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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Resilient Hierarchical Networked Control Systems: Secure Controls for Critical Locations and at Edge

Integration of information and communication technology (ICT) offers new opportunities in improving the management and operation of critical infrastructures such as power systems as it allows connection of different sensors and control components via a communication network, leading to the so-called networked control systems (NCS). However, the use of open and pervasive ICT such as the Internet or wireless communication technologies comes at a price of making NCS vulnerable to cyber intrusions/attacks which may cause physical damage. Here, this chapter presents control algorithms to ensure resilient and safe operation of NCS under unknown cyberattacks. Specifically, a variant of dynamic watermarking strategies is presented by embedding encoding/decoding components of chaotic signals into the NCS for secure control for critical locations where the measurement/control signals are transmitted to/from the control center via a communication network. In addition, resilient cooperative control algorithms are discussed to ensure safe operation at edge of the NCS which consists of a large number of distributed controllable devices. Several numerical examples are provided to illustrate the proposed control strategies.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1992

The Deep Space Network information system in the year 2000

The Deep Space Network (DSN), the largest, most sensitive scientific communications and radio navigation network in the world, is considered. Focus is made on the telemetry processing, monitor and control, and ground data transport architectures of the DSN ground information system envisioned for the year 2000. The telemetry architecture will be unified from the front-end area to the end user. It will provide highly automated monitor and control of the DSN, automated configuration of support activities, and a vastly improved human interface. Automated decision support systems will be in place for DSN resource management, performance analysis, fault diagnosis, and contingency management.

Markley, R. W.↗
NASA NTRS1993

Network command processing system overview

The Network Command Processing System (NCPS) developed for the National Aeronautics and Space Administration (NASA) Ground Network (GN) stations is a spacecraft command system utilizing a MULTIBUS I/68030 microprocessor. This system was developed and implemented at ground stations worldwide to provide a Project Operations Control Center (POCC) with command capability for support of spacecraft operations such as the LANDSAT, Shuttle, Tracking and Data Relay Satellite, and Nimbus-7. The NCPS consolidates multiple modulation schemes for supporting various manned/unmanned orbital platforms. The NCPS interacts with the POCC and a local operator to process configuration requests, generate modulated uplink sequences, and inform users of the ground command link status. This paper presents the system functional description, hardware description, and the software design.

Nam, Yon-Woo↗
NASA NTRS1988

Network Queueing System

Program directs traffic in UNIX-based network. NQS provides facilities for remote queueing, request routing, remote status, queue-access controls, batch-request resource-quota limits, and remote output return. Written in C.

Kingsbury, Brent↗
NASA NTRS1990

The AMSC network control system

The American Mobile Satellite Corporation (AMSC) is going to construct, launch, and operate a satellite system in order to provide mobile satellite services to the United States. AMSC is going to build, own, and operate a Network Control System (NCS) for managing the communications usage of the satellites, and to control circuit switched access between mobile earth terminals and feeder-link earth stations. An overview of the major NCS functional and performance requirements, the control system physical architecture, and the logical architecture is provided.

Garner, William B.↗
DOE OSTI2023

Redox-enabled electronic interrogation and feedback control of hierarchical and networked biological systems

Abstract Microelectronic devices can directly communicate with biology, as electronic information can be transmitted via redox reactions within biological systems. By engineering biology’s native redox networks, we enable electronic interrogation and control of biological systems at several hierarchical levels: proteins, cells, and cell consortia. First, electro-biofabrication facilitates on-device biological component assembly. Then, electrode-actuated redox data transmission and redox-linked synthetic biology allows programming of enzyme activity and closed-loop electrogenetic control of cellular function. Specifically, horseradish peroxidase is assembled onto interdigitated electrodes where electrode-generated hydrogen peroxide controls its activity. E. coli ’s stress response regulon, oxyRS , is rewired to enable algorithm-based feedback control of gene expression, including an eCRISPR module that switches cell-cell quorum sensing communication from one autoinducer to another—creating an electronically controlled ‘bilingual’ cell. Then, these disparate redox-guided devices are wirelessly connected, enabling real-time communication and user-based control. We suggest these methodologies will help us to better understand and develop sophisticated control for biology.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Human Systems Risk Network - A Ranking Analysis of Risks

