Large-scale Complex Systems Research Articles

Dual risk-aversion programming for regional industrial structure adjustment with water-energy nexus: A case study of Tianjin, China.

The water-energy nexus (WEN) system is a large-scale complex system that comes with diverse forms of risks owing to many challenges in the process of maintaining economic-resource-environmental sustainability. First, the rapidly increasing demand for water and energy subjects many regions to the high risk of water and energy shortages. Second, decision makers face difficulties in weighing system benefits and loss risks under a series of stricter water-energy policies. To handle the aforementioned dual risks of WEN, in this study we propose copula-based stochastic downside risk-aversion programming (CSDP) for regional water-energy management. CSDP integrates the superiority of the copula analysis method and downside risk-aversion programming into a framework, which can not only reveal the risk interactions between water resources and energy demand by using copula functions under different probability distributions, even previously unknown correlations, but also control economic risk, tackle systemic uncertainties, and provide an effective linkage between system stability and conflicting economic benefits. The proposed model was applied to a water-energy system case study in Tianjin City, China. Optimal solutions for various water resources and energy demand copulas associated with different scenarios and hierarchical risk levels were examined in the CSDP model. The results showed that water resources have a greater influence than energy on industrial structure adjustment in Tianjin, with consequent effects on system benefits, optimal output value schemes, and environmental protection strategies. In addition, the tertiary industry provides a new opportunity for economic growth based on a large amount of water-energy consumption, and its potential resources and water-air pollution risks also deserve extensive attention.

Component-Based Reduced Order Modeling of Large-Scale Complex Systems

Large-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena. Characterizing the operation and performance of such systems requires detailed computational models. Even with advances in modern computational capabilities, however, high-fidelity (e.g., large eddy) simulations of such a system remain out of reach. In this work, we develop a reduced‐order modeling framework to enable accurate predictions of large-scale systems. We target engineering systems which are difficult to simulate at a high-enough level of fidelity, but are decomposable into different components. These components can be modeled using a combination of strategies, such as reduced-order models (ROM) or reduced-fidelity full-order models (RF-FOM). Component-based training strategies are developed to construct ROMs for each individual component. These ROMs are then integrated to represent the full system. Notably, this approach only requires high-fidelity simulations of a much smaller computational domain. System-level responses are mimicked via external boundary forcing during training. Model reduction is accomplished using model-form preserving least-squares projections with variable transformation (MP-LSVT) (Huang et al., Journal of Computational Physics, 2022, 448: 110742). Predictive capabilities are greatly enhanced by developing adaptive bases which are locally linear in time. The trained ROMs are then coupled and integrated into the framework to model the full large-scale system. We apply the methodology to extremely complex flow physics involving combustion dynamics. With the use of the adaptive basis, the framework is demonstrated to accurately predict local pressure oscillations, time-averaged and RMS fields of target state variables, even with geometric changes.

Identifying Key Information on Life Cycle of Engineering Data by Graph Convolutional Networks and Data Mining

Engineering data, including product data-conversion networks and software dependency networks, are very important for the long-term preservation of product information. With the explosive growth of data in recent years, product information has become increasingly diversified and complex, which poses new challenges to the long-term preservation of product data. A better understanding of the functions of complex networks can help us take more effective measures to maintain and control such complex systems, and then adopt more effective methods to achieve life cycle management. It is currently difficult for traditional heuristic methods to deal with such large-scale complex systems. In recent years, however, the use of graph neural networks to identify key nodes attracted widespread attention, but this requires a large amount of training data. It is difficult to obtain large-scale relational data and establish identification models in engineering fields. Combining a graph convolution network with a data-mining method, a key node identification method in a graph convolution network based on data mining is proposed. The method first determines the type of complex network according to the power-law distribution and centrality of the network and then uses the corresponding evolutionary model to generate a large-scale synthetic network to effectively train the model. The experimental results from two real networks show that this method improves the identification performance of key nodes by using synthetic data with the same characteristics as the real network, and provides a new perspective for product life cycle management.

