gas turbine engine components. Particularly, two types of damage mechanisms are discussed: creep/rupture and thermo-mechanical fatigue. To control these damages and extend the life of engine hot-section components, we have investigated two methodologies to be implemented as additional control logic for the on-board electronic control unit. This new logic, the life-extending control (LEC), interacts with the engine control and monitoring unit and modifies the fuel flow to reduce component damages in a flight mission. The LEC methodologies were demonstrated in a real-time, hardware-in-the-loop simulation. The results show that LEC is not only a new paradigm for engine control design, but also a promising technology for extending the service life of engine components, hence reducing the life cycle cost of the engine.

Jaw, Link C.↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
DOE OSTI2024

Opportunities for Process Intensification with Membranes to Promote Circular Economy Development for Critical Minerals

Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportu-nities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially con-cerning chemical reagent usage. Feed concentrations and product demand variability require flex-ible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.

Dougher, Molly↗
NASA NTRS2016

Early Engagement of Safety and Mission Assurance Expertise Using Systems Engineering Tools: A Risk-Based Approach to Early Identification of Safety and Assurance Requirements

Decades of systems engineering practice have demonstrated that the earlier the identification of requirements occurs, the lower the chance that costly redesigns will needed later in the project life cycle. A better understanding of all requirements can also improve the likelihood of a design's success. Significant effort has been put into developing tools and practices that facilitate requirements determination, including those that are part of the model-based systems engineering (MBSE) paradigm. These efforts have yielded improvements in requirements definition, but have thus far focused on a design's performance needs. The identification of safety & mission assurance (S&MA) related requirements, in comparison, can occur after preliminary designs are already established, yielding forced redesigns. Engaging S&MA expertise at an earlier stage, facilitated by the use of MBSE tools, and focused on actual project risk, can yield the same type of design life cycle improvements that have been realized in technical and performance requirements.

Requirement↗
NASA NTRS2021

Extending the Licklider Transmission Protocol to Multi-Band Links

Most deep space missions return data to Earth using links operating at a single frequency band. Indeed, their data requirements are low enough that bandwidth regulations do not constrain the system. In contrast, spacecraft such as Kepler or Europa Clipper are transitioning to a new operational paradigm where engineering and science data are transmitted through simultaneous links operating at different frequency bands (henceforth termed multi-band links). This ensures, for instance, that critical data is correctly received using a well characterized X-band link, while science data at a much larger data rate can be returned efficiently (both in terms of bandwidth and energy) through a Ka-band link.Having a spacecraft establish two simultaneous links with a ground station opens a large span of potential improvements for space communications and mission operations. In this paper, we consider the problem of running a Licklider Transmission Protocol (LTP) session over a multi-band link. LTP is an implementation of a selective Automatic Repeat reQuest (ARQ) protocol, i.e. it ensures correct delivery of data over an error prone link with potentially long propagation delays. To maximize its efficiency in deep space environments, LTP operates in deferred-ACK mode and is typically included as one of the core protocols in the Delay Tolerant Networking (DTN) suite.The contributions of this paper are as follows: First, we propose an extension to LTP for multi-band links (denoted MBLTP) and sketch how it can be implemented without modifying the current definition of LTP data units. Next, we develop bounds on the performance of MBLTP when transmitting a single data file over a multi-band link. Three metrics are considered, file expected delivery time, total energy spent and bundle jitter. The results of the analytic model are first benchmarked against simulations to ensure validity, and then compared against the performance of both traditional LTP and Parallel LTP (PLTP).We demonstrate that MBLTP can significantly reduce the latency and jitter with which data products are delivered to destination over a deep space link compared to LTP at moderate energy cost. Similarly, we also demonstrate that MBLTP outperforms PLTP in all considered metrics.

Sanchez Net, Marc↗
NASA NTRS1993

The maturing of the quality improvement paradigm in the SEL

The Software Engineering Laboratory uses a paradigm for improving the software process and product, called the quality improvement paradigm. This paradigm has evolved over the past 18 years, along with our software development processes and product. Since 1976, when we first began the SEL, we have learned a great deal about improving the software process and product, making a great many mistakes along the way. Quality improvement paradigm, as it is currently defined, can be broken up into six steps: characterize the current project and its environment with respect to the appropriate models and metrics; set the quantifiable goals for successful project performance and improvement; choose the appropriate process model and supporting methods and tools for this project; execute the processes, construct the products, and collect, validate, and analyze the data to provide real-time feedback for corrective action; analyze the data to evaluate the current practices, determine problems, record findings, and make recommendations for future project improvements; and package the experience gained in the form of updated and refined models and other forms of structured knowledge gained from this and prior projects and save it in an experience base to be reused on future projects.

