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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
NASA NTRS1992

Network issues for large mass storage requirements

File Servers and Supercomputing environments need high performance networks to balance the I/O requirements seen in today's demanding computing scenarios. UltraNet is one solution which permits both high aggregate transfer rates and high task-to-task transfer rates as demonstrated in actual tests. UltraNet provides this capability as both a Server-to-Server and Server-to-Client access network giving the supercomputing center the following advantages highest performance Transport Level connections (to 40 MBytes/sec effective rates); matches the throughput of the emerging high performance disk technologies, such as RAID, parallel head transfer devices and software striping; supports standard network and file system applications using SOCKET's based application program interface such as FTP, rcp, rdump, etc.; supports access to the Network File System (NFS) and LARGE aggregate bandwidth for large NFS usage; provides access to a distributed, hierarchical data server capability using DISCOS UniTree product; supports file server solutions available from multiple vendors, including Cray, Convex, Alliant, FPS, IBM, and others.

Perdue, James↗
NASA NTRS1996

Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.

Alexander, June↗
NASA NTRS1990

High-order neural models for error correcting code

The decoding and error-correction of data transmitted over a noisy channel is in principle equivalent to the operation of a neural network performing as a content-addressable memory. For a successful application, however, the neural network has to be capable of storing arbitrary words, and it has to be guaranteed that the stored words represent the only stable attractors of the memory. This paper presents a novel high-order neural network architecture that has these characteristics. The analog nature of the network can be used to perform soft-decision decoding with any block code. The performance in terms of postdecoding bit error rate versus SNR is demonstrated for two exemplary block codes. The comparison with a conventional decoding algorithm for a (15,5) cyclic redundancy code shows, for example, that the bit error rate at 7dB SNR can be decreased by two orders of magnitude.

Jeffries, Clark↗
DOE OSTI2023

Nuclear explosion monitoring network design considerations

Design of an efficient monitoring network requires information on the type and size of releases to be detected, the accuracy and reliability of the measuring equipment, and the desired network performance. This work provides a scientific basis for optimizing or minimizing networks of 133 Xe samplers to achieve a desired performance level for different levels of release. The approach of this work varies the density of sampling locations to find optimal location subsets, and to explore the properties of variations of those subsets – how crucial is a specific subset; are substitutions problematic? The choice of possible station locations is arbitrary but constrained to some extent by the location of islands, land masses, difficult topography (mountains, etc.) and the places where infrastructure exists to run and support a sampler. Performance is evaluated using hypothetical releases and atmospheric transport models that cover an entire year.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1999

Cross-Sensor Calibration of the GAI Long Range Detection Network

The long range component of the North American Lightning Detection Network has been providing experimental data products since July 1996, offering cloud-to-ground lightning coverage throughout the Atlantic and Western Pacific oceans, as well as south to the Intertropical Convergence Zone. The network experiences a strong decrease in detection efficiency with range, which is also significantly modulated by differential propagation under day, night and terminator-crossing conditions. A climatological comparison of total lightning data observed by the Optical Transient Detector (OTD) and CG lightning observed by the long range network is conducted, with strict quality control and allowance for differential network performance before and after the activation of the Canadian Lightning Detection Network. This yields a first-order geographic estimate of long range network detection efficiency and its spatial variability. Intercomparisons are also performed over the continental US, allowing large scale estimates of the midlatitude climatological IC:CG ratio and its possible dependence on latitude.

Boccippio, Dennis J.↗
DOE OSTI2020

Network packet templating for GPU-initiated communication

Systems, apparatuses, and methods for performing network packet templating for graphics processing unit (GPU)-initiated communication are disclosed. A central processing unit (CPU) creates a network packet according to a template and populates a first subset of fields of the network packet with static data. Next, the CPU stores the network packet in a memory. A GPU initiates execution of a kernel and detects a network communication request within the kernel and prior to the kernel completing execution. Responsive to this determination, the GPU populates a second subset of fields of the network packet with runtime data. Then, the GPU generates a notification that the network packet is ready to be processed. A network interface controller (NIC) processes the network packet using data retrieved from the first subset of fields and from the second subset of fields responsive to detecting the notification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep Learning At Depth: Estimating subsurface parameters from geophysical monitoring data