ASSESSMENT OF RISK FOR THE INTEGRATION OF RENEWABLE ENERGY SOURCES INTO THE ELECTRICITY SUPPLY SYSTEM

The work is devoted to the development of a methodology that allows combining qualitative and quantitative assessments of different risk factors to make an integrated decision when renewable energy sources connect to the electricity supply system. The integration of quantitative information and qualitatively presented knowledge through the creation of appropriate algorithms can significantly expand the possibilities of risk analysis of renewable energy sources, established energy systems and justification of measures to manage them.
 The key features of large-scale and complex systems are the diversity and interdependence of the elements included in them, the large number of decision-makers in conditions of high uncertainty and the uniqueness of these objects. Therefore, the study describes the methods and approaches that can be used to analyze the risks of complex systems. In conditions of high uncertainty and complexity of large-scale systems, many solutions can be obtained by using qualitative assessments based on the knowledge and experience of experts.
 Maintaining the reliability, stability and efficiency of the electrical system is becoming a difficult issue for distribution networks with variable energy resources. Further movement in the direction of increasing the share of solar power plants (SPP) in the generation of the power system is a great challenge for effective management of the distribution network and a serious threat to its normal functioning.
 The article identifies the main factors that lead to the problems of integration of SPP. Also, the probability of occurrence and the level of possible consequences of the implementation of each of the identified risk factors were assessed by using the method of expert assessments.
 According to the results of the study, the main problems in integrating SPP into the power supply system are increasing shunting power, deteriorating electricity quality, the need to modernize lines, increasing debt on the "green" tariff, higher harmonics and reactive power flow.

Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades, there is still a lack of progress in information systems dynamics (ISD), owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems. The value of ISD lies in its ability to guide the design, development, application, and evaluation of large-scale information system-of-systems (SoSs), just as mechanical dynamics theories guide mechanical systems engineering. This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space, improving a mathematical theory for information measurement, and proposing a dynamic configuration model for information systems. In this way, it establishes a basic theoretical framework for ISD. The proposed theoretical methodologies have been successfully applied and verified in the Smart Court SoSs Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization. The proposed ISD provides an innovative paradigm for the analysis, design, development, and evaluation of large-scale complex information systems, such as electronic government and smart cities.

An arbitrary-order continuous sliding mode control technique for nonlinear PWR-type nuclear power plants

This paper presents an arbitrary order continuous-time sliding mode controller based on the super-twisting algorithm for a nonlinear pressurized water nuclear power plant. A proportional-derivative terminal sliding surface is designed to achieve the finite time convergence and to enhanced the tracking performance. The proposed controller is chattering free, which is always preferable in most of the practical applications and, it is robust against Lipschitz in time uncertainties. The implementation of the proposed controller requires only the information about the system output, and thus, it is most suitable for large scale complex systems, such as nuclear power plants. Superiority of the proposed controller over some conventional control techniques in the presence of uncertainties is shown with the help of simulation results in the MATLAB/Simulink environment.

Predictive maintenance for cyber physical systems using neural network based on deep soft sensor and industrial internet of things

Soft sensors are software libraries or techniques that can estimate status as well as predict important performance indicators. MVA (multivariate analysis) and ML methods have been developed in data-driven monitoring as well as fault detection method. Here this research proposes supervisory Just-in-time neural network (SJITNN) based CPS monitoring and predictive maintenance integrated with partial least squares key indicator (PLSKI) in linear systems and large scale complex systems which mitigate noise, complexity and improve the network robustness, accuracy of predictive maintenance, RMSE, recall, F-1 score and precision. Furthermore, by allowing user to design analysis chains themselves, framework presents a user-friendly predictive maintenance method. Aside from this, framework is based on containerization techniques to make platform versatile, durable, and scalable in a variety of production situations. The experimental results shows that the proposed technique obtained accuracy of 91.8%, precision of 87.6%, recall of 88%, F-1 score of 72% and RMSE of 50%.

Multiobjective Genetic Algorithm for Class Testing using OCL Class Contract Specifications: A Framework

It has been a software trend to build large-scale complex systems with high reliability. Due to the size of the software and the dynamic requirements of the stakeholders, it becomes hard to test those software systems manually. This may lead the software to fatal failures and cause irrecoverable catastrophic damage. To be safe, the software system must be investigated thoroughly before it is too late. Test sequence generation for Unified Modeling Language (UML) class models from their semiformal Object Constraint Language specifications can be helpful in identifying the defects in the early phase of the software life cycle. The existing approaches suffer from inherent problems of exhaustive exploration of finite state machines (infeasible paths, exponential number of test sequences, and uncertainty of completion of testing). Evolutionary algorithms can greatly help by optimizing the test sequences to get optimal coverage, minimal cost, and higher quality. The proposed approach helps us to improve the testing of Unified Modeling Language (UML) model-based software, by testing the conformance to semiformal class operation contract specifications (specified in the form of Object Management Group (OMG) standard and Object Constraint Language (OCL) semiformal language). The presented research achieved two main goals: (1) automation of testing process and conformance to standards of the current technique of test sequence generation, bridging the gap between the research and industry; (2) improvement in the state of the art approach through the application of multiobjective genetic algorithms (MOGAs). A case study along with the results achieved through the proposed technique is presented as well, clearly reflecting the significance of the proposed research.

