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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

A Comparison of Techniques for Scheduling Earth-Observing Satellites

Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks, conditions where evolutionary and related algorithms are often effective. However, there are many such algorithms and the best one to use is not clear. Here we compare multiple variants of the genetic algorithm: stochastic hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on ten realistically-sized EOS scheduling problems. Schedules are represented by a permutation (non-temperal ordering) of the observation requests. A simple deterministic scheduler assigns times and resources to each observation request in the order indicated by the permutation, discarding those that violate the constraints created by previously scheduled observations. Simulated annealing performs best. Random mutation outperform a more 'intelligent' mutator. Furthermore, the best mutator, by a small margin, was a novel approach we call temperature dependent random sampling that makes large changes in the early stages of evolution and smaller changes towards the end of search.

Globus, Al↗
NASA NTRS2005

Low-Speed Active Flow Control Laboratory Developed

The future of aviation propulsion systems is increasingly focused on the application of control technologies to significantly enhance the performance of a new generation of air vehicles. Active flow control refers to a set of technologies that manipulate the flow of air and combustion gases deep within the confines of an engine to dynamically alter its performance during flight. By employing active flow control, designers can create engines that are significantly lighter, are more fuel efficient, and produce lower emissions. In addition, the operating range of an engine can be extended, yielding safer transportation systems. The realization of these future propulsion systems requires the collaborative development of many base technologies to achieve intelligent, embedded control at the engine locations where it will be most effective. NASA Glenn Research Center s Controls and Dynamics Technology Branch has developed a state-of-the-art low-speed Active Flow Control Laboratory in which emerging technologies can be integrated and explored in a flexible, low-cost environment. The facility allows the most promising developments to be prescreened and optimized before being tested on higher fidelity platforms, thereby reducing the cost of experimentation and improving research effectiveness.

Culley, Dennis E.↗
NASA NTRS1989

Automating the multiprocessing environment

An approach to automate the programming and operation of tree-structured networks of multiprocessor systems is discussed. A conceptual, knowledge-based operating environment is presented, and requirements for two major technology elements are identified as follows: (1) An intelligent information translator is proposed for implementating information transfer between dissimilar hardware and software, thereby enabling independent and modular development of future systems and promoting a language-independence of codes and information; (2) A resident system activity manager, which recognizes the systems capabilities and monitors the status of all systems within the environment, is proposed for integrating dissimilar systems into effective parallel processing resources to optimally meet user needs. Finally, key computational capabilities which must be provided before the environment can be realized are identified.

Arpasi, Dale J.↗
NASA NTRS1992

Real-time decision aiding - Aircraft guidance for wind shear avoidance

Modern control theory and artificial intelligence technology are applied to the Wind Shear Safety Advisor, a conceptual airborne advisory system to help flight crews avoid or survive encounter with hazardous low-altitude wind shear. Numerical and symbolic processes of the system fuse diverse, time-varying data from ground-based and airborne measurements. Simulated wind-shear-encounter scenarios illustrate the need to consider a variety of factors for optimal decision reliability. The wind-shear-encounter simulations show the Wind Shear Safety Advisor's potential for effectively integrating the available information, highlighting the benefits of the computational techniques employed.

Stratton, D. A.↗
NASA NTRS1994

Communications and control for electric power systems: Power system stability applications of artificial neural networks

This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

Toomarian, N.↗
NASA NTRS2002

Compact Video Microscope Imaging System Implemented in Colloid Studies

Long description Photographs showing fiber-optic light source, microscope and charge-coupled discharge (CCD) camera head connected to camera body, CCD camera body feeding data to image acquisition board in PC, and Cartesian robot controlled via PC board. The Compact Microscope Imaging System (CMIS) is a diagnostic tool with intelligent controls for use in space, industrial, medical, and security applications. CMIS can be used in situ with a minimum amount of user intervention. This system can scan, find areas of interest in, focus on, and acquire images automatically. Many multiple-cell experiments require microscopy for in situ observations; this is feasible only with compact microscope systems. CMIS is a miniature machine vision system that combines intelligent image processing with remote control. The software also has a user-friendly interface, which can be used independently of the hardware for further post-experiment analysis. CMIS has been successfully developed in the SML Laboratory at the NASA Glenn Research Center and adapted for use for colloid studies and is available for telescience experiments. The main innovations this year are an improved interface, optimized algorithms, and the ability to control conventional full-sized microscopes in addition to compact microscopes. The CMIS software-hardware interface is being integrated into our SML Analysis package, which will be a robust general-purpose image-processing package that can handle over 100 space and industrial applications.

