System Modeling Techniques Research Articles

New horizons in systems engineering and thinking to improve health and social care for older people.

Existing models for the safe, timely and effective delivery of health and social care are challenged by an ageing population. Services and care pathways are often optimised for single-disease management, while many older people are presenting with multiple long-term conditions and frailty. Systems engineering describes a holistic, interdisciplinary approach to change that is focused on people, system understanding, design and risk management. These principles are the basis of many established quality improvement (QI) tools in health and social care, but implementation has often been limited to single services or condition areas. Newer engineering techniques may help reshape more complex systems. Systems thinking is an essential component of this mindset to understand the underlying relationships and characteristics of a working system. It promotes the use of tools that map, measure and interrogate the dynamics of complex systems. In this New Horizons piece, we describe the evolution of systems approaches while noting the challenges of small-scale QI efforts that fail to address whole-system problems. The opportunities for novel soft-systems approaches are described, along with a recent update to the Systems Engineering Initiative for Patient Safety model, which includes human-centred design. Systems modelling and simulation techniques harness routine data to understand the functioning of complex health and social care systems. These tools could support better-informed system change by allowing comparison of simulated approaches before implementation, but better effectiveness evidence is required. Modern systems engineering and systems thinking techniques have potential to inform the redesign of services appropriate for the complex needs of older people.

Research on digital twin architecture of ship maneuverability simulation for system model

Abstract With the development of civil and military requirements, ship maneuverability simulation systems tend to be complicated, and the traditional simulation methods are difficult to meet in terms of the development needs of digitalization. The joint simulation technology based on the FMI standard can realize the digital twin of complex system models, which can solve the difficult problem of multidisciplinary and multi-sub-system integration simulation. This paper analyzes the current domestic and foreign research content on joint simulation technology, digital twin technology, and complex system modeling, takes a typical Z-type maneuverability test as an example, establishes a multidisciplinary and cross-platform subsystem model, and designs and develops a ship maneuverability joint simulation algorithm and system software for the complex system model. The simulation results verify the effectiveness of the system modeling technique and the joint simulation technique based on FMI. Based on this system, the whole framework of the ship maneuverability simulation system, which is based on digital twins, is designed. According to the development requirements of ship maneuverability simulation, the main design framework, key technology, and implementation approach of this research method are given, which provides basic methods and ideas for the digital research of ship maneuverability simulation.

Predicting grout’s uniaxial compressive strength (UCS) for fully grouted rock bolting system by applying ensemble machine learning techniques

This study proposes a novel system for accurately predicting grout’s uniaxial compressive strength (UCS) in fully grouted rock bolting systems. To achieve this, a database comprising 73 UCS values with varying water-to-grout (W/G) ratios ranging from 22 to 42%, curing times from 1 to 28 days, the admixture of fly ash contents ranging from 0 to 30%, and two Australian commercial grouts, Stratabinder HS, and BU-100, was built after conducting comprehensive series of experimental tests. After building the dataset, a metaheuristic technique, the jellyfish search (JS) algorithm was employed to determine the weight of base models in the ensemble system. This system combined various data and modelling techniques to enhance the accuracy of the UCS predictions. What sets this technique apart is the comprehensive database and the innovative use of the JS algorithm to create a weighted averaging ensemble model, going beyond traditional methods for predicting grout strength. The proposed ensemble model was called the weighted averaging ensemble model (WAE-JS), in which the obtained results of several soft computing models such as multi-layer perceptron (MLP), Bayesian regularized (BR) neural networks, generalized feed-forward (GFF) neural networks, classification and regression tree (CART), and random forest (RF) were weighted based on JS and the new results were then generated. Eventually, the result of WAE-JS was compared to other models, including MLP, BR, GFF, CART, and RF, based on some statistical parameters, such as R-squared coefficients, RMSE, and VAF as indices for evaluating the performance and capability of the proposed model. The results suggested the superiority of the ensemble WAE-JS system over the base models. In addition, the proposed WAE-JS model effectively improved the predicting accuracy achieved from the MLP, BR, GFF, CART, and RF. Furthermore, the sensitivity analysis revealed that the W/G had the most significant impact on the grout’s UCS values.

