Design Of Flexible Systems Research Articles

Analysing the Performance of Large-Scale Rooftop Solar PV System Using Helioscope, PVsyst and PV*SOL Photovoltaic Simulation Software: As a Renewable Energy Strategy

In Malaysia, government initiatives and policymakers play significant role in creating effective strategies aiming at enhancing the usage of residential, industrial and commercial solar PV systems. However, the most significant barrier to the widespread adoption of photovoltaic (PV) systems is their high installation costs; as a result, numerous studies on PV system modification are now being conducted. Several simulation tools are available for the efficient and extensive integration of solar energy. To predict energy output using climate data and assess performance ration in accordance, such software can be optimized in terms of parameters like PV module number, inverter, tilt angle, and module design structure. The accuracy of these figures fluctuates, though, because climate factors affect how productive photovoltaic systems are. In this current paper, the simulation and design of a 6.9 MW solar power plant in University Tun Hussein Onn Malaysia, have been investigated using three popular software PVsyst v5.06, Helioscope and PV*SOL. This study's main objective is to assess the potential of solar renewable energy sources to produce electrical energy under the Supply Agreement of Renewable Energy (SARE) program using flexible solar system design modelling tools, PVsyst, Helioscope, and PV *SOL. The comparison results showed that Helioscope and PVsyst are considered to be the most dependable software. PVsyst appraised the definite energy production cost with lowermost relative error of 0.12 % and Helioscope estimated performance ratio with lowest relative error of 4.74%. In terms of energy losses, environmental element contributing to the most energy losses where PVsyst recorded 11.1% photovoltaic (PV) loss caused by irradiation and temperature changes. Whereby Helioscope clearly shows that environmental power loss of 8.4% represents the largest portion caused by irradiation and temperature changes. PV*SOL recorded an energy loss of 7.3% due to shading, temperature and reflection on module interface.

EXPERIENCE WITH GREENHOUSE GAS EMISSION ALLOWANCE ALLOCATION AND ANALYSIS OF LESSONS FOR VIET NAM

This study analyzes and synthesizes international experiences in allocating greenhouse gas emission quotas, drawing lessons for Viet Nam's developing carbon market. The research examines emission trading systems (ETS) and quota allocation methods from various countries and regions, including the European Union, Germany, Austria, the United Kingdom, Switzerland, the United States, Canada, Nigeria, Kenya, South Africa, China, Japan, and New Zealand. Key findings highlight the importance of diversifying allocation methods, combining free allocation, auctioning, and benchmarking approaches. The study emphasizes the need for flexibility in system design, adapting to specific economic and social conditions of each country. Lessons learned include prioritizing high-emission reduction potential sectors, setting reasonable emission caps based on actual data, ensuring transparency in information disclosure, and establishing effective monitoring and evaluation mechanisms. The research also stresses the importance of providing technical support to businesses, especially small and medium enterprises, in complying with ETS regulations. Additionally, the study addresses concerns such as preventing carbon leakage, assessing social impacts, and ensuring international compatibility of the system. These insights provide a valuable foundation for Viet Nam to develop an effective greenhouse gas ETS that aligns with national conditions and sustainable development goals.

Implementation challenges of electronic blood transfusion safety systems: Lessons from an international, multi-site comparative case study.

Severe transfusion reactions resulting from errors in matching the correct blood with the correct patient are considered never events. Despite the relative technical simplicity of barcode scanning for patient-blood bag matching, the adoption and universal application of this safety measure are by no means universal. This study highlights the logistical and institutional challenges associated with spreading, scaling up, and sustaining such IT-supported safety measures in healthcare. We report findings from a 5-year, prospective, multi-site case study conducted across one hospital in England and three hospitals in the Netherlands. Ethnographic methods, including interviews and observations, were used at each site to investigate the implementation of barcode scanning-supported safety pathways for blood transfusions. Significant variation was observed across the sites in the adoption and implementation of barcode scanning-supported safety pathways. Despite the potential for reducing transfusion errors, the introduction of this innovation was met with varying levels of success in different settings. This study highlights the critical role of inter-hospital learning and flexible system design in successfully implementing barcode scanning-supported safety pathways for blood transfusions. A more structured, national-level network for knowledge sharing could enhance the spread and sustainability of such innovations across healthcare settings.

