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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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NASA NTRS1994

Automated inspection of turbine blades: Challenges and opportunities

Current inspection methods for complex shapes and contours exemplified by aircraft engine turbine blades are expensive, time-consuming and labor intensive. The logistics support of new manufacturing paradigms such as integrated product-process development (IPPD) for current and future engine technology development necessitates high speed, automated inspection of forged and cast jet engine blades, combined with a capability of retaining and retrieving metrology data for process improvements upstream (designer-level) and downstream (end-user facilities) at commercial and military installations. The paper presents the opportunities emerging from a feasibility study conducted using 3-D holographic laser radar in blade inspection. Requisite developments in computing technologies for systems integration of blade inspection in production are also discussed.

Mehta, Manish↗

NASA NTRS2011

Product Lifecycle Management and the Quest for Sustainable Space Exploration Solutions

Product Lifecycle Management (PLM) is an outcome of lean thinking to eliminate waste and increase productivity. PLM is inextricably tied to the systems engineering business philosophy, coupled with a methodology by which personnel, processes and practices, and information technology combine to form an architecture platform for product design, development, manufacturing, operations, and decommissioning. In this model, which is being implemented by the Marshall Space Flight Center (MSFC) Engineering Directorate, total lifecycle costs are important variables for critical decision-making. With the ultimate goal to deliver quality products that meet or exceed requirements on time and within budget, PLM is a powerful concept to shape everything from engineering trade studies and testing goals, to integrated vehicle operations and retirement scenarios. This briefing will demonstrate how the MSFC Engineering Directorate is implementing PLM as part of an overall strategy to deliver safe, reliable, and affordable space exploration solutions and how that strategy aligns with the Agency and Center systems engineering policies and processes. Sustainable space exploration solutions demand that all lifecycle phases be optimized, and engineering the next generation space transportation system requires a paradigm shift such that digital tools and knowledge management, which are central elements of PLM, are used consistently to maximum effect. Adopting PLM, which has been used by the aerospace and automotive industry for many years, for spacecraft applications provides a foundation for strong, disciplined systems engineering and accountable return on investment. PLM enables better solutions using fewer resources by making lifecycle considerations in an integrative decision-making process.

Caruso, Pamela W.↗

DOE OSTI2024

Paradigm for approaching the forbidden phase transition in the one-dimensional Ising model at fixed finite temperature: Single chain in a magnetic field

In a previous paper, the forbidden spontaneous phase transition in the one-dimensional Ising model was found to be approachable arbitrarily closely in decorated ladders by ultra-narrow phase crossover (UNPC) at a given finite temperature T 0 with the crossover width 2δT reduced exponentially, which resemble a genuine first-order transition with large latent heat. Here, I reveal that the forbidden phase transition can be approached at fixed T 0 as well in decorated single-chain Ising models in the presence of a magnetic field, in which T 0 is determined by the interactions involving only the decorated parts and the magnetic field, while 2δT is independently, exponentially reduced (δT = 0 means a genuine transition) by restoring the ferromagnetic interaction between the ordinary spins on the chain backbone-which was neglected in the previous studies of pseudo-transition-thus manifesting that this asymptoticity to the forbidden transition is essentially the buildup of coherence in preformed crossover of local states. Furthermore, I show that the UNPC can be realized even in the absence of the conventional geometric frustration because the magnetic field itself can induce previously unnoticed hidden spin frustration. In conclusion, these findings make the doors wide open to the engineering and utilization of UNPC as a new paradigm for exploring exotic phenomena and 1D device applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2013

Integrated Tools for Future Distributed Engine Control Technologies

Turbine engines are highly complex mechanical systems that are becoming increasingly dependent on control technologies to achieve system performance and safety metrics. However, the contribution of controls to these measurable system objectives is difficult to quantify due to a lack of tools capable of informing the decision makers. This shortcoming hinders technology insertion in the engine design process. NASA Glenn Research Center is developing a Hardware-inthe- Loop (HIL) platform and analysis tool set that will serve as a focal point for new control technologies, especially those related to the hardware development and integration of distributed engine control. The HIL platform is intended to enable rapid and detailed evaluation of new engine control applications, from conceptual design through hardware development, in order to quantify their impact on engine systems. This paper discusses the complex interactions of the control system, within the context of the larger engine system, and how new control technologies are changing that paradigm. The conceptual design of the new HIL platform is then described as a primary tool to address those interactions and how it will help feed the insertion of new technologies into future engine systems.