Teaching content model for designing digital systems of power grid relay protection and automation

It was found that the effective formation of conceptual-analytical and productive-synthetic levels of formation of professional knowledge and skills regarding the design of digital systems of power grid relay protection and automation is possible with the systematic development and use of a generalized model of the teaching content. Based on the study of information models of technical systems, a feature model was adopted as a basic invariant, which includes a set of features of the purpose, construction, principle of operation and parameters of the technical system. It was found that the design process of digital systems of power grid relay protection and automation can be conventionally divided into three stages, namely, the development of the design task, the direct implementation of the design, and the determination of the parameters of operation. Causal chains of knowledge that form the basis of problem solving at these three stages are determined. It was found that the methods of step-by-step decomposition and aggregation are key in building the content of teaching the design of digital systems of power grid relay protection and automation based on causal chains of knowledge. A generalized model of the teaching content design of digital systems of power grid relay protection and automation based on causal chains of knowledge is substantiated. According to the generalized model, the teaching content design of specific microprocessor l systems of power grid relay protection and automation has been developed. With the help of experimental verification, the effectiveness of the teaching content design of digital systems of relay protection and automation based on causal chains of knowledge has been proven. The results of the research can be used in the educational process of professional training of future engineers in the specialty 141 Electric power, electrical engineering and electromechanics.

Energy–Logistics Cooperative Optimization for a Port-Integrated Energy System

In order to achieve carbon peak and neutrality goals, many low-carbon operations are implemented in ports. Integrated energy systems that consist of port electricity and cooling loads, wind and PV energy devices, energy storage, and clean fuels are considered as a future technology. In addition, ports are important hubs for the global economy and trade; logistics optimization is also part of their objective, and most port facilities have complex logistics. This article proposes an energy–logistics collaborative optimization method to fully tap the potential of port-integrated energy systems. A logistics–energy system model is established by deeply examining the operational characteristics of logistics systems and their corresponding energy consumption patterns, considering ships’ operational statuses, quay crane distribution constraints, and power balances. To better represent the ship–energy–logistics optimization problem, a hybrid system modeling technique is employed. The case of Shanghai Port is studied; the results show that costs can be reduced by 3.27% compared to the traditional optimization method, and a sensitivity analysis demonstrates the robustness of the proposed method.

A novel fractional neural grey system model with discrete q-derivative

The challenge of predicting time series with limited data has evolved over time due to nonlinearity, complexity, and limited information. It can be perceived as a mapping of dynamical systems in one-dimensional space. This article proposes a neural grey system to tackle this challenge. The system enhances its ability to fit nonlinearity by employing polynomials, captures complexity through a fractional-order cumulant operator, and resolves information-poor uncertainty by utilizing grey system modeling techniques. The model effectively integrates research findings from neural computing, uncertainty theory, and complexity theory at a theoretical level. It accurately describes dynamic processes of complex systems. Additionally, we have reduced the complexity of calculations in the algorithm design. We selected a dataset of total retail sales of consumer goods to test the model’s validity and applicability. Our experiments demonstrate that the newly proposed grey forecasting model can effectively forecast time series with small samples, offering good forecasting outcomes and generalization ability.

Supporting modular product family representations by methodically utilising meta-models

AbstractDue to the rising multidisciplinarity and connectivity of products especially modular product families, a sophisticated handling of the information is crucial for reducing complexity during the development. System modelling techniques have evolved to assist engineers with managing information. However, nowadays, it is rarely focusing on modular product families. This paper introduces a meta-model based on an ontology, which improves the creation and management of modular product family and its occurring data. The meta-model is presented using the example of a Passenger Service Unit (PSU).