Multi-model D-vine copula regression model with vine copula-based dependence description

Soft sensor modeling is a popular method to predict the key variables that are not easy to measure in chemical processes. Since the actual process is often a large-scale, multi-working condition, nonlinear, and non-Gaussian complex system, a single soft sensor model cannot fully extract all the information of the process, and the prediction accuracy is relatively low. To address this problem, a multi-model d-vine copula regression model with vine copula-based dependence description (VCDD-MCR) is proposed in this paper. This method first divides the training samples into multiple subsets with repetitive samples, and uses the vine copula-based dependence description (VCDD) model to characterize the distributions of these subsets. Then, a generalized local probability (GLP) index is used to determine the location of every training sample among these distributions. If a training sample is at the edges of all distributions, a new subset centered on this sample will be created. Furthermore, a d-vine copula regression model is established for each subset to predict the key variable. The proposed method can handle large-scale, nonlinear, non-Gaussian systems well. The effectiveness of the proposed method is demonstrated using a numerical example and an industrial example.

Cascading-Failures Effect on Heterogeneous Internet of Things Systems under Targeted Selective Attack

With the rapid development of the Internet of Things (IoT), the physical system and the network space are further deeply integrated, forming a larger-scale IoT heterogeneous fusion system. The attack mode considered in the security mechanism research of traditional large-scale complex systems is relatively simple; only simple attack types such as random attacks on physical systems or network systems are considered. In addition, existing attack modalities such as selectivity, locality, and distribution cannot fully consider the characteristics of security threats in the IoT system. In this paper, for large-scale heterogeneous IoT system scenarios, attackers can attack network systems or physical systems through cyberspace. We conduct situational awareness analysis on important traffic nodes or backbone nodes and study the cascading failures of two interdependent heterogeneous space systems. In view of the existence of such targeted attack threats in large-scale IoT heterogeneous systems, we focus on security assessment and risk prediction issues. First, this paper analyzes and models different IoT heterogeneous systems. Then using the penetration theory, we analyze the cascading failure process step by step and obtain the critical threshold for system collapse failure. Finally, we further verify the correctness of the theoretical values through simulation to effectively analyze and illustrate the reliability of the parameters affecting the system risk. The experimental results show that the large-scale IoT heterogeneous system presents a first-order discontinuous transition value near the critical threshold and the power-law index of the SF network has little effect on the system security.

Trusted Data Storage Architecture for National Infrastructure.

National infrastructure is a material engineering facility that provides public services for social production and residents’ lives, and a large-scale complex device or system is used to ensure normal social and economic activities. Due to the problems of difficult data collection, long project period, complex data, poor security, difficult traceability and data intercommunication, the archives management of most national infrastructure is still in the pre-information era. To solve these problems, this paper proposes a trusted data storage architecture for national infrastructure based on blockchain. This consists of real-time collection of national infrastructure construction data through sensors and other Internet of Things devices, conversion of heterogeneous data source data into a unified format according to specific business flows, and timely storage of data in the blockchain to ensure data security and persistence. Knowledge extraction of data stored in the chain and the data of multiple regions or fields are jointly modeled through federal learning. The parameters and results are stored in the chain, and the information of each node is shared to solve the problem of data intercommunication.

Improved Design and Thermo-Mechanical Verification of Deflection Magnet Beam Collimator of EAST-NBI System

The deflection magnet (DM) is the most important component of the Neutral Beam Injection (NBI) system of Experimental Advanced Superconducting Tokamak (EAST), which can magnetically deflect the un-neutralized charged particles after the neutralized process of the beam is extracted from the ion source, and then form a neutral beam injected into the tokamak. Under the operating conditions of the NBI system, by using the thermocouple monitoring system in the experiment, it can be found that the currently operating DM beam collimator has a quite high temperature rise. It is necessary to redesign the DM beam collimator to improve its heat transfer performance. The parallel arrangement of multiple rows of tubes is proposed as the basic method for the redesign of the beam collimator of DM, the thermal-fluid-structure analysis model of this redesign model is established and its temperature field, pressure field and stress field are analyzed. Taking the surface temperature of the beam collimator, the overall dimension after the total tube combination and the pressure drop of the whole structure of collimator as the optimization objectives, and setting the fluid velocity, the tube’s inner diameter and the number of tube rows as the design variables, the optimized design scheme of the beam collimator structure is obtained. From the results of simulation, the new structure can better meet the operation requirements of DM, and its maximum temperature rise is well controlled, which is expected to meet the long pulse operation requirements of the NBI system. The proposed simulation and design optimization method can provide a certain reference for the design and optimization of other high-heat-flux structures in complex large-scale neutral beam systems in the future.