Geophysical imaging techniques are a non-invasive way to image the subsurface and understand both subsurface solid (rock/soil) and fluid property distributions and their evolution in time. Inversions of the geophysical data, such as Electrical Resistance Tomography (ERT) data, are solved to estimate the subsurface property distributions, such as conductivity, and many inversion techniques smooth out sharp gradients in rock or fluid property distributions. Sharp gradients in subsurface properties tend to be present in situations with complex subsurface structures, which are common in many subsurface applications. We have successfully demonstrated that it is possible to inform, or constrain, inversions with neural networks trained on synthetic data with complex subsurface structures. Initial results suggest this process may be optimizable to yield property distributions that better represent the true property distributions than the same inversion process without the neural network constraint. Future work would optimize the neural network performance for this application and then apply the synthetic-data trained neural network to real data to understand the utility and performance of this technique for real data sets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Network traffic control for multi-homed end-hosts via SDN

Software-defined networking (SDN) is an emerging technology of efficiently controlling and managing computer networks, such as in data centres, wide-area networks, as well as in ubiquitous communication. In this study, the authors explore the idea of embedding the SDN components, represented by SDN controller and virtual switch, in end-hosts to improve network performance. In particular, the authors consider load balancing across multiple network interfaces on end-hosts with different link capacity scenarios. The authors have explored and implemented different SDN-based load-balancing approaches based on OpenFlow software switches, and have demonstrated the feasibility and the potential of this approach. The proposed system has been evaluated with MultiPath transmission control protocol (MPTCP). Furthermore, the proposed results demonstrated the potential of applying the SDN concepts on multi-homed devices resulting in an increase in achieved throughput of 55% compared to the legacy single network approach and 10% compared to the MPTCP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nuclear Physics Network Requirements Review Report

The Energy Sciences Network (ESnet) is the Office of Science’s high-performance network user facility, delivering highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the U.S. Department of Energy (DOE) science mission by connecting each and every DOE lab and its user facilities. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) Program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet connects DOE national laboratories, user facilities, and major experiments so scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large data sets, and access distributed data repositories. While ESnet provides network connectivity, it cannot be characterized as an internet service provider as it is specifically built to provide a range of network services that are tailored to meet the unique requirements of DOE’s data-intensive science.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Neural network architecture for crossbar switch control

A Hopfield neural network architecture for the real-time control of a crossbar switch for switching packets at maximum throughput is proposed. The network performance and processing time are derived from a numerical simulation of the transitions of the neural network. A method is proposed to optimize electronic component parameters and synaptic connections, and it is fully illustrated by the computer simulation of a VLSI implementation of 4 x 4 neural net controller. The extension to larger size crossbars is demonstrated through the simulation of an 8 x 8 crossbar switch controller, where the performance of the neural computation is discussed in relation to electronic noise and inhomogeneities of network components.

Troudet, Terry P.↗
DOE OSTI2022

The Portals 4.3 Network Programming Interface

This report presents a specification for the Portals 4 network programming interface. Portals 4 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded systems. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is targeted to the next generation of machines employing advanced network interface architectures that support enhanced offload capabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

NASA Deep Space Network operating control

The primary function of the Deep Space Network (DSN) is to provide effective and reliable tracking and data acquisition for planetary and interplanetary space flight missions. This involves providing data to flight project mission operations, accepting commands from mission operations and transmitting the commands to stations and spacecraft, and providing a record of telemetry and command data to mission operations. Also included are network performance monitoring, the generation of predictions for antenna pointing and signal acquisition, network scheduling, and network validation tests. Descriptions are given of the three facilities and six systems of the DSN. Also described are interfaces, automation and standardized procedures, and discrepancy reporting. It is pointed out that the greatest challenge facing the DSN is the implementation of NASA's Network Consolidation Program, which is scheduled to be completed in 1986. The objectives of this program are enumerated.

Weisman, W. D.↗
NASA NTRSDate not supplied

Urban Aerial Mobility Networks using Amphibious Vertical Takeoff and Landing Vehicles

