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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Aspect-Oriented Programming

Computer science has experienced an evolution in programming languages and systems from the crude assembly and machine codes of the earliest computers through concepts such as formula translation, procedural programming, structured programming, functional programming, logic programming, and programming with abstract data types. Each of these steps in programming technology has advanced our ability to achieve clear separation of concerns at the source code level. Currently, the dominant programming paradigm is object-oriented programming - the idea that one builds a software system by decomposing a problem into objects and then writing the code of those objects. Such objects abstract together behavior and data into a single conceptual and physical entity. Object-orientation is reflected in the entire spectrum of current software development methodologies and tools - we have OO methodologies, analysis and design tools, and OO programming languages. Writing complex applications such as graphical user interfaces, operating systems, and distributed applications while maintaining comprehensible source code has been made possible with OOP. Success at developing simpler systems leads to aspirations for greater complexity. Object orientation is a clever idea, but has certain limitations. We are now seeing that many requirements do not decompose neatly into behavior centered on a single locus. Object technology has difficulty localizing concerns invoking global constraints and pandemic behaviors, appropriately segregating concerns, and applying domain-specific knowledge. Post-object programming (POP) mechanisms that look to increase the expressiveness of the OO paradigm are a fertile arena for current research. Examples of POP technologies include domain-specific languages, generative programming, generic programming, constraint languages, reflection and metaprogramming, feature-oriented development, views/viewpoints, and asynchronous message brokering. (Czarneclu and Eisenecker s book includes a good survey of many of these technologies).

Elrad, Tzilla↗
NASA NTRS1990

Automated extraction of knowledge for model-based diagnostics

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

Gonzalez, Avelino J.↗
NASA NTRS2014

GRAIL Refinements to Lunar Seismic Structure

Gravity field measurements are perhaps the most numerous of the indirect observations relevant to the Moon's internal structure. Multiple recent missions have mapped the global lunar gravity field, each one improving upon the resolution of the last. The details of the Moon's deepest structure, including the parameters that define the lunar core, however, were still largely unaddressed by pre-GRAIL gravity measurements, which were not high enough resolution to resolve the tidal coefficients at a sufficient degree of accuracy. Current constraints on core size and state arise from other indirect measurements, including lunar laser ranging, magnetic induction studies, and analyses of elemental abundances in depth-derived mare basalts. These inferences vary widely, but when considered together with structure models derived from the seismic data gathered during the Apollo missions, a schematic of the lunar interior containing a partially molten deepest mantle layer overlying molten outer and solid inner core layers was obtained. Seismology provides the most direct constraints on the variables that govern the dynamic properties of the body. However, the GRAIL mission's high-resolution measurements of the lunar gravity field are being used to constrain the interior structure of the Moon using a "crust to core" approach. GRAIL's constraints on crustal thickness, mantle structure, core radius and stratification, and core state (solid vs. molten) therefore complement seismic investigations. This work focuses on expanding our knowledge of the Moon's internal structure using joint gravity and seismic analyses, which will improve constraints on the deep lunar mantle and core.

Weber, Renee C.↗
DOE OSTI2021

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learn- ing to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

machine learning, multiscale modeling, physics-bas↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
NASA NTRS1972

Comets: Scientific data and missions; Proceedings of the Tucson Comet Conference, University of Arizona, Tucson, Ariz., April 8, 9, 1970.

Current knowledge of comets is surveyed, and comet-rendezvous mission constraints, opportunities, modes, and spacecraft capabilities are discussed. Attention is given to cometary nuclei, infrared measurements of comets, the nature and origin of the cometary head, L-alpha photometry of Comet Bennett, Types I and II tails, comet spectra and orbits, and evidence from stream meteoroids. Some scientific criteria for a cometary mission are considered. Individual items are announced in this issue.

