Network Science for Human Systems Risks: From Insight to Action on Risk of Renal Stone Formation
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Network Analysis↗
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Optimal Feedback Control of Thermal Networks
An improved approach to the mathematical modeling of feedback control of thermal networks has been devised. Heretofore software for feedback control of thermal networks has been developed by time-consuming trial-and-error methods that depend on engineers expertise. In contrast, the present approach is a systematic means of developing algorithms for feedback control that is optimal in the sense that it combines performance with low cost of implementation. An additional advantage of the present approach is that a thermal engineer need not be expert in control theory. Thermal networks are lumped-parameter approximations used to represent complex thermal systems. Thermal networks are closely related to electrical networks commonly represented by lumped-parameter circuit diagrams. Like such electrical circuits, thermal networks are mathematically modeled by systems of differential-algebraic equations (DAEs) that is, ordinary differential equations subject to a set of algebraic constraints. In the present approach, emphasis is placed on applications in which thermal networks are subject to constant disturbances and, therefore, integral control action is necessary to obtain steady-state responses. The mathematical development of the present approach begins with the derivation of optimal integral-control laws via minimization of an appropriate cost functional that involves augmented state vectors. Subsequently, classical variational arguments provide optimality conditions in the form of the Hamiltonian equations for the standard linear-quadratic-regulator (LQR) problem. These equations are reduced to an algebraic Riccati equation (ARE) with respect to the augmented state vector. The solution of the ARE leads to the direct computation of the optimal proportional- and integral-feedback control gains. In cases of very complex networks, large numbers of state variables make it difficult to implement optimal controllers in the manner described in the preceding paragraph.
Papalexandris, Miltiadis↗
Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks
Abstract We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks to use less than 9 min of GNSS data to forecast the full tsunami waveforms over 6 hr at select locations, and obtain accurate forecasts on a test data set. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.
Rim, Donsub↗
Method and system for pattern analysis using a coarse-coded neural network
A method and system for performing pattern analysis with a neural network coarse-coding a pattern to be analyzed so as to form a plurality of sub-patterns collectively defined by data. Each of the sub-patterns comprises sets of pattern data. The neural network includes a plurality fields, each field being associated with one of the sub-patterns so as to receive the sub-pattern data therefrom. Training and testing by the neural network then proceeds in the usual way, with one modification: the transfer function thresholds the value obtained from summing the weighted products of each field over all sub-patterns associated with each pattern being analyzed by the system.
Spirkovska, Liljana↗
Thermodynamic Modeling of Complex Solid Solutions in the Lu-H-N System via Graph Neural Network Accelerated Monte Carlo Simulations
Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures, e.g., 𝑂(10 2 ) GPa, usually treat them as stoichiometric compounds without considering interstitial lattice disorder. As pressures become more moderate in the 𝑂(10 0 ) GPa and below range, hydrogen disorder at interstitial lattice sites becomes prominent, e.g., in the N-doped Lu hydride that was recently claimed superconducting near 1 GPa. Further adding compositional complexity from alloying and/or multielement interstitial occupation makes elucidating pressure- and temperature-dependent observables intractable by first-principles calculations alone. We therefore propose a lattice graph neural-network surrogate modeling approach to predict configuration- and pressure-dependent equation-of-state properties. Their efficiency permits Monte Carlo simulations to calculate Gibbs energies and pressure-dependent phase diagrams, thereby revealing insights into the synthesis conditions required for achieving desired phase equilibria. We demonstrate this concept for the compositionally complex cubic Lu(H,N,Va) 3 system where three constituents (hydrogen, nitrogen and vacancy) have disordered multielement interstitial occupancies and insights into pressure-dependent phase equilibria are critically needed, e.g., N-doping levels can significantly lower dehydrogenation temperatures and provide a new strategy to optimize hydrogen-storage alloys. This work can improve the thermodynamic understanding of the Lu-H-N system and help rational synthesis of N-doped Lu hydrides, but more generally demonstrates an efficient approach to model pressure-dependent thermodynamics of multicomponent solid solutions.
Monte Carlo methods↗
Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network
Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (𝑁), plunge force (𝐹𝑧), and welding speed (𝑉) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (𝑁,𝑉,𝐹𝑧,𝐸𝐹𝐼) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.
Mohammad W Dewan↗
DSN telemetry system Mark 3-77
A description of the Deep Space Network Telemetry System, Mark III-77, and the recent improvements are provided. Telemetry functions and performance are identified.
Gatz, E. C.↗
Reference clock parameters for digital communications systems applications
The basic parameters relevant to the design of network timing systems describe the random and systematic time departures of the system elements, i.e., master (or reference) clocks, transmission links, and other clocks controlled over the links. The quantitative relations between these parameters were established and illustrated by means of numerical examples based on available measured data. The examples were limited to a simple PLL control system but the analysis can eventually be applied to more sophisticated systems at the cost of increased computational effort.
Kartaschoff, P.↗
SS focused technology: Gateways and NOS's
The extensions and enhancements of the fiber optic data bus technology supported by the Space Station Focused Technology Program are discussed. This includes the operating software for the network called the Network Operating System (NOS); gateways and bridges for multiple network topologies; and very large scale topology implimentations to shrink the size and power of the Bus Interface Unit (BIU) down to more manageable dimensions. CMOS is being investigated for the lower speed (parallel) logic. Gallium arsendide is being studied for the high speed (serial) logic.
