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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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A ganged potentiometer with the interesting property of a constant 10k ohm or greater series impedance for all rotations of the shaft was rediscovered. The device provided a versatile passive mixer circuit when used with most signal sources and can be used as a variable series input summing resistor in operational amplifier networks. The potentiometer gave simple solutions to missing problems with a single control knob.
The software portability problem is examined from the viewpoint of experience gained in the operation of a software exchange and information center. First, the factors contributing to the program interchange to date are identified, then major problem areas remaining are noted. The import of the development of programming language and documentation standards is noted, and the program packaging procedures and dissemination practices employed by the Center to facilitate successful software transport are described. Organization, or installation, dependencies of the computing environment, often hidden from the program author, and data interchange complexities are seen as today's primary issues with dedicated processors and network communications offering an alternative solution.
Analysis of energy emitted from simple or complex cavity designs can lead to intricate solutions due to nonuniform radiosity and irradiation within a cavity. A numerical ray tracing technique was applied to simulate radiation propagating within and from various cavity designs. To obtain the energy balance relationships between isothermal and nonisothermal cavity surfaces and space, the computer code NEVADA was utilized for its statistical technique applied to numerical ray tracing. The analysis method was validated by comparing results with known theoretical and limiting solutions, and the electrical resistance network method. In general, for nonisothermal cavities the performance (apparent emissivity) is a function of cylinder length-to-diameter ratio, surface emissivity, and cylinder surface temperatures. The extent of nonisothermal conditions in a cylindrical cavity significantly affects the overall cavity performance. Results are presented over a wide range of parametric variables for use as a possible design reference.
An integration of 3-D vision systems with robot manipulators will allow robots to operate in a poorly structured environment by visually locating targets and obstacles. However, by using computer vision for objects acquisition makes the problem of overall system calibration even more difficult. Indeed, in a CAD based manipulation a control architecture has to find an accurate mapping between the 3-D Euclidean work space and a robot configuration space (joint angles). If a stereo vision is involved, then one needs to map a pair of 2-D video images directly into the robot configuration space. Neural Network approach aside, a common solution to this problem is to calibrate vision and manipulator independently, and then tie them via common mapping into the task space. In other words, both vision and robot refer to some common Absolute Euclidean Coordinate Frame via their individual mappings. This approach has two major difficulties. First a vision system has to be calibrated over the total work space. And second, the absolute frame, which is usually quite arbitrary, has to be the same with a high degree of precision for both robot and vision subsystem calibrations. The use of computer vision to allow robust fine motion manipulation in a poorly structured world which is currently in progress is described along with the preliminary results and encountered problems.
A technique is presented by which direction- and speed-tuned cells, such as those commonly found in the middle temporal region of the primate brain, can be utilized to analyze the patterns of retinal image motion that are generated during observer movement through the environment. The developed model determines heading by finding the peak response in a population of detectors or neurons each tuned to a particular heading direction. It is suggested that a complex interaction of multiple cell networks is required for the solution of the self-motion problem in the primate brain.
To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.
NOAA's program of long-term monitoring of the vertical distribution of ozone with electrochemical concentration cell (ECC) ozonesondes has undergone a number of changes over the 50-year record. In order to produce a homogenous data set, these changes must be documented and, where necessary, appropriate corrections applied. This is the first comprehensive and consistent reprocessing of NOAA's ozonesonde data records that corrects for these changes using the rawest form of the data (cell current and pump temperature) in native resolution as well as a point-by-point uncertainty calculation that is unique to each sounding. The reprocessing is carried out uniformly at all eight ozonesonde sites in NOAA's network with differences in sensing solution and ozonesonde types accounted for in the same way at all sites. The corrections used to homogenize the NOAA ozonesonde data records greatly improve the ozonesonde measurements with an average one sigma uncertainty of +/- 4-6% in the stratosphere and +/- 5-20% in the troposphere. A comparison of the integrated column ozone from the ozonesonde profile with co-located Dobson spectrophotometers total column ozone measurements shows agreement within +/- 5% for >70% of the profiles. Very good agreement is also found in the stratosphere between ozonesonde profiles and profiles retrieved from the Solar Backscatter Ultraviolet (SBUV) instruments.
