Mesostructures and Phase Transition of Imidazolium-Based Ionic Liquid Systems for Training Deep Neural Network Algorithm
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Results for “Networked Systems”
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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Scalable Distributed Reachability Analysis for Cyber-Physical Networked Microgrids with Communication Latency
Due to the high penetration of power-electronic-interfaced distributed energy resources (DERs), a microgrid's inertia is significantly reduced, making it sensitive to cyberand physical operational changes. When multiple microgrids are interconnected to form networked microgrids for improving system's resilience, the changes of one microgrid can quickly escalate to the overall system. In cyber-physical networked microgrids, the communication latency varies a lot. To efficiently quantify the impact of the communication latency on the dynamics of networked microgrids, a scalable distributed reachability analysis approach is presented in this paper. Reachable sets are calculated for each microgrid and then composited for the overall system dynamics evaluation. Extensive tests in the full paper will demonstrate the influence of communication latency on system dynamics and validate reachable sets can bound a system's all dynamic trajectories subject to latency changes. The test results also offer an insight into designing and managing the communication network for enhancing the system's resilience.
Li, Yan↗
Malcolm Deployment Guide for Solar Power Generation Plants
This guide provides detailed instructions for deploying Malcolm in Solar Power Generation systems. It covers the deployment process, from understanding the network architecture of these systems to configuring network switches and Switched Port Analyzer (SPAN) ports or mirror ports or TAPs. The guide also includes best practices for deploying Hedgehog sensors, another critical component in these systems. Following this guide, users can enhance network visibility, improve their system’s security, and effectively troubleshoot common issues.
14 SOLAR ENERGY↗
Roadmap for Advancement of Low-Voltage Secondary Distribution Network Protection
Downtown low-voltage (LV) distribution networks are generally protected with network protectors that detect faults by restricting reverse power flow out of the network. This creates protection challenges for protecting the system as new smart grid technologies and distributed generation are installed. This report summarizes well-established methods for the control and protection of LV secondary network systems and spot networks, including operating features of network relays. Some current challenges and findings are presented from interviews with three utilities, PHI PEPCO, Oncor Energy Delivery, and Consolidated Edison Company of New York. Opportunities for technical exploration are presented with an assessment of the importance or value and the difficulty or cost. Finally, this leads to some recommendations for research to improve protection in secondary networks.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization
This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.
42 ENGINEERING↗
Space Network Ground Segment Sustainment (SGSS) Project: Developing a COTS-Intensive Ground System
Purpose of the Space Network Ground Segment Sustainment (SGSS) is to implement a new modern ground segment that will enable the NASA Space Network (SN) to deliver high quality services to the SN community for the future The key SGSS Goals: (1) Re-engineer the SN ground segment (2) Enable cost efficiencies in the operability and maintainability of the broader SN.
Saylor, Richard↗
Switching systems
Fault diagnosibility and test point development for digital networks and switching systems
Finkelstein, H.↗
The General-Use Nodal Network Solver (GUNNS) Modeling Package for Space Vehicle Flow System Simulation
The General-Use Nodal Network Solver (GUNNS) is a modeling software package that combines nodal analysis and the hydraulic-electric analogy to simulate fluid, electrical, and thermal flow systems. GUNNS is developed by L-3 Communications under the TS21 (Training Systems for the 21st Century) project for NASA Johnson Space Center (JSC), primarily for use in space vehicle training simulators at JSC. It has sufficient compactness and fidelity to model the fluid, electrical, and thermal aspects of space vehicles in real-time simulations running on commodity workstations, for vehicle crew and flight controller training. It has a reusable and flexible component and system design, and a Graphical User Interface (GUI), providing capability for rapid GUI-based simulator development, ease of maintenance, and associated cost savings. GUNNS is optimized for NASA's Trick simulation environment, but can be run independently of Trick.
Harvey, Jason↗
Software defined grid energy storage
Today, consumer battery installations are isolated, physical devices. Virtual power plants (VPPs) allow consumer devices to aggregate for grid services, but they are are vertically integrated, vendor controlled systems (e.g., Tesla’s VPP). Consumer batteries are therefore unable to participate in energy markets or other grid services outside what their vendor provides. We describe a software system that provides software control of multiple, networked battery energy storage systems in the electric grid. The system introduces two new ideas that enable flexible and dependable management of energy storage. The first is a virtual battery, which can either partition a battery or aggregate multiple batteries. The second is a reservation-based API which allows asynchronous control of batteries to meet contractual guarantees in a safe and dependable manner. Virtual batteries and a reservation-based API address the unique challenges of achieving high and efficient utilization of energy storage systems, including heterogeneity of battery systems such as varying C-rates, participation in energy markets, utility bill management systems, community resource sharing, and reliability. Using a testbed comprised of sonnen Inc. storage units installed in several homes and a lab, we demonstrate that virtualized batteries can seamlessly replace physical batteries, flexibly manage energy storage resources, isolate multiple clients using a shared battery, and create new energy storage applications.