INTRODUCTION The Human Systems Risk Board (HSRB) is responsible for understanding, managing, and mitigating the risks associated with spaceflight. For a particular mission, the HSRB assigns each human system risk a rating on a 5x5 grid assessing its likelihood and consequence, which is ultimately used to compare and rank the risks. The HSRB approaches risk management by primarily establishing the context of each human system risk individually with the understanding that mitigating one risk might affect the likelihood, consequence, and mitigation approaches of another. To support this effort the HSRB, subject matter experts, and risk custodian teams created directed acyclic graphs (DAG), often called a causal graph, for the twenty-nine risks. In this presentation, we propose a new ranking algorithm for the risks which includes the downstream influence of each risk according to the information in the DAGs and provide an application of graph theoretic tools. METHODS In 2014, Mindock and Klaus proposed a taxonomy for human system risk influences which we have adopted to categorize the nodes in each DAG. Analyzing the nodes that correspond to the risks in this taxonomy allows us to analyze and understand how each risk influences the others. We construct an auxiliary network, which we call the Primary Risk Network (PRN), where the nodes are the twenty-nine space flight risks and, a directed edge connects Risk A to Risk B if Risk A has some influence on the likelihood or consequence of Risk B as described in the DAGS. We perform a variety of graph theoretic ranking methods on the nodes (or risks) in the PRN, including Katz centrality. RESULTS We rank the nodes in the PRN using the Katz centrality score. The ten risks with the highest score are pictured in Figure 1, colored (light to dark) according to their score. We analyze other centrality measures like betweenness centrality, eigenvector centrality, and the Estrada index, and provide the meaning of the corresponding rankings in terms of the risks. Future work includes analyzing the other categories in the taxonomy defined by Mindock and Klaus [1]. For example, we are interested in analyzing the nodes that are labeled as countermeasures or capabilities and perform similar analysis to measure their effect on certain medical conditions.

dag↗
NASA NTRS1993

The Test Analysis Retrieval System (TARS): Meeting the challenges of the network's test processes

The Networks Systems Test Section (GSFC 531.4) is responsible for managing a variety of engineering and operational tests used to assess the status of the Network elements relative to readiness certification for new and ongoing mission support and for performance trending. To conduct analysis of data collected during these tests, to disseminate and share the information, and to catalog and create reports based on the analysis is currently a cumbersome and inefficient task due primarily to the manual handling of paper products and the inability to easily exchange information between the various Networks elements. The Test Analysis and Retrieval System (TARS) is being implemented to promote concise data analysis, intelligible reporting of test results, to minimize test duplication by fostering a broad sharing of test data, and perhaps most importantly, to provide significantly improved response to the Network's internal and external customers. This paper outlines the intended application, architecture, and benefits of the TARS.

Stelmaszek, Robert L.↗
NASA NTRS1988

Advanced information processing system: Input/output network management software

The purpose of this document is to provide the software requirements and specifications for the Input/Output Network Management Services for the Advanced Information Processing System. This introduction and overview section is provided to briefly outline the overall architecture and software requirements of the AIPS system before discussing the details of the design requirements and specifications of the AIPS I/O Network Management software. A brief overview of the AIPS architecture followed by a more detailed description of the network architecture.

Nagle, Gail↗
NASA NTRS1980

Some queuing network models of computer systems

Queuing network models of a computer system operating with a single workload type are presented. Program algorithms are adapted for use on the Texas Instruments SR-52 programmable calculator. By slightly altering the algorithm to process the G and H matrices row by row instead of column by column, six devices and an unlimited job/terminal population could be handled on the SR-52. Techniques are also introduced for handling a simple load dependent server and for studying interactive systems with fixed multiprogramming limits.