Understanding the dispersion of heat around a cryogenic fluid tank, specifically the interaction between the cryogenic fluid and the tank wall is critical in the analysis of long duration cryogen storage in microgravity. The heat transfer interaction between a cryogenic storage tank and heat sources from external spacecraft structures is also one of the many factors that determine how much heat enters a tank. Recent flight experiments with two-phase fluids have indicated that local concentrations of heat input (also known as “hot spots”) can cause unwanted affects including local boiling. Computational fluid dynamic (CFD) models can provide a detailed assessment of the heat transfer occurring across a cryogenic storage system. However, CFD modeling takes time to construct and run. A simpler approach that can act as initial guidance for later CFD modeling analyzes external “hot spots” as point or finite heat sources. A radial, finite element network or a local direct solution can effectively estimate the heat spread across a cryogenic storage tank by calculating the temperature and heat load as a function of distance from the heat source. This calculation accounts for the convective heat transfer between the cryogenic fluid and storage tank surface. Similar approaches can be used to determine the effectiveness of cooling from a cryocooler as a finite, local heat sink. This approach allows for quick approximations of the thermal map across a cryogenic tank as well as sensitivity analysis under a wide range of design parameters including gravitational fields as implied through natural convection coefficients.
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.
Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.
This paper first describes a hierarchical model for very large area networks (VLAN). Space communication problems whose solution could profit by the model are discussed and then an enhanced version of this model incorporating the knowledge needed for the missile detection-destruction problem is presented. A satellite network or VLAN is a network which includes at least one satellite. Due to the complexity, a compromise between fully centralized and fully distributed network management has been adopted. Network nodes are assigned to a physically localized group, called a partition. Partitions consist of groups of cell nodes with one cell node acting as the organizer or master, called the Group Master (GM). Coordinating the group masters is a Partition Master (PM). Knowledge is also distributed hierarchically existing in at least two nodes. Each satellite node has a back-up earth node. Knowledge must be distributed in such a way so as to minimize information loss when a node fails. Thus the model is hierarchical both physically and informationally.
Provided herein are nanostructure networks having high energy storage, electrochemically active electrode materials including nanostructure networks having high energy storage, as well as electrodes and batteries including the nanostructure networks having high energy storage. According to various implementations, the nanostructure networks have high energy density as well as long cycle life. In some implementations, the nanostructure networks include a conductive network embedded with electrochemically active material. In some implementations, silicon is used as the electrochemically active material. The conductive network may be a metal network such as a copper nanostructure network. Methods of manufacturing the nanostructure networks and electrodes are provided. In some implementations, metal nanostructures can be synthesized in a solution that contains silicon powder to make a composite network structure that contains both. The metal nanostructure growth can nucleate in solution and on silicon nanostructure surfaces.
Provided herein are nanostructure networks having high energy storage, electrochemically active electrode materials including nanostructure networks having high energy storage, as well as electrodes and batteries including the nanostructure networks having high energy storage. According to various implementations, the nanostructure networks have high energy density as well as long cycle life. In some implementations, the nanostructure networks include a conductive network embedded with electrochemically active material. In some implementations, silicon is used as the electrochemically active material. The conductive network may be a metal network such as a copper nanostructure network. Methods of manufacturing the nanostructure networks and electrodes are provided. In some implementations, metal nanostructures can be synthesized in a solution that contains silicon powder to make a composite network structure that contains both. The metal nanostructure growth can nucleate in solution and on silicon nanostructure surfaces.
Quantum key distribution (QKD) provides a potent solution to securely distribute keys for two parties. However, QKD itself is vulnerable to denial of service (DoS) attacks. A flexible and resilient QKD-enabled networked microgrids (NMs) architecture is needed but does not yet exist. In this article, we present a programmable quantum NMs (PQNMs) architecture. It is a novel framework that integrates both QKD and software-defined networking (SDN) techniques capable of enabling scalable, programmable, quantum-engineered, and ultra-resilient NMs. Equipped with a software-defined adaptive post-processing approach, a two-level key pool sharing strategy and an SDN-enabled event-triggered communication scheme, these PQNMs mitigate the impact of DoS attacks through programmable post-processing and secure key sharing among QKD links, a capability unattainable using existing technologies. Through comprehensive evaluations, we validate the benefits of PQNMs and demonstrate the efficacy of the presented strategies under various circumstances. Extensive results provide insightful resources for building QKD-enabled NMs in practice.