Dynamic Robust Parameter Design Using Response Surface Methodology based on Generalized Linear Model

Purpose: When designing an input-output system susceptible to noise, engineers assume a functional relation between the input and the output. The Taguchi method, which uses a dynamic, robust parameter design (RPD) to evaluate the robustness of the input-output relation against noise, is employed. This study aims to address extending the scope of use of a dynamic RPD. Methodology/Approach: A target system in a typical dynamic RPD can be interpreted as one in which the relation between the input and the output is a linear model, and the output error follows a normal distribution. However, an actual system often does not conform to this premise. Therefore, we propose a new analysis approach that can realize a more flexible system design by applying a response surface methodology (RSM) based on a generalized linear model (GLM) to dynamic RPD. Findings: The results demonstrate that 1) a robust solution can be obtained using the proposed method even for a typical dynamic RPD system or an actual system, and 2) the target function can be evaluated using an adjustment parameter. Research Limitation/implication: Further analysis is required to determine which factor(s) in the estimated process model largely contribute(s) to changes in the adjustment parameter. Originality/Value of paper: The applicability of typical dynamic RPD is limited. Hence, this study’s analytical process provides engineers with greater design flexibility and deeper insights into dynamic systems across various contexts. Category: Research paper Keywords: robust parameter design; dynamic system; generalized linear model; response surface methodology; Taguchi method

Integrated control‐structure co‐design for flexible manipulators

AbstractThis work aims to develop a methodology for the integrated control‐structure design of flexible multibody systems. Such an issue is linked to a generalization of the inverse dynamics problem, where both the feedforward control and mechanical parameters are designed to perform a desired trajectory optimally. The resulting constrained motion is aimed at improving the dynamic performance of the system, which is underactuated, with less power demand but possible elastic deformation. Stable inversion methods are required to deal with the internal dynamics related to underactuation. The proposed methodology is based on the optimal control theory. For illustrative purposes, we considered a fully actuated linear time‐invariant (LTI) system, establishing the integrated design results in a least‐squares homogeneous system and in an optimization problem. An additional formulation is proposed for the integrated design of the flexible multibody systems, where both a multi‐criterion optimization and a scalarized problem of a corresponding nonlinear programming problem are formulated to deal with the unstable zero‐dynamics related to non‐minimum phase systems and nonlinearity. The numerical simulation of an underactuated two‐masses‐spring‐damper system and a flexible robotic manipulator in planar motions are used for validation. Finally, the beneficial aspects of the integrated design are analyzed.

Robustness of interdependent directed higher-order networks against cascading failures

In the real world, directed networks are not just constructed as pairs of directed interactions, but also occur in groups of three or more nodes that form the higher-order structure of the network. From social networks to biological networks, there is growing evidence that real-world systems connect the functional relationships of multiple systems through interdependence. To understand the robustness of interdependent directed higher-order networks, we propose a new theoretical framework to model and analyze the robustness of such networks under random failures by percolation theory. We find that adding higher-order edges makes the network more vulnerable which quantifies and compares by two criteria: the size of the giant connected components and the percolation threshold. Increasing the hyperdegree distribution of heterogeneity or the hyperedge cardinality distribution of heterogeneity in interdependent directed higher-order networks will also make the network more vulnerable. Interestingly, the phase transition type changes from continuous to discontinuous with the increase of coupling strength, and partially interdependent directed higher-order networks exist hybrid phase transition. Moreover, by applying our theoretical analysis to real interdependent directed higher-order networks further validated our conclusion, it has implications for the design of flexible complex systems.

Gradient-metasurface directional photodetectors.