Enhancing design representations of information and knowledge of complex and long-living assets by applying system modelling techniques

AbstractManaging general domain knowledge and asset-specific information in the form of digital representations is especially important and challenging when focusing on long-living and complex assets. Implicit knowledge and existing structures need to be captured and digitalised, ideally without introducing unnecessary complexity through unfamiliar wording or new structures. To achieve this, a methodical approach that utilises ontologies as well as system modelling techniques and focuses on early-stage model instantiation is presented and applied to the cabin retrofit of aircraft

Reliability-Based Preventive Maintenance Strategy for Subsea Control System

The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the complex interplay between components under variable loads, improving model accuracy. This study also examines the effects of imperfect maintenance on degradation paths and introduces a holistic lifecycle cost model for preventive maintenance (PM), optimized against reliability and economic considerations. Numerical simulations on a Subsea Control Module demonstrate the effectiveness of the developed models.

Locustamigratoria (L.) (Orthoptera) in a warming world: unravelling the ecological consequences of climate change using GIS.

The migratory locust, Locustamigratoria (L.), a significant grasshopper species known for its ability to form large swarms and cause extensive damage to crops and vegetation, is subject to the influence of climate change. This research paper employs geographic information system (GIS) and MaxEnt ecological modelling techniques to assess the impact of climate change on the distribution patterns of L.migratoria. Occurrence data and environmental variables are collected and analysed to create predictive models for the current and future distribution of the species. The study highlights the crucial role of climate factors, particularly temperature and precipitation, in determining the locust's distribution. The MaxEnt models exhibit high-performance indicators, accurately predicting the potential habitat suitability of L.migratoria. Additionally, specific bioclimatic variables, such as mean temperature and annual precipitation, are identified as significant factors influencing the species' presence. The generated future maps indicate how this species will invade new regions especially in Europe. Such results predict the risk of this destructive species for many agriculture communities as a direct result of a warming world. The research provides valuable insights into the complex relationship between locust distribution and environmental factors, enabling the development of effective strategies for locust management and early warning systems to mitigate the impact on agriculture and ecosystems.

Investigating the Surrogate Modeling Capabilities of Continuous Time Echo State Networks

Continuous Time Echo State Networks (CTESNs) are a promising yet under-explored surrogate modeling technique for dynamical systems, particularly those governed by stiff Ordinary Differential Equations (ODEs). A key determinant of the generalization accuracy of a CTESN surrogate is the method of projecting the reservoir state to the output. This paper shows that of the two common projection methods (linear and nonlinear), the surrogates developed via the nonlinear projection consistently outperform those developed via the linear method. CTESN surrogates are developed for several challenging benchmark cases governed by stiff ODEs, and for each case, the performance of the linear and nonlinear projections is compared. The results of this paper demonstrate the applicability of CTESNs to a variety of problems while serving as a reference for important algorithmic and hyper-parameter choices for CTESNs.

A neuro-fuzzy modular system for modeling nonlinear systems

The real world is nonlinear and in the control application field, this aspect needs to be resolved to build models so we need to refer to nonlinear system modeling techniques. Neuro-fuzzy systems and modular neural networks (NNs) are among the best modeling approaches for nonlinear systems. The combined features of both approaches provide better models. Thus, we propose in this paper a neuro-fuzzy modular architecture for modeling nonlinear systems. The modular architecture consists of dividing a nonlinear problem into several simpler subproblems. We assigned to each subproblem an NN. Each NN provides individual solutions that will be combined to provide a general solution to the original problem. In this respect, the decomposition of the original problem is based on a fuzzy decision mechanism. This mechanism consists of a set of fuzzy rules for processing nonlinear problems using two different strategies. The first involves training only the network weights, and the second adds the fuzzy set parameters to the training step. A comparative study of both strategies reveals the competence of the second strategy in providing better accuracy and simplicity. Using the neuro-fuzzy combination among the modular NNs reduces the complexity of the original problem and achieves much better performance. The proposed architecture is evaluated by two second-order nonlinear systems, a numerical system and a real system called “the chemical reactor,” which is used to carry out a chemical reaction not only in chemical and biochemical engineering, but also in the petrochemical industry. For both systems, the proposed approach provides better performance in terms of the learning time, learning error, and number of neurons.

