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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 ground station network tracked a high altitude spacecraft which in turn tracked a low orbiting satellite. Orbit data are relayed back to the ground stations. A refraction study was performed on this configuration to compute ionospheric and tropospheric refraction effects along the satellite and ground links.
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit.
Abstract The Earth mover’s distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error.
The valve monitoring system is a stand alone unit with network capabilities for integration into a higher level health management system. The system is designed for aiding in failure predictions of high-geared ball valves and linearly actuated valves. It performs data tracking and archiving for identifying degraded performance. The data collection types are cryogenic cycles, total cycles, inlet temperature, body temperature torsional strain, linear bonnet strain, preload position, total travel and total directional changes. Events are recorded and time stamped in accordance with the IRIG B True Time. The monitoring system is designed for use in a Class 1 Division II explosive environment. The basic configuration consists of several instrumentation sensor units and a base station. The sensor units are self contained microprocessor controlled and remotely mountable in three by three by two inches. Each unit is potted in a fire retardant substance without any cavities and limited to low operating power for maintaining safe operation in a hydrogen environment. The units are temperature monitored to safeguard against operation outside temperature limitations. Each contains 902-928 MHz band digital transmitters which meet Federal Communication Commission's requirements and are limited to a 35 foot transmission radius for preserving data security. The base-station controller correlates data from the sensor units and generates data event logs on a compact flash memory module for database uploading. The entries are also broadcast over an Ethernet network. Nitrogen purged National Electrical Manufactures Association (NEMA) Class 4 enclosures are used to house the base-station
Safety issues associated with transporting crude oil by rail have been a concern since the boom of the U.S. domestic shale oil production in 2012. During the last decade, over 300 crude-oil-by-rail incidents have occurred in the United States. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on crude-on-rail routes and their associated risks is available to the public. To this end, this study proposed an unconventional way to reconstruct crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method linked the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments that those crude oil trains were traveling on. Here, a shortest path-based method was applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their directional information. Validation of the inferred routes was performed using a public map and official crude oil incident data. The results suggested that the inferred routes based on geotagged photos had high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through several metropolitan areas of high population density, who were exposed to potential risk. These findings could improve situational awareness for policy makers and transportation planners. In addition, with the inferred routes, this study has established a good foundation for future crude oil train risk-analyses along the rail route.
This paper describes the modeling and simulation of the communication network for the NASA Advanced Solid Rocket Motor (ASRM) facility under construction at Yellow Creek near Luka, Mississippi. Manufacturing, testing, and operations at the ASRM site will be performed in different buildings scattered over an 1800 acre site. These buildings are interconnected through a local area network (LAN), which will contain one logical Fiber Distributed Data Interface (FDDI) ring acting as a backbone for the whole complex. The network contains approximately 700 multi-vendor workstations, 22 multi-vendor workcells, and 3 VAX clusters interconnected via Ethernet and FDDI. The different devices produce appreciably different traffic patterns, each pattern will be highly variable, and some patterns will be very bursty. Most traffic is between the VAX clusters and the other devices. Comdisco's Block Oriented Network Simulator (BONeS) has been used for network simulation. The two primary evaluation parameters used to judge the expected network performance are throughput and delay.
Highlights: • Astrocytes display highly complex mitochondrial architectures in vivo. • Mitochondrial trafficking and dynamics in astrocytes are regulated. • Mitochondria critically contribute to astrocyte Ca{sup 2+} handling. • Astrocyte reacting to injury undergo marked changes in mitochondrial dynamics. Mitochondria are increasingly recognized for playing important roles in regulating the evolving metabolic state of mammalian cells. This is particularly true for nerve cells, as dysregulation of mitochondrial dynamics is invariably associated with a number of neuropathies. Accumulating evidence now reveals that changes in mitochondrial dynamics and structure may play equally important roles also in the cell biology of astroglial cells. Astroglial cells display significant heterogeneity in their morphology and specialized functions across different brain regions, however besides fundamental differences they seem to share a surprisingly complex meshwork of mitochondria, which is highly suggestive of tightly regulated mechanisms that contribute to maintain this unique architecture. Here, we summarize recent work performed in astrocytes in situ indicating that this may indeed be the case, with astrocytic mitochondrial networks shown to experience rapid dynamic changes in response to defined external cues. Although the mechanisms underlying this degree of mitochondrial re-shaping are far from being understood, recent data suggest that they may contribute to demarcate astrocyte territories undergoing key signalling and metabolic functions.
With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.
Abstract Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.
An analysis of upper air winds was performed to define the wind environment at potential operating altitudes for high-altitude powered platform concepts. Expected wind conditions of the contiguous United States, Pacific area (Alaska to Sea of Japan), and European area (Norwegian and Mediterranean Seas) were obtained using a representative network of sites selected based upon adequate high-altitude sampling, geographic dispersion, and observed upper wind patterns. A data base of twenty plus years of rawinsonde gathered wind information was used in the analysis. Annual variations from surface to 10 mb (approximately 31 km) pressure altitude were investigated to encompass the practical operating range for the platform concepts. Parametric analysis for the United States and foreign areas was performed to provide a basis for vehicle system design tradeoffs. This analysis of wind magnitudes indicates the feasibility of annual operation at a majority of sites and more selective seasonal operation for the extreme conditions between the pressure altitudes of 100 to 25 mb based upon the assumed design speeds.