Angle-sensitive photodetectors are a promising device technology for many advanced imaging functionalities, including lensless compound-eye vision, lightfield sensing, optical spatial filtering, and phase imaging. Here we demonstrate the use of plasmonic gradient metasurfaces to tailor the angular response of generic planar photodetectors. The resulting devices rely on the phase-matched coupling of light incident at select geometrically tunable angles into guided plasmonic modes, which are then scattered and absorbed in the underlying photodetector active layer. This approach naturally introduces sharp peaks in the angular response, with smaller footprint and reduced guided-mode radiative losses (and therefore improved spatial resolution and sensitivity) compared to analogous devices based on diffractive coupling. More broadly, these results highlight a promising new application space of flat optics, where gradient metasurfaces are integrated within image sensors to enable unconventional capabilities with enhanced system miniaturization and design flexibility.

Quickest detection of bias injection attacks on the glucose sensor in the artificial pancreas under meal disturbances

Modern glucose sensors deployed in closed-loop insulin delivery systems, so-called artificial pancreas use wireless communication channels. While this allows a flexible system design, it also introduces vulnerability to cyberattacks. Timely detection and mitigation of attacks are imperative for device safety. However, large unknown meal disturbances are a crucial challenge in determining whether the sensor has been compromised or the sensor glucose trajectories are normal. We address this issue from a control-theoretic security perspective. In particular, a time-varying Kalman filter is employed to handle the sporadic meal intakes. The filter prediction error is then statistically evaluated to detect anomalies if present. We compare two state-of-the-art online anomaly detection algorithms, namely the χ2 and CUSUM tests. We establish a robust optimal detection rule for unknown bias injections. Even if the optimality holds only for the restrictive case of constant bias injections, we show that the proposed model-based anomaly detection scheme is also effective for generic non-stealthy sensor deception attacks through numerical simulations.

Dynamic Event-Triggered Strategy-Based Optimal Control of Modular Robot Manipulator: A Multiplayer Nonzero-Sum Game Perspective.

Due to the limited computing and processing ability of modular robot manipulator (MRM) components, such as sensors and controllers, event-triggered mechanisms are considered a crucial communication paradigm shift in resource constrained applications. Dynamic event-triggered mechanism is developing into a new technology by reason of its higher resource utilization efficiency and more flexible system design requirements than traditional event-triggered. Therefore, an optimal control scheme of multiplayer nonzero-sum game based on dynamic event-triggered is developed for MRM systems with uncertain disturbances. First, dynamic model of the MRM is established according to joint torque feedback technique and model uncertainty is estimated by data-driven-based neural network identifier. In the framework of differential game, the tracking control problem of MRM system is transformed into the optimal control problem for multiplayer nonzero-sum game with the control input of each joint module as the player. Then, the static event-triggered control problem of MRM system is studied based on adaptive dynamic programming algorithm. On this basis, the internal dynamic variable describing the previous state of the system is introduced, and the characteristics of dynamic trigger rule and its relationship with static rule are revealed theoretically. By designing an exponential attenuation signal, the minimum sampling interval of the system is always positive, so that Zeno behavior is excluded. Lyapunov theory proves that the system is asymptotically stable and the experimental results verify the validity of the proposed method.

IAAP: INTERDEPENDENCY ATTRIBUTE AUTHENTICATION PROTOCOL FOR IoMT SYSTEMS SECURITY ENHANCEMENT AND ANALYSIS

Internet has changed and evolved since its origin and in recent time the communication channel for the internet has developed a flexible system design to accommodate newer devices and customized services. Internet of Medical Things (IoMT) is one such blooming service for larger data processing and customization. In this paper, we propose a novel interdependency attribute protocol for authentication and enhancing security aspects of IoMT communication. The protocol is developed on remote monitoring and Message Queuing Telemetric Transport (MQTT) protocol foundation values to secure data transmission and communication via active public domain internet and cloud storage. The approach is developed on machine learning models for attribute customization and classification. The model has been validated with AWS open pipeline cloud platforms for IoMT devices customization. The IoMT device cloud server evaluation and authentication via interdependency attribute protocol has outperformed the existing security models and analysis with Machine learning tools.