Hartenstein, R.↗
The Telecommunications and Data Acquisition Report
Deep Space Network and Systems topics addressed include: tracking and ground-base navigation; communications, spacecraft-ground; station control and system technology; capabilities for existing projects; and network upgrading and sustaining.
Posner, E. C.↗
Integration between terrestrial-based and satellite-based land mobile communications systems
A survey is given of several approaches to improving the performance and marketability of mobile satellite systems (MSS). The provision of voice/data services in the future regional European Land Mobile Satellite System (LMSS), network integration between the Digital Cellular Mobile System (GSM) and LMSS, the identification of critical areas for the implementation of integrated GSM/LMSS areas, space segment scenarios, LMSS for digital trunked private mobile radio (PMR) services, and code division multiple access (CDMA) techniques for a terrestrial/satellite system are covered.
Arcidiancono, Antonio↗
Data archiving
The viewgraphs of a discussion on data archiving presented at the National Space Science Data Center (NSSDC) Mass Storage Workshop is included. The mass storage system at the National Center for Atmospheric Research (NCAR) is described. Topics covered in the presentation include product goals, data library systems (DLS), client system commands, networks, archival devices, DLS features, client application systems, multiple mass storage devices, and system growth.
Pitts, David↗
Fuzzy and neural control
Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.
Berenji, Hamid R.↗
A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications
Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.
Gourisetti, Sri, Nikhil Gupta (ORCID:0000000188778↗
Semi-2-interpenetrating polymer networks of high temperature systems
A semi-interpenetrating (semi-IPN) polymer system of the semi-2-IPN type is described in which a polymer of acetylene-terminated imidesulfone (ATPISO2) is cross linked in the presence of polyimidesulfone (PISO2). Six different formulations obtained by mixing of either ATPISO2-1n or ATPISO2-3n with PISO2 in three different proportions were characterized in terms of glass transition temperature, thermooxidative stability, inherent viscosity, and dynamic mechanical properties. Adhesive (lap shear) strength was tested at elevated temperatures on aged samples of adhesive scrim cloth prepared from each resin. Woven graphite (Celion 1000)/polyimide composites were tested for flexural strength, flexural modulus, and shear strength. The network polymers have properties intermediate between those of the component polymers alone, have greatly improved processability over either polyimide, and are able to form good adhesive bonds and composites, making the semi-2-IPN systems superior materials for aerospace structures.
Hanky, A. O.↗
Security Enhancements for Distributed Energy Resource Systems Interconnected with Distribution Networks: Final Technical Report
The revised IEEE 1547 Standard defines new complex communication-adjustable voltage and frequency regulation and ride-through characteristics, while maintaining the general antiislanding requirement for unintentional islanding situations. Unintentional islanding is prohibited while intentional islanding is specifically allowed, thus effectively enabling microgrid operation mode. Further, IEEE 1547-2018 Standard introduces new requirements in terms of interoperability so that the DER plant/circuit segments may be seamlessly integrated with the utility networks but at the same time become vulnerable to a cyber-attack. Traditional cybersecurity measures including encryption, authentication and role-based access control may not be fully implementable to all communication protocols specified in the IEEE 1547 Standard. Therefore, in this project we have identified, researched, implemented and tested several cyberphysical approaches that rely mostly on the behavior of the DER circuit and may help with validating the incoming command and control action potentially coming through an insecure communications channel. Additionally, we have built semantic models and communications profiles for DER facilities and have implemented lightweight IEC 61850 based publisher-subscriber GOOSE messaging mechanism, with security extensions in terms of authentication and encryption. The project proposed information models for integration into UCA OpenFMB 2.0 profiles focusing on grid code compliance.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Marshall Space Flight Center Ground Systems Development and Integration
Ground Systems Development and Integration performs a variety of tasks in support of the Mission Operations Laboratory (MOL) and other Center and Agency projects. These tasks include various systems engineering processes such as performing system requirements development, system architecture design, integration, verification and validation, software development, and sustaining engineering of mission operations systems that has evolved the Huntsville Operations Support Center (HOSC) into a leader in remote operations for current and future NASA space projects. The group is also responsible for developing and managing telemetry and command configuration and calibration databases. Personnel are responsible for maintaining and enhancing their disciplinary skills in the areas of project management, software engineering, software development, software process improvement, telecommunications, networking, and systems management. Domain expertise in the ground systems area is also maintained and includes detailed proficiency in the areas of real-time telemetry systems, command systems, voice, video, data networks, and mission planning systems.
Wade, Gina↗
Modeling Data Flows with Network Calculus in Cyber-Physical Systems: Enabling Feature Analysis for Anomaly Detection Applications
The electric grid is becoming increasingly cyber-physical with the addition of smart technologies, new communication interfaces, and automated grid-support functions. Because of this, it is no longer sufficient to only study the physical system dynamics, but the cyber system must also be monitored as well to examine cyber-physical interactions and effects on the overall system. To address this gap for both operational and security needs, cyber-physical situational awareness is needed to monitor the system to detect any faults or malicious activity. Techniques and models to understand the physical system (the power system operation) exist, but methods to study the cyber system are needed, which can assist in understanding how the network traffic and changes to network conditions affect applications such as data analysis, intrusion detection systems (IDS), and anomaly detection. In this paper, we examine and develop models of data flows in communication networks of cyber-physical systems (CPSs) and explore how network calculus can be utilized to develop those models for CPSs, with a focus on anomaly and intrusion detection. This provides a foundation for methods to examine how changes to behavior in the CPS can be modeled and for investigating cyber effects in CPSs in anomaly detection applications.
97 MATHEMATICS AND COMPUTING↗