Kuiper, G. P.↗
NASA NTRS1986

An experimental study of the properties of surface pressure fluctuations for separating turbulent boundary layers

Noise generated by helicopter and turbomachine rotors is a nuisance that designers would like to predict and to minimize within other design constraints. A key element for the noise calculation procedure is knowledge relating the flowfield structure to the surface pressure fluctuation structure. Surface pressure fluctuation data for zero-pressure-gradient and accelerating turbulent boundary layers were obtained. The zero-pressure-gradient turbulent boundary layers were examined with freestream velocities of 72 and 105 fps. Mean and fluctuation velocity profiles and streamwise velocity spectra and wavespeeds were obtained for momentum thickness Reynolds numbers up to 18000 for the zero-pressure-gradient case and up to 4000 for the favorable-pressure-gradient case. The wall shearing stress was estimated from a Clauser plot of the near wall data. It is clear that turbulent pressure fluctuations are produced by turbulent velocity fluctuations. Detailed simultaneous measurements of all of these fluctuations are needed to determine in more detail the structural relationships between velocity and pressure fields. Although some measurements were made for unseparated flows, none were made for separated flows.

Simpson, Roger L.↗
NASA NTRS1988

Cooperating knowledge-based systems

This final report covers work performed under Contract NCC2-220 between NASA Ames Research Center and the Knowledge Systems Laboratory, Stanford University. The period of research was from March 1, 1987 to February 29, 1988. Topics covered were as follows: (1) concurrent architectures for knowledge-based systems; (2) methods for the solution of geometric constraint satisfaction problems, and (3) reasoning under uncertainty. The research in concurrent architectures was co-funded by DARPA, as part of that agency's Strategic Computing Program. The research has been in progress since 1985, under DARPA and NASA sponsorship. The research in geometric constraint satisfaction has been done in the context of a particular application, that of determining the 3-D structure of complex protein molecules, using the constraints inferred from NMR measurements.

Feigenbaum, Edward A.↗
NASA NTRS1988

Moderately volatile elements

That the fractionation of moderately volatile and highly volatile elements was a major process in the early solar system is reflected in the variable concentrations of Rb and associated variations in initial Sr-isotopic ratios in chondritic meteorites. Greater knowledge of processes leading to volatile depletion would place stronger constraints on the formation conditions of solid material in the solar system. It will especially become possible to ascertain whether evaporation or incomplete condensation was the major process in establishing the elemental abundance patterns observed in primitive meteorites and planets.

Palme, H.↗
NASA NTRS1999

The Use of Laser Altimetry in the Orbit and Attitude Determination of Mars Global Surveyor

Altimetry from the Mars Observer Laser Altimeter (MOLA) which is carried on board Mars Global Surveyor (MGS) has been analyzed for the period of the MOS mission known as Science Phasing Orbit 1 (SPO-1). We have used these altimeter ranges to improve orbit and attitude knowledge for MGS. This has been accomplished by writing crossover constraint equations that have been derived from short passes of MOLA data. These constraint equations differ from traditional Crossover constraints and exploit the small foot print associated with laser altimetry.

Rowlands, D. D.↗
NASA NTRS1991

ISyCL technical report

The Air Force Integrated Information Systems Evolution Environment (IISEE) is a program focused on the development of technology for enabling the planning definition development and maintenance of evolutionary integrated information systems. This program has studied the representational needs of this knowledge base from the point of view of the needs of engineering, manufacturing, and logistics Evolutionary Information Systems (EIS). The definition of the concepts for a suite of automated tools and environments to support the pursuit of such engineering, manufacturing, and logistics EIS's has also required examination of languages and representation schemes for the knowledge base of these integrated systems. To accomplish this purpose, the language must be very rich, and must support object orientation, relational orientation, persistent storage, process transaction specification, and first order logic. The task of designing a unified language which supports these paradigms is a challenge. The Information Systems Constraint Language (ISyCL) is described. It was designed to meet the definitional and knowledge representation needs of three perspectives. It supports users from the area expert to the data base designer.