Enhancing operation flexibility of distributed energy systems: A flexible multi-objective optimization planning method considering long-term and temporary objectives

Flexibility is a crucial capability that distributed energy systems need to possess. Flexible distributed energy systems can attain the objectives of daily economic operation for users, temporary demand response for the power grid, and low-carbon operation for environmental benefits. Among these objectives, economy and low-carbon objectives are long-term objectives that need to be met routinely, while grid-friendly interactions only occur temporarily but need to be met as a priority. Current multi-objective optimization planning models disregard the inherent differences in temporal property of these objectives, which may weaken the system's flexibility. Here, a novel multi-objective optimization model is proposed to enhance the flexibility of distributed energy systems. First, a new flexibility index is formulated, in which diverse objectives are evaluated synergistically across different scenarios. Second, a flexible mixed-integer multi-objective programming model is developed, which returns the most flexible system design and its operation strategies for each objective. The application in a real case in Tianjin, China revealed that, compared with the conventional multi-objective planning method, the proposed method could improve the system flexibility by 6%–10 %. The universality of the proposed method was further verified by traversal analysis.

Reference management-based resilient control for delayed periodic piecewise polynomial systems under proportional integral observer

This article examines the proportional integral observer (PIO)-based resilient tracking control problem for periodic piecewise polynomial systems (PPPSs) in the presence of time delay and disturbances. To be precise, the PPPSs are formulated by dividing the fundamental period of periodic systems into numerous subintervals, each of which is defined using matrix polynomial functions. Further, the PIO strategy is employed to estimate the states of the undertaken system with high precision wherein the integral term in PIO enhances the system's design flexibility and also increases its robustness. Taking advantage of these characteristics, a PIO-based tracking control is configured with gain perturbations to track the desired reference states. Moreover, to attenuate the implications made by disturbances in considered system design, H ∞ performance is employed. Furthermore, in order to establish adequate conditions in the form of linear matrix inequalities, the time-varying polynomial Lyapunov–Krasovskii functional is implemented. Then, the time-varying gain matrices of the control scheme and PIO configuration are computed by solving the obtained criteria through the MATLAB platform. Concludingly, to evidence for the discoveries' potential and usefulness, a numerical example is offered.

Terminal-based zonal bus: A novel flexible transit system design and implementation

The pursuit of individual mobilities expedites the development of demand-responsive systems, while the balance between service flexibilities and operational cost is becoming particularly important. This paper proposes a method for operating a novel demand-responsive bus system termed terminal-based zonal bus (TZB). It is a flexible system with vehicles executing a terminal-based zone routing scheme in specified service zones. The terminal-based routing scheme intends to generate TZB lines, and each line is operated by one vehicle that travels along a series of terminal stations arranged in order and serves enroute passenger requests between any two consecutive terminal stations. As instant response is the main trait of demand-responsive systems, TZB is able to address instance response by incorporating reserve capacity, which is employed to indicate the vehicle responsive ability of new real-time requests. Further, a tailored heuristic algorithm is designed to solve the proposed model, which simultaneously incorporates several practical restrictions, including the capability of adopting real-time requests, terminal stations’ fleet-size constraints, time window constraints, and vehicle capacity. Computational experiments are conducted based upon the newly developed modern city Xiong An, China which confirm that the proposed method is capable of obtaining high-quality solutions efficiently, and TZB can provide flexible service at a relatively low operational cost.

Economic performance evaluation of flexible centralised and decentralised blue hydrogen production systems design under uncertainty