Culley, Dennis↗

NASA NTRS2004

Enabling a New Planning and Scheduling Paradigm

The Flight Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called "tasks models," from the scientists and technologists for the tasks that they want to be done. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, another cadre further modifies the models to be compatible with the scheduling engine. This last cadre also submits the models to the scheduling engine or builds the timeline manually to accommodate requirements that are expressed in notes. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components. (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphics methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm which can exploit timing flexibilities and constraint and relationship options. (3) A web-based architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.

Jaap, John↗

NASA NTRS2014

Simulation/Emulation Techniques: Compressing Schedules With Parallel (HW/SW) Development

NASA has always been in the business of balancing new technologies and techniques to achieve human space travel objectives. NASA's Kedalion engineering analysis lab has been validating and using many contemporary avionics HW/SW development and integration techniques, which represent new paradigms to NASA's heritage culture. Kedalion has validated many of the Orion HW/SW engineering techniques borrowed from the adjacent commercial aircraft avionics solution space, inserting new techniques and skills into the Multi - Purpose Crew Vehicle (MPCV) Orion program. Using contemporary agile techniques, Commercial-off-the-shelf (COTS) products, early rapid prototyping, in-house expertise and tools, and extensive use of simulators and emulators, NASA has achieved cost effective paradigms that are currently serving the Orion program effectively. Elements of long lead custom hardware on the Orion program have necessitated early use of simulators and emulators in advance of deliverable hardware to achieve parallel design and development on a compressed schedule.

Mangieri, Mark L.↗

NASA NTRSDate not supplied

Considerations For Waste-to-Base Future Research Paths

Developing innovative technologies to reprocess low value waste into high value base products will enable a new “Waste-to-Base” (WtB) capability that can spawn a new approach to mission design. Systems engineers and mission architects will work together under a new paradigm in logistics planning. Equipment, components, packaging, storage, and structures will be understood not only by functional requirements, but also in terms of useful lifetimes and materials of construction. The advent of a new technical discipline - reprocess engineering - will grow from the intersection between chemical, mechanical, and systems engineering, mission design, and logistics planning. A crowdsourcing approach was employed to gather low TRL concepts into workable solutions for issues in trash management, CO2 processing, foam re-processing, and fecal processing. The results of the crowdsourcing activities are summarized along with a roadmap for future technology development work.

Logistics reduction↗

NASA NTRSDate not supplied

Considerations For Waste-to-Base Future Research Paths

Developing innovative technologies to reprocess low value waste into high value base products will enable a new “Waste-to-Base” (WtB) capability leading to innovative approaches to space mission design. Systems engineers and mission architects will work together under a new paradigm in logistics planning. Equipment, components, packaging, storage, and structures will be understood not only by functional requirements, but also in terms of useful lifetimes and materials of construction. The advent of a new technical discipline - reprocess engineering - will grow from the intersection between chemical, mechanical, and systems engineering, mission design, and logistics planning. A crowdsourcing approach was employed to gather low Technology Readiness Level (TRL) concepts into workable solutions for issues in trash management, CO 2 processing, foam reprocessing, and fecal processing. The results of the crowdsourcing activities are summarized along with a discussion for future technology development work.