Circuit voltage output is reduced by voltage equivalent to ampere resistance drop of nonlogarithmic resistances of practical diodes; therefore, output is same as circuit using ideal diode. Circuit applies to meter and electronic recorder movements, and improves performance of radiation detectors and several microbiological monitoring devices
Signal acquisition and data transmission for innovative sensor systems and networks inside the containment of nuclear power plants (NPPs) is still a challenge with respect to safety, performance, reliability, availability, and costs. This especially applies to equipment upgrades for existing plants, special measurements, but also for new builds. This paper presents a novel method for efficient and cost-effective sensor signal acquisition and data transmission via power lines, in order to cope with the disadvantages of common system architectures that often suffer from poor signal integrity due to raw data transmissions via long cables, huge efforts and costs for installation, and low flexibility with respect to maintenance and upgrades. A transmitter-receiver architecture is proposed that allows multiplexing of multiple sensor inputs for unidirectional point-to-point transmission by superimposing information signals on existing AC or DC supply lines, but also on active and inactive sensor wires, or spare cables, using power line communication (PLC) technology. Based on commercial off-the-shelf (COTS) electronic parts, a radiation hard transmitter hardware is designed to operate in harsh environment within the containment during full plant operation. The system's modular approach allows application specific trade-offs between redundancy and throughput regarding data transmission, as well as various sensor input front-ends which are compatible with state of the art systems. PLC technology eliminates the need for costly installation of additional cables and wall penetrations, while providing a complementary and diverse communication technology for upgrades of existing systems. At the receiver side in low dose areas, signals are extracted from the power line, demodulated, and de-multiplexed, in order to regain the original sensor signal information and provide it either in analog or digital output format. Successful laboratory qualification tests, field trails, and a demonstrator setup installation within a NPP verify the overall performance of the proposed system. (authors)
To impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.
Proton-exchange-membrane water electrolysis (PEMWE) requires acidic oxygen-evolution-reaction (OER) anodes that combine high activity, high durability, and low Ir loading. Here, we report a Ti-Zr composite electrospun oxide (ESO) nanorod support that enables ultralow-Ir anodes for high-performance PEMWE. Zr-containing Ti oxide heterostructures stabilize anatase-rich TiO2, tune the local oxygen-coordination environment, and strengthen interfacial anchoring of IrOx under acidic anodic conditions. The electrospun nanorod network further creates an open, mechanically coherent catalyst layer that improves Ir utilization, ionomer penetration, and mass transport. At an anode loading of 0.2 mgIr cm-2, the optimized Ir/TiZr20-ESO anode delivers a PEMWE mass activity of 0.99 A mgIr-1 at 1.45 V, 28.3 and 43.0 times higher than commercial Ir black and commercial IrO2/TiO2, respectively. The same anode reaches 3.0 and 4.0 A cm-2 at 1.75 and 1.83 V, respectively, and sustains 2000 h operation at 2.0 A cm-2. Also, accelerated stress tests up to 525 hours over 31,500 cycles confirm promising long-term durability, with an insignificant performance decay of 0.4 μV per cycle. Density functional theory indicates that the Ti-Zr oxide heterostructure suppresses Ti demetallation and strengthens IrO2 interfacial binding, rationalizing the improved high-current-density stability.
Cyber-physical systems and electric utilities significantly depend on the reliability and efficiency of information and operational technology. However, false data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and reliable operation of modern power systems. Here, to mitigate this problem, a rapid monitoring and defense approach is proposed to defend against cyber attacks. Initially, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. Within the TFCN, the advances are that both time and frequency domain information can be fused without extra spectrum analysis methods, and can save detection time to speed the calculation efficiency using the developed time-frequency block. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system according to the feedback detection results. The advances of this strategy are that different control strategies can be automatically selected to recover the stability to the greatest extent according to the detected attacks. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57% accuracy, outperforming both traditional and some advanced neural networks. Importantly, the defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which illustrates profound performance in faster stability restoration.
The scheduler is a crucial component of the multipath transmission control protocol (MPTCP) that dictates the path that a data packet takes. Schedulers are in charge of delivering data packets in the right order to prevent delays caused by head-of-line blocking. The modern Internet is a complicated network whose characteristics change in real-time. MPTCP schedulers are supposed to understand the real-time properties of the underlying network, such as latency, path loss, and capacity, in order to make appropriate scheduling decisions. However, the present scheduler does not take into account all of these characteristics together, resulting in lower performance. We present the low latency and high data rate (LLHD) scheduler, which successfully makes scheduling decisions based on real-time information on latency, path loss, and capacity, and achieves around 25% higher throughput and 45% lower data transmission delay than Linux’s default MPTCP scheduler.
The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of to the high-level trigger farm. The DAQ architecture is based on state-of-the-art network technologies for the event building. For the data concentration, 10/40 Gbit/s Ethernet technologies are used together with a reduced TCP/IP protocol implemented in FPGA for a reliable transport between custom electronics and commercial computing hardware. A 56 Gbit/s Infiniband FDR Clos network has been chosen for the event builder. This paper presents the implementation and performance of the event-building system.
In this paper, we propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transport in fractured media. DFN models are used to simulate flow and transport in subsurface fractured rock with low-permeability. One method to alleviate the heavy computational overhead associated with these simulations is to reduce the size of the DFN using a graph representation of it to identify the primary flow sub-network and only simulate flow and transport thereon. The first of these methods used unweighted graphs constructed solely on DFN topology and could be used for accurate predictions of first-passage times. However, these techniques perform poorly when predicting later stages of the mass breakthrough. We utilize a weighted-graph representation of the DFN where edge weights are based on hydrological parameters in the DFN that allows us to exploit the kinematic quantities derivable a posteriori from the flow solution obtained on the graph representation of the DFN to perform system reduction and predict the later stages of the breakthrough curve with high fidelity. We also propose and demonstrate the use of an adaptive pruning algorithm with error control that produces a pruned DFN sub-network whose predicted mass breakthrough agrees with the original DFN within a user-specified tolerance. Lastly, the method allows for the level of accuracy to be a user-controlled parameter.