McDowell, Mark↗
NASA NTRS1997

Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

Fang, Wai-Chi↗
NASA NTRS1994

Knowledge-Based Intelligent Software Support of Cellular Adaptation to Microgravity Investigations

One of the most significant new opportunities that the Space Station affords cell biologists is the ability to do long-term cultivation of cells in the space environment. This facility is essential for investigations that are primarily focused on effects requiring a longer timeline of observation than that provided by the STS (Space Transportation System) platform. Such work requires both very strong laboratory skills to properly and quickly interact with the hardware hosting the culture and deep knowledge of the cell biology domain in order to optimally react to unanticipated scientific developments. Such work can be enabled by advanced automation techniques that have recently been used in the STS-based Spacelab, and that are being readied for the Space Station. In this paper, we describe the adaptation of PI-in-a-Box, the first interactive space science assistant system, to the study of the effects of space flight on cell cycle progression and proliferation.

Groleau, Nick↗
NASA NTRS1992

Sound transmission reduction with intelligent panel systems

Experimental and theoretical investigations are performed of the use of intelligent panel systems to control the sound transmission and radiation. An intelligent structure is defined as a structural system with integrated actuators and sensors under the guidance of an adaptive, learning type controller. The system configuration is based on the Active Structural Acoustic Control (ASAC) concept where control inputs are applied directly to the structure to minimize an error quantity related to the radiated sound field. In this case multiple piezoelectric elements are employed as sensors. The importance of optimal shape and location is demonstrated to be of the same order of influence as increasing the number of channels of control.

Fuller, Chris R.↗
NASA NTRS1989

STRUTEX: A prototype knowledge-based system for initially configuring a structure to support point loads in two dimensions

Only recently have engineers begun making use of Artificial Intelligence (AI) tools in the area of conceptual design. To continue filling this void in the design process, a prototype knowledge-based system, called STRUTEX has been developed to initially configure a structure to support point loads in two dimensions. This prototype was developed for testing the application of AI tools to conceptual design as opposed to being a testbed for new methods for improving structural analysis and optimization. This system combines numerical and symbolic processing by the computer with interactive problem solving aided by the vision of the user. How the system is constructed to interact with the user is described. Of special interest is the information flow between the knowledge base and the data base under control of the algorithmic main program. Examples of computed and refined structures are presented during the explanation of the system.

Robers, James L.↗
NASA NTRS1994

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization.

Kemeny, Sabrina E.↗
NASA NTRS1987

Space station interior noise analysis program

Documentation is provided for a microcomputer program which was developed to evaluate the effect of the vibroacoustic environment on speech communication inside a space station. The program, entitled Space Station Interior Noise Analysis Program (SSINAP), combines a Statistical Energy Analysis (SEA) prediction of sound and vibration levels within the space station with a speech intelligibility model based on the Modulation Transfer Function and the Speech Transmission Index (MTF/STI). The SEA model provides an effective analysis tool for predicting the acoustic environment based on proposed space station design. The MTF/STI model provides a method for evaluating speech communication in the relatively reverberant and potentially noisy environments that are likely to occur in space stations. The combinations of these two models provides a powerful analysis tool for optimizing the acoustic design of space stations from the point of view of speech communications. The mathematical algorithms used in SSINAP are presented to implement the SEA and MTF/STI models. An appendix provides an explanation of the operation of the program along with details of the program structure and code.