Decker, Louis P.↗
NASA NTRS2005

Knowledge Capture and Management for Space Flight Systems

The incorporation of knowledge capture and knowledge management strategies early in the development phase of an exploration program is necessary for safe and successful missions of human and robotic exploration vehicles over the life of a program. Following the transition from the development to the flight phase, loss of underlying theory and rationale governing design and requirements occur through a number of mechanisms. This degrades the quality of engineering work resulting in increased life cycle costs and risk to mission success and safety of flight. Due to budget constraints, concerned personnel in legacy programs often have to improvise methods for knowledge capture and management using existing, but often sub-optimal, information technology and archival resources. Application of advanced information technology to perform knowledge capture and management would be most effective if program wide requirements are defined at the beginning of a program.

Goodman, John L.↗
NASA NTRS1988

A flexible computer aid for conceptual design based on constraint propagation and component-modeling

The Rubber Airplane program, which combines two symbolic processing techniques with a component-based database of design knowledge, is proposed as a computer aid for conceptual design. Using object-oriented programming, programs are organized around the objects and behavior to be simulated, and using constraint propagation, declarative statements designate mathematical relationships among all the equation variables. It is found that the additional level of organizational structure resulting from the arrangement of the design information in terms of design components provides greater flexibility and convenience.

Kolb, Mark A.↗
DOE OSTI2021

Optimal Power Flow in DC Networks with Robust Feasibility and Stability Guarantees

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in DC networks such that stability and operational constraint satisfaction are of concern. Most existing DC network optimal power flow (DN-OPF) formulations assume exact knowledge of loading conditions and do not provide stability guarantees. Here, in contrast, this paper studies a DN-OPF formulation which considers both stability and operational constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this paper's robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally tractable problem by constructing a tight convex inner approximation of the stability set using sufficient conditions for the existence of a feasible and stable power flow solution. Optimal generator set-points are obtained by optimizing over the proposed convex stability set. The validity and effectiveness of the propose algorithm is demonstrated through various DC networks adapted from IEEE test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Hydrocarbon Rocket Technology Impact Forecasting

Ever since the Apollo program ended, the development of launch propulsion systems in the US has fallen drastically, with only two new booster engine developments, the SSME and the RS-68, occurring in the past few decades.1 In recent years, however, there has been an increased interest in pursuing more effective launch propulsion technologies in the U.S., exemplified by the NASA Office of the Chief Technologist s inclusion of Launch Propulsion Systems as the first technological area in the Space Technology Roadmaps2. One area of particular interest to both government agencies and commercial entities has been the development of hydrocarbon engines; NASA and the Air Force Research Lab3 have expressed interest in the use of hydrocarbon fuels for their respective SLS Booster and Reusable Booster System concepts, and two major commercially-developed launch vehicles SpaceX s Falcon 9 and Orbital Sciences Antares feature engines that use RP-1 kerosene fuel. Compared to engines powered by liquid hydrogen, hydrocarbon-fueled engines have a greater propellant density (usually resulting in a lighter overall engine), produce greater propulsive force, possess easier fuel handling and loading, and for reusable vehicle concepts can provide a shorter turnaround time between launches. These benefits suggest that a hydrocarbon-fueled launch vehicle would allow for a cheap and frequent means of access to space.1 However, the time and money required for the development of a new engine still presents a major challenge. Long and costly design, development, testing and evaluation (DDT&E) programs underscore the importance of identifying critical technologies and prioritizing investment efforts. Trade studies must be performed on engine concepts examining the affordability, operability, and reliability of each concept, and quantifying the impacts of proposed technologies. These studies can be performed through use of the Technology Impact Forecasting (TIF) method. The Technology Impact Forecasting method is a normative forecasting technique that allows the designer to quantify the effects of adding new technologies on a given design. This method can be used to assess and identify the necessary technological improvements needed to close the gap that exists between the current design and one that satisfies all constraints imposed on the design. The TIF methodology allows for more design knowledge to be brought to the earlier phases of the design process, making use of tools such as Quality Function Deployments, Morphological Matrices, Response Surface Methodology, and Monte Carlo Simulations.2 This increased knowledge allows for more informed decisions to be made earlier in the design process, resulting in shortened design cycle time. This paper will investigate applying the TIF method, which has been widely used in aircraft applications, to the conceptual design of a hydrocarbon rocket engine. In order to reinstate a manned presence in space, the U.S. must develop an affordable and sustainable launch capability. Hydrocarbon-fueled rockets have drawn interest from numerous major government and commercial entities because they offer a low-cost heavy-lift option that would allow for frequent launches1. However, the development of effective new hydrocarbon rockets would likely require new technologies in order to overcome certain design constraints. The use of advanced design methods, such as the TIF method, enables the designer to identify key areas in need of improvement, allowing one to dial in a proposed technology and assess its impact on the system. Through analyses such as this one, a conceptual design for a hydrocarbon-fueled vehicle that meets all imposed requirements can be achieved.