Secure spectrum sharing or spectrum co-existence of multiple 5G networks and future 6G networks is a powerful enabler technology. The National Spectrum Strategy (NSS) published by the White House in November, 2023, and the subsequent NSS implementation plan led by the National Telecommunication and Information Administration (NTIA) is the driver of a national effort to enable co-existence of government incumbents and commercial networks in selected spectrum bands. Cellular networks such as 5G & 6G and non-cellular Wi-Fi 6E & 7 are the prominent wireless technologies considered for co-existence with incumbent wireless links. Security of the spectrum sharing solutions is a must to make this transformation of spectrum use possible, specially for mission critical communications. However, current spectrum sharing solutions rely on centralized data bases with inherent vulnerabilities. This paper focuses on secure spectrum sharing among multiple 5G networks using unlicensed and shared frequency bands. It presents an innovative AI/ML based distributed spectrum sharing approach that can be autonomously used by multiple networks. Each sharing network uses its own observation of the Radio Frequency (RF) environment, which consists of RF measurements reported from the 5G User Equipment (UE), to adjust the transmission power levels for secure co-existence. Data is presented to illustrate the superior performance of this solution compared to other spectrum sharing solutions where each network can utilize usage data of the other networks. Finally it discusses how this efficient spectrum sharing solution can evolve in the future for the 6G networks.
Reliable broadcast is an essential tool to disseminate information among a set of nodes in the presence of failures. We present a novel study of reliable broadcast in interdependent networks, in which the failures in one network may cascade to another network. In particular, we focus on the interdependency between the communication network and power grid network, where the power grid depends on the signals from the communication network for control and the communication network depends on the grid for power. In this paper, we build a resilient solution to handle crash failures in the communication network that may cause cascading failures and may even partition the network. In order to guarantee that all the correct nodes deliver the messages, we use soft links, which are inactive backup links to non-neighboring nodes that are only active when failures occur. At the core of our work is a fully distributed algorithm for the nodes to predict and collect the information of cascading failures so that soft links can be maintained to correct nodes prior to the failures. In the presence of failures, soft links are activated to guarantee message delivery and new soft links are built accordingly for long term robustness. Our evaluation results show that the algorithm achieves low packet drop rate and handles cascading failures with little overhead.
Peer-to-peer energy management systems for smart grids require developers to consider the trade-offs between the amount of communication traffic generated and the quality and speed of convergence of the control algorithms that are deployed. Employing a fully connected communication causes messages to scale exponentially with the number of nodes, while using a sparse connectivity causes less information dissemination leading to degradation of the algorithm performance. The best communication topology for a particular application lies somewhere in between and often requires empirical evaluation by application designers. Existing methods do not put focus on the needs for smart grid applications, which is information dissemination throughout the network and they do not provide a flexible solution for application developers to prototype and deploy different topologies without modifying the application code. This paper introduces a configurable virtual communication topology framework TopLinkMgr, allowing users to specify any chosen communication topology and deploy peer-to-peer applications using it. It also introduces a self-adaptive, fault-tolerant topology management algorithm, Bounded Path Dissemination that can ensure the dissemination of information to all peers within a specified threshold for a sparsely connected topology. Experiments show that the algorithm improves on convergence speed and accuracy over state-of-the-art methods and is also robust against node failures. The results indicate the possibility of achieving a close-to optimal convergence without overloading the network allowing the realization of peer-to-peer control platforms covering larger and more complex power systems.
Over the past several months, major industry vendors have made a business case for the network computer as a win-win solution toward lowering total cost of ownership. This report provides results from Phase I of the Ames Research Center network computer evaluation project. It identifies factors to be considered for determining cost of ownership; further, it examines where, when, and how network computer technology might fit in NASA's desktop computing architecture.