This study considers novel urban aerial mobility (UAM) networks that address some of the key operator and community acceptance challenges inherent in proposed UAM operations. This paper seeks to examine three possible approaches to improve operator and community acceptance – specifically targeting cost, safety, and noise. First, the conceptual design space will be examined for VTOL amphibious vehicle technologies, including the implications of onwater versus in-flight time/speed. Second, the implications of minimizing community overflights by flying over-water will be examined. Third, the implications of the partial use of onwater, or near-shore/littoral, vertiports on UAM network performance will be examined. This study considers an alternate design trade space for urban aerial mobility systems. A large number of cities in the United States are located near or surround large bodies of water. Many of these same cities are considered to be candidates for metropolitan aerial transportation systems so as to moderate the impact of urban ground-transportation congestion. This paper will expand discussion and study of notional amphibious VTOL vehicles. There is nothing particularly new with regards to helicopters with amphibious takeoff and landing capability. For example, light rotorcraft have been outfitted with pontoons since the 1950s. Larger utility helicopters – used for carrying offshore oil-rig crew – have been designed, with varying degrees of success, to emergency land in rough waters in case of onboard mechanical system failures. The unique difference for the proposed amphibious UAM vehicles, as compared to these earlier amphibious rotorcraft, is that water operation is the norm rather than the exception and that, further, the water-born operation (and design) of such vehicles can be optimized to yield significant economic and operational benefits over solely UAM land-based stations and operations. Various different amphibious UAM networks will be discussed. A first-order set of analyses – employing novel mission metrics – is presented in this paper that will examine the operational performance of these amphibious networks. In particular, amphibious operations might address critical safety and community acceptance issues. An examination of the aerodynamic and hydrodynamic characteristics of amphibious VTOL UAM vehicles will be presented in this paper. One possible conceptual design for an amphibious UAM vehicle is a hybrid synchropter/hydroplane vehicle. As interest in urban aerial mobility grows, it is worthwhile to consider whether or not amphibious vertical takeoff and landing vehicles can play an important role in providing such mobility.

Urban Aerial Mobility↗
NASA NTRS1994

Robotic tape library system level testing at NSA: Present and planned

In the present of declining Defense budgets, increased pressure has been placed on the DOD to utilize Commercial Off the Shelf (COTS) solutions to incrementally solve a wide variety of our computer processing requirements. With the rapid growth in processing power, significant expansion of high performance networking, and the increased complexity of applications data sets, the requirement for high performance, large capacity, reliable and secure, and most of all affordable robotic tape storage libraries has greatly increased. Additionally, the migration to a heterogeneous, distributed computing environment has further complicated the problem. With today's open system compute servers approaching yesterday's supercomputer capabilities, the need for affordable, reliable secure Mass Storage Systems (MSS) has taken on an ever increasing importance to our processing center's ability to satisfy operational mission requirements. To that end, NSA has established an in-house capability to acquire, test, and evaluate COTS products. Its goal is to qualify a set of COTS MSS libraries, thereby achieving a modicum of standardization for robotic tape libraries which can satisfy our low, medium, and high performance file and volume serving requirements. In addition, NSA has established relations with other Government Agencies to complete this in-house effort and to maximize our research, testing, and evaluation work. While the preponderance of the effort is focused at the high end of the storage ladder, considerable effort will be extended this year and next at the server class or mid range storage systems.

Shields, Michael F.↗
DOE OSTI2022

Systems and methods for quality of service (QoS) based management of bottlenecks and flows in networks

Techniques based on the Theory of Bottleneck Ordering can reveal the bottleneck structure of a network, and the Theory of Flow ordering can take advantage of the revealed bottleneck structure to manage and configure network flows so as to improve the overall network performance. These two techniques provide insights into the inherent topological properties of a network at least in three areas: (1) identification of the regions of influence of each bottleneck; (2) the order in which bottlenecks (and flows traversing them) may converge to their steady state transmission rates in distributed congestion control algorithms; and (3) the design of optimized traffic engineering policies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗
NASA NTRS1994

Congestion Avoidance Testbed Experiments

DARTnet provides an excellent environment for executing networking experiments. Since the network is private and spans the continental United States, it gives researchers a great opportunity to test network behavior under controlled conditions. However, this opportunity is not available very often, and therefore a support environment for such testing is lacking. To help remedy this situation, part of SRI's effort in this project was devoted to advancing the state of the art in the techniques used for benchmarking network performance. The second objective of SRI's effort in this project was to advance networking technology in the area of traffic control, and to test our ideas on DARTnet, using the tools we developed to improve benchmarking networks. Networks are becoming more common and are being used by more and more people. The applications, such as multimedia conferencing and distributed simulations, are also placing greater demand on the resources the networks provide. Hence, new mechanisms for traffic control must be created to enable their networks to serve the needs of their users. SRI's objective, therefore, was to investigate a new queueing and scheduling approach that will help to meet the needs of a large, diverse user population in a "fair" way.

Denny, Barbara A.↗