Herndon, E. S.↗
DOE OSTI2020

On the Robustness of Network Community Structure Under Addition of Edges

Communities represent important functional modules in networked systems. A key goal in preserving such communities is understanding their robustness under perturbations. Previous research has studied the impact of node removals and edge removals on the community structure. However, the impact of edge additions on the robustness of the community structure is relatively unknown. Edge additions or false positive edges may simulate measurement errors or external exceptional events that threaten the functionality of networked systems. Here, we study the impact of edge additions on the community structure using Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks. We show that, for a fixed network size, the impact of edge additions is greater on networks with initially weak community structure than on networks with strongly clustered structures. In addition, we find that the perception of the impact is also dependent on the community detection algorithm used to uncover communities. In particular, we found that modularity-based methods such as Leiden and Louvain are less affected than information-theoretic and message passing-based methods such as Infomap and Label Propagation. Our results demonstrate that edge addition can (a) significantly impact the community structure of networks based on their initial conditions, and (b) the perception of the impact is dependent on the community detection algorithm used. We describe limitations, open challenges, and how this methodology can inform the design of resilient networked systems under edge additions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Peer-to-peer communication control for resilient operations of networked cyberphysical systems

This report includes two main accomplishments of the peer-to-peer communication control for resilient operation of networked microgrids project in FY24, which include a scheme for cyberattack-aware coordination of networked microgrids for supporting voltages of bulk power systems and a scheme for price signal-based operations of EV-rich networked microgrids with mixed ownership. First, the cyberattack-aware scheme enables networked microgrids to distributedly determine the amount of reactive power injection to support the voltage of bulk power system (BPS) in a fair manner. In this scheme, a risk-informed algorithm is presented to generate the peer-to- peer (P2P) communication graph with minimal risk of attack on communication links. To deal with cyberattacks on MG controllers, the resilient consensus algorithm (CA) is utilized for MG controllers to robustly estimate the total reactive power headroom, from which the MGs can accurately provide the needed amount of reactive power injection for supporting the voltage of BPS. The CA implementation and performance within the P2P communication framework are demonstrated on the IEEE 39-bus system with 6 microgrids contained in the distribution feeder under different cyberattack scenarios. Second, the price-based scheme enables the usage of the real-time price signal for the operations of electric vehicle (EV)-rich networked-microgrids with mixed ownership, in which not all the microgrids can communicate with the distribution system operator (DSO). In this scheme, a max consensus is introduced to enable the real-time price signal to be propagated from the DSO to all the microgrids, from which each microgrid controller will manage the DERs to balance the load demand and the power injection from the EV charging stations within its microgrid. Numerical results over one day with 288 slots of 5-minute intervals on the modified 123-node test feeder including 3 microgrids with high penetration of EV are presented to evaluate how the price signal affects the operations of networked microgrids under different charging strategies of the EV charging stations. The result indicates that our proposed EVCS (dis)charging strategy, which leverages the flexibility of EVs to support the grid through discharging during peak demand, proves to be a cost-effective solution that reduces operational costs while improving the social welfare of EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
NASA NTRSDate not supplied

Integrating Low-Cost Sensor Systems and Networks to Enhance Air Quality Applications

Recent advances in low-cost air quality sensor systems are rapidly increasing the accessibility of air quality information around the world. At the same time, there are many technical challenges to appropriately and effectively using of the information they provide. A key opportunity to increase the applicability and actionability of low-cost sensor data is to use these devices at network scale and combine their information with insights from other air quality data sources such as numerical models and satellite remote sensing. When the limitations of low-cost sensors are understood and acknowledged, and appropriate complementary data are used to overcome these limitations, low cost sensors can support a variety of applications such as improving location-specific air quality forecasting and estimation, quantifying local source impacts, identifying air quality disparities, assessing the benefits of mitigation actions, and promoting community engagement with air quality issues. This presentation will first provide an overview and summarize key findings from a recently released World Meteorological Organization report discussing how networks of low-cost air quality sensor systems can be integrated to effectively support such applications. The presentation will also give a brief overview of an ongoing NASA-funded effort to create accessible tools for integrating multiple sources of air quality information, including global forecasting models, satellite remote sensing data, and in-situ information from both regulatory and low-cost monitors.

Carl Malings↗
NASA NTRS2005

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Alexandrov, Natalia↗