Modeling De-Coring Tools with Coupled Multibody Simulation and Finite Element Analysis

De-coring is an essential process in the casting process chain, determining the quality and cost of production. In this study, a coupled multibody system (MBS) and finite element modeling (FEM) technique is presented to study the mechanical loads during the de-coring process. The removal of cast-in sand cores from the inner regions of the cast part by de-coring or knocking out is a complex process with dynamic loads. Currently, the process relies upon empirical knowledge and tests. Inorganic sand cores pose additional challenges in the success of the de-coring process. Increasing complexity in geometry and stringent environmental regulations compel a predictive process in the earlier stages of design. Predicting the process’ success is challenged by the dynamic non-linearities of the system. The dynamic characteristics and the interaction between hammer and casting were studied here for the first time using an industrial-based test rig, and a novel modeling approach was formulated. The results of the developed model are in good compliance with the experiments. The methodology presented in this study can be used to include a varying number of hammers and loads. The proposed approach presents the possibility to discretize the process and qualitatively assess the process parameters for optimization.

Developing a collector system aggregation technique of a large-scale grid-connected wind farm

The increasing complexity of power systems necessitates the development of aggregation techniques to address specific issues. Model reduction is crucial to expand simulation capacity, ensuring the stability and reliability of power system design. This paper presents a collector system full aggregation technique based on the per unit system model and power loss-based aggregation technique applied to the impedance model of a large-scale grid-connected Wind Farm (WF). The purpose of the proposed technique is to obtain a fully aggregated equivalent representation of the WF collector system, which includes High Voltage (HV) three-winding transformer(s) or more than one HV two-winding transformer, to reduce the simulation time and the system complexity while maintaining similar performance compared to the Detailed Model (DM) and high-accuracy results in both steady-state and dynamic conditions. To be validated, the presented Per Unit Full Aggregation (PUFA) technique is used to obtain a Full Aggregated Equivalent Model (FAEM) of the existing Gabel El-Zeit WF, Egypt, Detailed Model (DM) that contains two HV three-winding transformers. The PUFA model is tested using the DIgSILENT PowerFactory software package, leading to a significant reduction in model order and simulation time with respect to the DM. The PUFA technique is compared to the DM and other aggregated models in terms of power flow using Quasi-Dynamic Simulation (QDS), dynamic response using Low Voltage Right Through (LVRT) simulation, Modal Analysis (MA), and small signal simulation. The comparison between the different models and the proposed one has confirmed that it realizes very competitive results with high accuracy in both steady-state and dynamic conditions with the least simulation time.

Artificial Intelligence Methods in Hydraulic System Design

Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

Identification of small-scale hydropower potential sites using geographic information system and hydrologic modeling technique: Awata river, Genale Dawa basin, Ethiopia

Small-scale hydropower is a significant source of renewable and inexpensive energy. This study was carried out to identify suitable potential sites for small-scale hydropower for the generation of electricity along the Awata River in Ethiopia. Primary and secondary data collected from various sources were used in the present study to achieve its objective. After the consistency of rainfall was checked and areal rainfall was estimated using the Theissen polygon method, the rainfall–runoff​ relationship was simulated using HEC-HMS. The head of each identified site was estimated by using GIS to overlay the DEM and stream network starting at the outlet. To estimate the discharge, the flow duration curve was developed. HEC-HMS performance was evaluated using Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE), Percent Bias (PBIAS), and coefficient of determination (R2). During calibration, the values were 0.82, 4.39, −2.93, and 0.83, and during validation, the values were 0.76, 5.35, 6.25, and 0.76. The identified sites were ranked using an analytical hierarchy process based on a ratio of five criteria. There are twenty-eight sites identified, with estimated power output and energy output of 45.26 MW and 198.79 GWh at 50% and 6.74 MW and 53.23 GWh at 90%, respectively. The overall rank indicated that the selected site code 29 is ranked first and can be used for the implementation of a small hydropower plant. As a result, the design of all the components of small hydropower plants will be required to implement and generate energy for rural areas in future development.

Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

AbstractVehicle‐to‐Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non‐line‐of‐sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context‐Aware Target Classification (CA‐TC) module coupled with a hybrid learning‐based predictive modeling technique for CVS systems is proposed. The CA‐TC consists of two modules: a Context‐Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA‐TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real‐world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on‐the‐fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

Towards formal verification of smart grids: An effective modelling approach and some first experimentation

Nowadays, smart grids are used widely around the world when they enable detecting, reacting and pro-acting to changes in usage and many concerns with the power system, as well as having self-healing capabilities. Some smart grids have recently been created and operational in Vietnam. To ensure the effectiveness of the smart grids, the correctness of the system designs must be studied carefully before the explosion of the use of smart grids, particularly in developing countries such as Vietnam. As formal methods, including formal verification and model checking, recently play more important role in verifying smart grid properties such as load balancing and fault resilience, the effectiveness of formal verification depends mostly on system modeling and verification techniques. Recent researches have shown the feasibility of applying model-checking tools in smart grid verification, and also the inability to test complex properties and systems. In our opinion, the inability could be come from the complicating of the models of the system-under-test. In this study, we suggested a new method for representing smart grids using Colored Petri Net (CPN), a formal representa- tion language. In comparing to the current modeling approach, the new model allows engineers transforms complex grids into simple models. Moreover, based on the advantages of the “color” aspect of the CPN, the result model can be easily upgraded to adapt to the changes of the original smart grids without re- modeling. Also in this study, a basic case study for representing a smart grid, which consists of multiple power sources and multiple consumers, will be shown. The model will be configured to capture some problems that may happen in the grid such as the changing of the capacity of the power sources. The verification experimentation conducted on the case study shows the usefulness of the proposed method.

Modeling and Simulation of Circuit-Level Nonidealities for an Analog Computing Design Approach With Application to EEG Feature Extraction

This article presents a design approach for the modeling and simulation of ultralow power (ULP) analog computing machine learning (ML) circuits for seizure detection using electroencephalography (EEG) signals in wearable health monitoring applications. In this article, we describe a new analog system modeling and simulation technique to associate power consumption, noise, linearity, and other critical performance parameters of analog circuits with the classification accuracy of a given ML network, which allows to realize a power and performance optimized analog ML hardware implementation based on diverse application-specific needs. We carried out circuit simulations to obtain nonidealities, which are then mathematically modeled for an accurate mapping. We have modeled noise, nonlinearity, resolution, and process variations such that the model can accurately obtain the classification accuracy of the analog computing-based seizure detection system. Noise has been modeled as an input-referred white noise that can be directly added at the input. Device process and temperature variations were modeled as random fluctuations in circuit parameters, such as gain and cut-off frequency. Nonlinearity was mathematically modeled as a power series. The combined system level model was then simulated for classification accuracy assessments. The design approach helps to optimize power and area during the development of tailored analog circuits for ML networks with the ability to potentially trade power and performance goals while still ensuring the required classification accuracy. The simulation technique also enables to determine target specifications for each circuit block in the analog computing hardware. This is achieved by developing the ML hardware model, and investigating the effect of circuit nonidealities on classification accuracy. Simulation of an analog computing EEG seizure detection block shows a classification accuracy of 91%. The proposed modeling approach will significantly reduce design time and complexity of large analog computing systems. Two feature extraction approaches are also compared for an analog computing architecture.

Providing mechanisation of loading and unloading operations in hopper rail stores

Grain and fertiliser transportation are the fastest growing segments of the transportation services market, including railway transportation. Presidential Decree No. 204 of 7 May 2018 “On the national goals and strategic objectives for the development of the Russian Federation for the period until 2024” tasked the Russian government with developing a programme of measures to increase the productivity of economic sectors, including the agroindustrial complex. The increase in the productivity of the latter should entail the development of both domestic consumption and the export component, which places an additional burden on rail transport. The aim of the research is to improve loading and unloading operations in bunker-type railway yards by using auxiliary constructions to speed up and facilitate the loading and unloading of rolling stock by bulk cargo. The research is based on the principles of the system approach using methods of mathematical modelling of technical systems, numerical methods of problem solution and rheology engineering methods. The results of the research are resource-saving ways of bulk cargo loading and unloading from containers of various purposes, allowing to increase the degree of their uniform distribution in the container, and to ensure smooth release of material.