Basili, Victor R.↗
DOE OSTI2024

The Digital Engineering Vision for DOME: Facilitating Design, Deployment, and Operations [Poster]

DOME is a planned microreactor test facility at INL’s Materials and Fuels Complex. It is a complex system with several interdependent sub-systems such as the reactor (up to 20 MWth), radioactive confinement, temperature and pressure regulation system, ventilation system, etc. The engineering design process for such a system traditionally involves several documents from various sources and the system information is scattered across these documents. Digital engineering represents a paradigm shift through which systems are designed using digital models and integrated data. The digital engineering vision for DOME utilizes a model-based systems engineering (MBSE) approach. The system architecture, physical components, control logic, and verification experiments are all designed using MathWorks MATLAB and Simulink. This hierarchical model can combine data from multiple sources at various levels of abstraction. It can be used to simulate the facility’s operations and to test the system using different sets of parameters. Its capabilities can be expanded by interfacing it with high-fidelity multi-physics models, risk analysis tools, etc. The same model can evolve into a digital twin that can monitor operations and conduct predictive analysis using real-time sensor data from the facility. The eventual goal of this effort is to transform the end-to-end engineering of nuclear facilities in every phase of their lifecycle, including design, deployment, and operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

SinhaRoy_TechPresentation_2024 [Slides]

DOME is a planned microreactor test facility at INL’s Materials and Fuels Complex. It is a complex system with several interdependent sub-systems such as the reactor (up to 20 MWth), radioactive confinement, temperature and pressure regulation system, ventilation system, etc. The engineering design process for such a system traditionally involves several documents from various sources and the system information is scattered across these documents. Digital engineering represents a paradigm shift through which systems are designed using digital models and integrated data. The digital engineering vision for DOME utilizes a model-based systems engineering (MBSE) approach. The system architecture, physical components, control logic, and verification experiments are all designed using MathWorks MATLAB and Simulink. This hierarchical model can combine data from multiple sources at various levels of abstraction. It can be used to simulate the facility’s operations and to test the system using different sets of parameters. Its capabilities can be expanded by interfacing it with high-fidelity multi-physics models, risk analysis tools, etc. The same model can evolve into a digital twin that can monitor operations and conduct predictive analysis using real-time sensor data from the facility. The eventual goal of this effort is to transform the end-to-end engineering of nuclear facilities in every phase of their lifecycle, including design, deployment, and operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1992

Automated support for system's engineering and operations - The development of new paradigms

Technological developments in spacecraft ground operations are reviewed. The technological, operations-oriented, managerial, and economic factors driving the evolution of the Mission Operations Control Center (MOCC), and its predecessor the Operational Control Center are examined. The functional components of the various MOCC subsystems are outlined. A brief overview is given of the concepts behind the The Knowledge-Based Software Engineering Environment, the Generic Spacecraft Analysis Assistant, and the Knowledge From Pictures tool.

Truszkowski, Walt↗
NASA NTRS1988

Simple explanations and reasoning: From philosophy of science to expert systems

A preliminary prototype of a simple explanation system was constructed. Although the system, based on the idea of storytelling, did not incorporate all of the principles of simple explanation, it did demonstrate the potential of the approach. The system incorporated a hypertext system, an inference engine, and facilities for constructing contrast type explanations. The continued development of such a system should prove to be valuable. By extending the resources of the expert system paradigm, the knowledge engineer is not forced to learn a new set of skills, and the domain knowledge already acquired by him is not lost. Further, both the beginning user and the more advanced user can be accommodated. For the beginning user, corrective explanations and ES explanations provide facilities for more clearly understanding the way in which the system is functioning. For the more advanced user, the instance and state explanations allow him to focus on the issues at hand. The simple model of explanation attempts to exploit and show how the why and how facilities of the expert system paradigm can be extended by attending to the pragmatics of explanation and adding texture to the ordinary pattern of reasoning in a rule based system.

Rochowiak, Daniel↗
NASA NTRS2004

Collaborative Engineering for Research and Development

Research and development (R&D) organizations are being required to be relevant, to be more application-oriented, and to be partners in the strategic management of the business while meeting the same challenges as the rest of the organization, namely: (1) reduced time to market; (2) reduced cost; (3) improved quality; (4) increased reliability; and (5) increased focus on customer needs. Recent advances in computer technology and the Internet have created a new paradigm of collaborative engineering or collaborative product development (CPD), from which new types of relationships among researchers and their partners have emerged. Research into the applicability and benefits of CPD in a low/no production, R&D, and/or government environment is limited. In addition, the supply chain management (SCM) aspects of these relationships have not been studied. This paper presents research conducted at the NASA Glenn Research Center (GRC) investigating the applicability of CPD and SCM in an R&D organization. The study concentrates on the management and implementation of space research activities at GRC. Results indicate that although the organization is engaged in collaborative relationships that incorporate aspects of SCM, a number of areas, such as development of trust and information sharing merit special attention.

Davis, Jose M.↗
DOE OSTI2024

Exact enforcement of temporal continuity in sequential physics-informed neural networks

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.

42 ENGINEERING↗