Stusnick, E.↗
NASA NTRSDate not supplied

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗
NASA NTRS2000

Variable Emissivity Through MEMS Technology

This paper discusses a new technology for variable emissivity (vari-e) radiator surfaces, which has significant advantages over traditional radiators and promises an alternative design technique for future spacecraft thermal control systems. All spacecraft rely on radiative surfaces to dissipate waste heat. These radiators have special coatings, typically with a low solar absorptivity and a high infrared-red emissivity, that are intended to optimize performance under the expected heat load and thermal sink environment. The dynamics of the heat loads and thermal environment make it a challenge to properly size the radiator and often require some means of regulating the heat rejection rate of the radiators in order to achieve proper thermal balance. Specialized thermal control coatings, which can passively or actively adjust their emissivity offer an attractive solution to these design challenges. Such systems would allow intelligent control of the rate of heat loss from a radiator in response to heat load and thermal environmental variations. Intelligent thermal control through variable emissivity systems is well suited for nano and pico spacecraft applications where large thermal fluctuations are expected due to the small thermal mass and limited electric resources. Presently there are three different types of vari-e technologies under development: Micro ElectroMechanical Systems (MEMS) louvers, Electrochromic devices, and Electrophoretic devices. This paper will describe several prototypes of micromachined (MEMS) louvers and experimental results for the emissivity variations measured on theses prototypes. It will further discuss possible actuation mechanisms and space reliability aspects for different designs. Finally, for comparison parametric evaluations of the thermal performances of the new vari-e technology and standard thermal control systems are presented in this paper.

Darrin, Ann Garrison↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS1991

On the role of exchange of power and information signals in control and stability of the human-robot interaction

A human's ability to perform physical tasks is limited, not only by his intelligence, but by his physical strength. If, in an appropriate environment, a machine's mechanical power is closely integrated with a human arm's mechanical power under the control of the human intellect, the resulting system will be superior to a loosely integrated combination of a human and a fully automated robot. Therefore, we must develop a fundamental solution to the problem of 'extending' human mechanical power. The work presented here defines 'extenders' as a class of robot manipulators worn by humans to increase human mechanical strength, while the wearer's intellect remains the central control system for manipulating the extender. The human, in physical contact with the extender, exchanges power and information signals with the extender. The aim is to determine the fundamental building blocks of an intelligent controller, a controller which allows interaction between humans and a broad class of computer-controlled machines via simultaneous exchange of both power and information signals. The prevalent trend in automation has been to physically separate the human from the machine so the human must always send information signals via an intermediary device (e.g., joystick, pushbutton, light switch). Extenders, however are perfect examples of self-powered machines that are built and controlled for the optimal exchange of power and information signals with humans. The human wearing the extender is in physical contact with the machine, so power transfer is unavoidable and information signals from the human help to control the machine. Commands are transferred to the extender via the contact forces and the EMG signals between the wearer and the extender. The extender augments human motor ability without accepting any explicit commands: it accepts the EMG signals and the contact force between the person's arm and the extender, and the extender 'translates' them into a desired position. In this unique configuration, mechanical power transfer between the human and the extender occurs because the human is pushing against the extender. The extender transfers to the human's hand, in feedback fashion, a scaled-down version of the actual external load which the extender is manipulating. This natural feedback force on the human's hand allows him to 'feel' a modified version of the external forces on the extender. The information signals from the human (e.g., EMG signals) to the computer reflect human cognitive ability, and the power transfer between the human and the machine (e.g., physical interaction) reflects human physical ability. Thus the information transfer to the machine augments cognitive ability, and the power transfer augments motor ability. These two actions are coupled through the human cognitive/motor dynamic behavior. The goal is to derive the control rules for a class of computer-controlled machines that augment human physical and cognitive abilities in certain manipulative tasks.

Kazerooni, H.↗
NASA NTRS2014

Embedded Thermal Control for Spacecraft Subsystems Miniaturization

Optimization of spacecraft size, weight and power (SWaP) resources is an explicit technical priority at Goddard Space Flight Center. Embedded Thermal Control Subsystems are a promising technology with many cross cutting NSAA, DoD and commercial applications: 1.) CubeSatSmallSat spacecraft architecture, 2.) high performance computing, 3.) On-board spacecraft electronics, 4.) Power electronics and RF arrays. The Embedded Thermal Control Subsystem technology development efforts focus on component, board and enclosure level devices that will ultimately include intelligent capabilities. The presentation will discuss electric, capillary and hybrid based hardware research and development efforts at Goddard Space Flight Center. The Embedded Thermal Control Subsystem development program consists of interrelated sub-initiatives, e.g., chip component level thermal control devices, self-sensing thermal management, advanced manufactured structures. This presentation includes technical status and progress on each of these investigations. Future sub-initiatives, technical milestones and program goals will be presented.

Thermal Control↗
NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