Logistics reduction↗

NASA NTRSDate not supplied

Considerations For Waste-to-Base Future Research Paths

Developing innovative technologies to reprocess low value waste into high value base products will enable a new “Waste-to-Base” (WtB) capability leading to innovative approaches to space mission design. Systems engineers and mission architects will work together under a new paradigm in logistics planning. Equipment, components, packaging, storage, and structures will be understood not only by functional requirements, but also in terms of useful lifetimes and materials of construction. The advent of a new technical discipline - reprocess engineering - will grow from the intersection between chemical, mechanical, and systems engineering, mission design, and logistics planning. A crowdsourcing approach was employed to gather low Technology Readiness Level (TRL) concepts into workable solutions for issues in trash management, CO2 processing, foam reprocessing, and fecal processing. The results of the crowdsourcing activities are summarized along with a discussion for future technology development work.

Logistics reduction↗

NASA NTRS1988

The TAME Project: Towards improvement-oriented software environments

Experience from a dozen years of analyzing software engineering processes and products is summarized as a set of software engineering and measurement principles that argue for software engineering process models that integrate sound planning and analysis into the construction process. In the TAME (Tailoring A Measurement Environment) project at the University of Maryland, such an improvement-oriented software engineering process model was developed that uses the goal/question/metric paradigm to integrate the constructive and analytic aspects of software development. The model provides a mechanism for formalizing the characterization and planning tasks, controlling and improving projects based on quantitative analysis, learning in a deeper and more systematic way about the software process and product, and feeding the appropriate experience back into the current and future projects. The TAME system is an instantiation of the TAME software engineering process model as an ISEE (integrated software engineering environment). The first in a series of TAME system prototypes has been developed. An assessment of experience with this first limited prototype is presented including a reassessment of its initial architecture.

Basili, Victor R.↗

NASA NTRS2004

Software for Intelligent System Health Management (ISHM)

The slide presentation is a briefing in four areas: overview of health management paradigms; overview of the ARC-Houston Software Engineering Technology Workshop held on April 20-22, 2004; identified technologies relevant to technical themes of intelligent system health management; and the author's thoughts on these topics.

Trevino, Luis C.↗

NASA NTRS2018

Why Space Technology Needs More Non-Traditional Chemical Engineers – From the Perspective of a NASA Chemical Engineering Student

Final document is attached. With planetary missions and deep space gateway on the horizon, humankind is entering a new era of space exploration. Manned mission durations will be longer than any previous flight, resupply capabilities are limited, and system robustness is more imperative than ever. Traditional space systems, especially in the realm of carbon dioxide removal and water remediation, are large in size, mass, energy consumption, and number of mechanical moving parts prone to failure. Non-traditional space exploration needs non-traditional technology solutions. Organizational psychologist Adam Grant is well-known for his studies on originality and how non-conformists can move the world. In the words of Adam Grant, "to become original, you have to try something new, which means accepting some measure of risk." As chemical engineers, we shouldn't let that measure of risk stop us from considering ingenious technology. As a part of the new generation of engineers at NASA, I believe we need a paradigm shift in space technology for this new era of exploration.

Rogers, Tanya↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRS2013

Mobile Timekeeping Application Built on Reverse-Engineered JPL Infrastructure

Every year, non-exempt employees cumulatively waste over one man-year tracking their time and using the timekeeping Web page to save those times. This app eliminates this waste. The innovation is a native iPhone app. Libraries were built around a reverse- engineered JPL API. It represents a punch-in/punch-out paradigm for timekeeping. It is accessible natively via iPhones, and features ease of access. Any non-exempt employee can natively punch in and out, as well as save and view their JPL timecard. This app is built on custom libraries created by reverse-engineering the standard timekeeping application. Communication is through custom libraries that re-route traffic through BrowserRAS (remote access service). This has value at any center where employees track their time.

Witoff, Robert J.↗

NASA NTRS2004

An Enabling Technology for New Planning and Scheduling Paradigms

The Night Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called ?ask models," from the scientists and technologists for the tasks that are to be scheduled. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next, a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, the models are modified to be compatible with the scheduling engine. Then the models are submitted to the scheduling engine for automatic scheduling or, when requirements are expressed in notes, the timeline is built manually. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components: (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphical methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm that can exploit timing flexibilities and constraint and relationship options. (3) An innovative architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the remote users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.

Jaap, John↗