Distributed Estimation with Decentralized Control for Quadruple-Tank Process

This paper proposes the design of quadruple-tank process due to the unique multivariable MIMO system under minimum and non-minimum scenario with respect to the valve ratio. This model is then implemented the distributed estimation algorithm with decentralized control. The inputs are set in divergent gains of pumps while the four-tank process is interconnected so that the stability properties are different, making the usage of decentralized control is reasonable. The number of outputs is designed the same as those of inputs which are also that of distributed Luenberger observer with the continuous linearized dynamical system. This distributed comprises local estimates only in certain output, meaning that it would lead to insufficiency so that the neighbouring links under some network topologies are required in the dynamical system. This concept fortunately works in two different characteristic stability of the tank process regarding estimating the states. This success leads to the further research of the more large-scale dynamic complex system.

A fitness sharing based ant clustering method for multimodal optimization of the aircraft longitudinal automatic carrier landing system

With the flight envelope becoming larger and larger, the Automatic Carrier Landing System (ACLS) is becoming a complex large-scale system, and the corresponding control parameter design has been the most important key link to ensure the safety and flight quality of aircraft carrier landing missions. In this paper, a Fitness Sharing based Ant Clustering (FSAC) method is presented for use in multimodal optimization of the longitudinal ACLS and to reveal the hidden properties of the solution space. In doing so, first, the lattice rule based space sampling strategy is used to create the individual ant sequences. Then a fading memory fitness sharing function is applied to modify the clustering strategy and regroup the ants for multimodal optimization of the search space. An adaptive learning strategy is developed to dynamically adjust the search scope of the ant colony. Moreover, an online health monitor is used to replace the weak ants with new ones in a timely manner in order to keep robustness of the FSAC. Finally, the multimodal feasible solutions which are good candidates for the ACLS design are presented and the characteristics of the solution space are also analyzed. The result shows that many large solution sets are located at the bottom of the solution space, whereas on the upper side of the solution space, the number of feasible solutions decreases sharply. An F/A-18 model is used as a test bed and the simulations are carried out in various wind conditions to demonstrate the effectiveness and feasibility of the proposed method.

Real-Time Event-Driven Learning in Highly Volatile Systems: A Case for Embedded Machine Learning for SCADA Systems

Extracting key system parameters and their impact on state transition is a necessity for knowledge and data engineering. In Decision Support Systems, the quest for yet more efficient and faster methods of sensitivity analysis (SA) and feature extraction in complex and volatile systems persists. A new improved event tracking methodology, the fastTracker, for real-time SA in large scale complex systems is proposed in this paper. The main feature of fastTracker is its high-frequency analytics using meager computational cost. It is suitable for data processing and prioritization in embedded systems, Internet of Things (IoT), distributed computing (e.g. lEdge computing) applications. The presented algorithm’s underpinning rationale is event driven; its objective is to correctly and succinctly quantify the sensitivity of observable changes in the system (output) with respect to the input variables. To demonstrate the performance of the proposed fastTracker methodology, fastTracker was deployed in the Supervisory control and data acquisition (SCADA) system from real cement industry. fastTracker has been verified by system experts in real industrial application. Its performance was compared with other real-time event-based SA techniques. The comparison revealed savings of 98.8% in processing time per sensitivity index and 20% in memory usage when compared with EventTracker, its closest rival. The proposed methodology is more accurate and 80.9% faster than an entropy-based method. Its application is recommended for reinforced learning and/or formulating system key performance indicators from raw data.

Optimization of Overcast Stripping Technology Parameters Based on Discrete Event System Simulation

In the process of open‐pit mining, the system parameters determine the economic benefit and production efficiency of the mine. Conventional optimization involves building a system model for the process parameters. However, complex large‐scale systems such as open‐pit mining are difficult to model, resulting in a failure to obtain effective solutions. This paper describes a system simulation method for the process parameters involved in open‐pit mining. The nature and interaction of each component of the system are analyzed in detail, and the logical flow of each layer of the system is determined. Taking the basic operational linkages of the equipment as the system drivers, we obtained the operational flow of dragline information. The barycentric circular projection method is used to simplify the control logic of the system, and a system storage state model is constructed to identify dynamic changes in the system and obtain the operation parameters of the dragline. A discrete event system is used for quantitative modeling, and the event step method is employed to advance the simulation process and obtain decision information. Finally, simulations are performed using various system parameters. The simulation results show that the maximum efficiency is achieved when the dragline height is ∼13 m, giving a capacity of 4276.52 m3/h. Error analysis indicates that the modeling error is minimized using a simulation correction coefficient of α = 0.94.