Stuber, Eric↗
NASA NTRS1990

Knowledge representation to support reasoning based on multiple models

Model Based Reasoning is a powerful tool used to design and analyze systems, which are often composed of numerous interactive, interrelated subsystems. Models of the subsystems are written independently and may be used together while they are still under development. Thus the models are not static. They evolve as information becomes obsolete, as improved artifact descriptions are developed, and as system capabilities change. Researchers are using three methods to support knowledge/data base growth, to track the model evolution, and to handle knowledge from diverse domains. First, the representation methodology is based on having pools, or types, of knowledge from which each model is constructed. In addition information is explicit. This includes the interactions between components, the description of the artifact structure, and the constraints and limitations of the models. The third principle we have followed is the separation of the data and knowledge from the inferencing and equation solving mechanisms. This methodology is used in two distinct knowledge-based systems: one for the design of space systems and another for the synthesis of VLSI circuits. It has facilitated the growth and evolution of our models, made accountability of results explicit, and provided credibility for the user community. These capabilities have been implemented and are being used in actual design projects.

Gillam, April↗
DOE OSTI2026

Polar primary aerosols across the ocean-sea ice-snow-atmosphere interface: From sources to impacts

Primary aerosols play a critical role in polar climate systems, influencing cloud formation, precipitation, radiative balance, and surface energy budgets. This paper provides a comprehensive synthesis of primary aerosol sources, transformation and removal processes, and broader atmospheric impacts in polar regions, emphasizing their links to ocean and sea ice biogeochemistry. These aerosols (including sea salt, primary organic aerosol, and primary biological aerosol particles) originate from marine and cryospheric environments and are emitted through physical processes, such as wave breaking, bubble bursting, and blowing snow. Emission sources include seawater, sea ice, snow, and freshwater from river discharge and glacial runoff. Once airborne, these particles can serve as a chemical reservoir, influencing atmospheric composition and reactivity, and as seeds for cloud droplet and ice crystal formation, influencing cloud microphysics and polar climate. Despite their importance, many of the processes governing primary aerosol emissions and transformations remain poorly constrained. The most pressing knowledge gaps pertain to emission processes, limited spatiotemporal observational coverage, instrumentation constraints, parameterization development, and the integration of interdisciplinary expertise. To improve our understanding of primary aerosol drivers and their response to climate, future research efforts should prioritize strategically coordinated and cross-disciplinary process studies, advancements in measurement technologies and coverage, and close collaboration between modelers and observational scientists to inform and refine model parameterizations. As polar regions continue to undergo profound changes marked by increased precipitation, reduced sea and land ice, freshening oceans, and shifting ecosystem dynamics, characterizing present-day primary aerosol populations is vital. Improved understanding will be essential for anticipating future changes in aerosol-radiation and aerosol-cloud interactions and their implications for polar and global climate systems.

Aerosol-cloud↗
NASA NTRS1990

Capturing design knowledge

A scheme is proposed to capture the design knowledge of a complex object including functional, structural, performance, and other constraints. Further, the proposed scheme is also capable of capturing the rationale behind the design of an object as a part of the overall design of the object. With this information, the design of an object can be treated as a case and stored with other designs in a case base. A person can then perform case-based reasoning by examining these designs. Methods of modifying object designs are also discussed. Finally, an overview of an approach to fault diagnosis using case-based reasoning is given.

Babin, Brian A.↗