25 ENERGY STORAGE↗
TrioSim: A Lightweight Simulator for Large-Scale DNN Workloads on Multi-GPU Systems
Deep Neural Networks (DNNs) have become increasingly capable of performing tasks ranging from image recognition to content generation. The training and inference of DNNs heavily rely on GPUs, as GPUs' massively parallel architecture delivers extremely high computing capability. With the growing complexity of DNNs and the size of training datasets, training DNNs with a large number of GPUs is becoming a prevalent strategy. Researchers have been exploring how to design software and hardware systems for GPU farms to achieve the best utilization, efficiency, and DNN accuracy during training or inference. However, when designing and deploying such systems, designers usually rely on testing on physical hardware platforms equipped with many GPUs, incurring high costs that are almost prohibitive for system designers to test different configurations and designs, even for highly resourceful companies. While an alternative solution is to test on GPU simulators, they are often too slow for these l
Li, Ying [William & Mary, Williamsburg, VA, USA] (↗
Modeling Air Traffic Management Technologies with a Queuing Network Model of the National Airspace System
This report describes an integrated model of air traffic management (ATM) tools under development in two National Aeronautics and Space Administration (NASA) programs -Terminal Area Productivity (TAP) and Advanced Air Transport Technologies (AATT). The model is made by adjusting parameters of LMINET, a queuing network model of the National Airspace System (NAS), which the Logistics Management Institute (LMI) developed for NASA. Operating LMINET with models of various combinations of TAP and AATT will give quantitative information about the effects of the tools on operations of the NAS. The costs of delays under different scenarios are calculated. An extension of Air Carrier Investment Model (ACIM) under ASAC developed by the Institute for NASA maps the technologies' impacts on NASA operations into cross-comparable benefits estimates for technologies and sets of technologies.
Long, Dou↗
Method for Assessment of Security-Relevant Settings in Anomaly-Based Intrusion Detection for Industrial Control Systems
Ensuring the integrity of Ethernet-based networks is a challenging and constantly evolving domain. This problem is exacerbated for those operational technology (OT) networks supporting industrial control systems (ICS) since much of that equipment was originally designed to be on a network that was isolated and generally considered free of malefactors. Increasing pressure to bridge these systems with traditional information technology (IT) networks has introduced a bevy of new threats. In response, both academia and industry have responded with security solutions tailored to ICS environments. Deploying these protection systems often involves several configuration choices. While some of these choices are clear (e.g., block/enable protocol X) others are far more subjective (e.g. alert threshold == 3.43). Further complicating the situation, while often similar to IT networks, OT networks have unique challenges and characteristics that make the task of protecting them simultaneously more difficult and straight forward.Extant solutions for quantifying the relative security of intrusion detection systems fail to effectively support the operators of said systems with understanding the impact of various configuration changes. Further, they assume that the attacks are static and not subject to manipulation or alteration in the face of defenses. In this paper, we present a threat-based method for quantifying the relative impact of various security settings for intrusion detection systems (IDSs) within ICS environments. This method provides operational staff with a clear understanding of the relative impact of their settings and assumes that the attacks levied against them are dynamic. The model is described in detail, we apply the model to a synthetic data set, and discuss the inferences that can be made and what types of decisions they could be used to support.
Gillen, Rob↗
The Sensitivity Analysis of Coil Misalignment for a 200-kW Dynamic Wireless Power Transfer System with an LCC-S and LCC-P Compensation
The dynamic wireless power transfer systems may reduce the battery size of electric vehicles while maintaining the travel range. Similar to the stationary wireless power transfer systems, the compensation networks are desired in dynamic wireless charging systems at the primary and secondary sides to reduce the reactive power requirement from the source and improve the efficiency. Consequently, it is essential to understand the behavior of the compensation networks, especially the sensitivity to misalignments. This paper presents a sensitivity analysis of LCC-S and LCC-P compensation networks for a 200-kW power transfer with variations in the coupling coefficient due to the electric vehicle travel and misalignments between the pads. The sensitivity study combines the electromagnetic finite element analysis and circuit analysis to determine the impact of misalignments on the wireless power transfer system characteristics. The theoretical analysis is verified by conducting circuit simulations at discrete points to verify the accuracy of the mathematical model.