A New Pattern Framework and Innovative Practices in the Smart Court System-of-Systems Engineering Project of China

Developing large-scale complex information systems, such as the Smart Court system-of-systems (SoSs) of China, is a worldwide engineering challenge. This paper, from a methodological perspective, aims to expound the theoretical construction and practical progress of Smart Court system-of-systems engineering (SoSE) of China. The concept and key task requirements of SoSE are explored, technical difficulties faced by the Smart Court SoSE are analyzed, and a “two-track parallel, six-ring linkage” pattern framework is proposed for the progressive collaboration SoSE of large-scale autonomous information systems. Based on the theories including a universal information model, information metric system, and dynamic configurations of information systems, a key evaluation indicator system for an information SoSs is proposed. To satisfy the SoSE design requirements, an overall design method based on information relationships and its enabling tool are proposed, and a reference model of the Smart Court SoSs is designed to provide a top-level reference for the system development and integration of the Smart Court. Moreover, the development and collaborative integration of the autonomous and backbone systems in the Smart Court SoSE are presented in a comprehensive manner. The nationwide application and promotion of the Smart Court SoSs support the upgrade and transformation of the conventional judicial operation pattern of people’s courts in China. Through continuous analyses of the quality and effectiveness of the Smart Court based on the key evaluation indicators, targeted improvement can be conducted to further enhance the SoSs capabilities, thereby contributing to the progress of judicial civilization in the information age.

Applicability of Geographically Distributed Simulations

Geographically distributed simulations (GDS) overcome the inherent limitations in capabilities of single research infrastructure to accurately represent large-scale complex power and energy systems within representative operating environments in real-time. The feasibility of GDS has been proven, however, there is a lack of confidence in its adoption owing to limited evidence of its stability and accuracy that ascertain its practical applicability. This paper presents detailed small signal stability models for GDS setups with two interface signals transformations. The models have been validated by empirical analysis and used for determining the boundaries for stable operation of GDS setups. For the common region of stability of the two transformations considered, accuracy analysis presented offers insights for their selection. This advancement, thereby, enables realisation of experimental setups that can cater for the growing need to design and validate operational schemes that ensure robust and resilient operation of critical national infrastructure.

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Physical Internet (PI) is an open global logistics system of which components are hyperconnected for increased efficiency and sustainability. Digital twin (DT), referring to the virtual representation of a physical object, is well-perceived as a key driver in the development of PI-based Supply Chain Management (SCM). Due to the capabilities of real-time monitoring and evaluation of large-scale complex systems, significant research efforts have been made to exploit values of PI/DT in SCM. Despite this, the current literature remained largely unstructured and scattered due to a lack of systematic literature reviews to synergise research findings, analyse the evolution of research fronts and extract emerging trends in the field. To address this issue, the paper deploys a bibliometric knowledge mapping approach to provide a bird's eye view of the current research status in the PI/DT-SCM area. Using CiteSpace's keyword co-occurrence network, 518 journal articles are clustered into 10 key research streams on PI/DT applications in: job shop scheduling, smart manufacturing design, PI-based SCM, manufacturing virtualisation, information management, sustainability development, data analytics, manufacturing operations management, simulation and optimisation, and assembly process planning. Based on citation burst rate, keywords representing research frontiers of the PI/DT are detected and their temporal evolutions are discussed. Likewise, some identified emerging research trends are production process and system, robotics, computer architecture, and cost. Finally, seven future research directions are suggested, which emphasise on several PI/DT-related issues, including business ecosystem, sustainability development, SC downstream management, cognitive thinking in Industry 5.0, citizen twin in digital society, and SC resilience.

New results on robust finite-time stability of singular large-scale complex systems with interconnected delays

In this paper, we provide an efficient approach based on combination of singular value decomposition (SVD) and Lyapunov function methods to finite-time stability of linear singular large-scale complex systems with interconnected delays. By representing the singular large-scale system as a differential-algebraic system and using Lyapunov function technique, we provide new delay-dependent conditions for the system to be regular, impulse-free and robustly finite-time stable. The conditions are presented in the form of a feasibility problem involving linear matrix inequalities (LMIs). Finally, a numerical example is presented to show the validity of the proposed results.