Kavimandan, Utkarsh↗
Overview of High-Power Wireless Charging Systems and Analysis of Polyphase Wireless Charging System Phase Winding and Resonant Tuning Network Connection Configurations
In spite of the high energy efficiency and environmental benefits of electric vehicles (EVs), adoption rates are increasing at a relatively slow pace, primarily because of EVs’ limited range and long charging durations. To reduce EV charging times to be comparable to refueling times of conventional vehicles, extreme fast charging systems are required. Such charging systems would reduce drivers’ range anxiety and enable long-distance interstate travel with EVs. Here, this substantial target in charging rates with 15–20 minutes of recharging times, requires research and development from grid to batteries with advanced charging systems. Wireless power transfer systems for EV charging applications are flexible, convenient, and highly efficient, and they allow automated charging. This paper reviews the power electronics and winding and resonant tuning network configurations for polyphase wireless power transfer systems for high-power wireless charging applications.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multi‐Objective Urban Observational Strategies: A Risk‐Based Framework for Expanding Flood Sensor Networks
In coupled human and natural systems, developing an observation strategy which maximizes insight into both the natural system and the human system is a challenging multi-objective optimization problem. In this article, we describe the expansion of a flood risk observation system in Southeast Texas designed to improve our understanding of both physical and socioeconomic exposure to hydrological hazards at fine spatial scales, in the context of a structured hazard-exposure-vulnerability risk framework. We describe a new approach for assessing the spatial extent through which a flood sensor's observations can be assumed to be relevant, and estimate the population served within each sensor's area of information using downscaled socio-demographic data. As hydrological observations and modeling move to ever finer scale, assessing the information they contain in the context of both social and natural systems becomes increasingly important for developing actionable scientific insights.
54 ENVIRONMENTAL SCIENCES↗
System Identification of X-33 Neural Network
Modern flight control research has improved spacecraft survivability as its goal. To this end we need to have a failure detection system on board. In case the spacecraft is performing imperfectly, reconfiguration of control is needed. For that purpose we need to have parameter identification of spacecraft dynamics. Parameter identification of a system is called system identification. We treat the system as a black box which receives some inputs that lead to some outputs. The question is: what kind of parameters for a particular black box can correlate the observed inputs and outputs? Can these parameters help us to predict the outputs for a new given set of inputs? This is the basic problem of system identification. The X33 was supposed to have the onboard capability of evaluating the current performance and if needed to take the corrective measures to adapt to desired performance. The X33 is comprised of both rocket and aircraft vehicle design characteristics and requires, in general, analytical methods for evaluating its flight performance. Its flight consists of four phases: ascent, transition, entry and TAEM (Terminal Area Energy Management). It spends about 200 seconds in ascent phase, reaching an altitude of about 180,000 feet and a speed of about 10 to 15 Mach. During the transition phase which lasts only about 30 seconds, its altitude may increase to about 190,000 feet but its speed is reduced to about 9 Mach. At the beginning of this phase, the Main Engine is Cut Off (MECO) and the control is reconfigured with the help of aerosurfaces (four elevons, two flaps and two rudders) and reaction control system (RCS). The entry phase brings down the altitude of X33 to about 90,000 feet and its speed to about Mach 3. It spends about 250 seconds in this phase. Main engine is still cut off and the vehicle is controlled by complex maneuvers of aerosurfaces. The last phase TAEM lasts for about 450 seconds and the altitude and speed, both are reduced to zero. The present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.
Aggarwal, Shiv↗
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems
This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.
deep neural network↗
Flight Test Design and Implementation for Airspace Independent Surveillance Through a Distributed Ground Based Sensor Network
The paper presents a system architecture for distributed sensing, networking and computing, its hardware implementation, and execution of initial flight experiments to validate theoretical findings. It induces development of distributed sensing requirements, framework, and architecture, development of distributed ground node hardware prototypes, integration of all nodes and testing of baseline functionalities, integration of in-house developed perception, migration and tracking software packages, establishing flight scenario and flyable path for a selected UAS, flying the air vehicle along the path, recording sensors measurements, pre-processing them and transferring the resulting data to an optimal computing center. It also addresses the challenges related to pre-flight hardware calibration, clock synchronization, sensor registration and establishing a communication network. Sensors data processing results demonstrate the functionality of the presented distributed architecture and satisfactory performance of the applied technologies.
Target tracking↗