Blue hydrogen is viewed as an important energy vector in a decarbonised global economy, but its large-scale and capital-intensive production displays economic performance vulnerabities in the face of increased market and regulatory uncertainty. This study analyses flexible (modular) blue hydrogen production plant designs and evaluates their effectiveness to enhance economic performance under uncertainty. The novelty of this work lies in the development of a comprehensive techno-economic evaluation framework that considers flexible centralised and decentralised blue hydrogen plant design alternatives in the presence of irreducible uncertainty, whilst explicitly considering the time value of money, economies of scale and learning effects. A case study of centralised and decentralised blue hydrogen production for the transport sector in the San Francisco area is developed to highlight the underlying value of flexibility. The proposed methodological framework considers various blue hydrogen plant designs (fixed, phased, and flexible) and compares them using relevant economic indicators (net present value (NPV), capex, value-at-risk/gain, etc.) through a detailed Monte Carlo simulation framework. Results indicate that flexible centralised hydrogen production yields greater economic value than alternative designs, despite the associated cost-premium of modularity. It is also shown that the value of flexibility increases under greater uncertainty, higher learning rates and weaker economies of scale. Moreover, sensitivity analysis reveals that flexible design remains the preferred investment option over a wide range of market and regulatory conditions except for high initial hydrogen demand. Finally, this study demonstrates that major regulatory and market uncertainties surrounding blue hydrogen production can be effectively managed through the application of flexible engineering system design that protects the investment from major downside risks whilst allowing access to favourable upside opportunities.

A framework for flexible and reconfigurable vision inspection systems

Reconfiguration activities remain a significant challenge for automated Vision Inspection Systems (VIS), which are characterized by hardware rigidity and time-consuming software programming tasks. This work contributes to overcoming the current gap in VIS reconfigurability by proposing a novel framework based on the design of Flexible Vision Inspection Systems (FVIS), enabling a Reconfiguration Support System (RSS). FVIS is achieved using reprogrammable hardware components that allow for easy setup based on software commands. The RSS facilitates offline software programming by extracting parameters from real images, Computer-Aided Design (CAD) data, and rendered images using Automatic Feature Recognition (AFR). The RSS offers a user-friendly interface that guides non-expert users through the reconfiguration process for new part types, eliminating the need for low-level coding. The proposed framework has been practically validated during a 4-year collaboration with a global leading automotive half shaft manufacturer. A fully automated FVIS and the related RSS have been designed following the proposed framework and are currently implemented in 7 plants of GKN global automotive supplier, checking 60 defect types on thousands of parts per day, covering more than 200 individual part types and 12 part families.

A multi-objective flexible manufacturing system design optimization using a hybrid response surface methodology

The present study proposes a hybrid framework combining multiple methods to determine the optimal values of design variables in a flexible manufacturing system (FMS). The framework uses a multi-objective response surface methodology (RSM) to achieve optimum performance. The performance of an FMS is characterized using various weighted measures using the best–worst method (BWM). Subsequently, an RSM approximates the functional relationship between the FMS performance and design variables. The central composite design (CCD) is used for this aim, and a polynomial regression model is fitted among the factors. Eventually, a bi-objective model, including the fitted and cost functions, is formulated and solved. As a result, the optimal percentage for deploying the FMS equipment and machines to achieve optimal performance with the lowest deployment cost is determined. The proposed framework can serve as a guideline for manufacturing organizations to lead strategic decisions regarding the design problems of FMSs. It significantly increases productivity for the manufacturing system, reduces redundant labor and material handling costs, and facilitates production.

On the design of resilient flexible manufacturing systems

In the past few years, due to various man-made and natural disasters such as COVID-19, the manufacturing industry worldwide has undergone significant changes. To counter and prevent the losses, it is important to consider the resilience of the manufacturing systems. In this paper, a new method for designing resilient Flexible Manufacturing System (FMS) is presented. The FMS is modeled by a directional network. The method consists of two types of strategies: one is to use Flexible Manufacturing Cells (FMC) and reconfigure route (Strategy A) and the other is to use a combination of FMC and its extended storage capacity (Strategy B). The resilience is measured by the Production Lost and the Investment Return. Using the Monte Carlo Simulation, FMS can be investigated under various failure conditions. For complex FMS, the size of the strategy set is very large. Thus, two theorems are proposed to reduce the size without effecting the optimal solution. A demonstration example with 33 nodes fabricating 5 kinds of products is presented. It shows that compared to simply waiting for fixing failure, the use of Strategy A and Strategy B will result in more than 10% increase in Investment Return.

Polymeric Materials Selection for Flexible Pulsating Heat Pipe Manufacturing Using a Comparative Hybrid MCDM Approach.

The right choice of polymeric materials plays a vital role in the successful design and manufacture of flexible fluidic systems, as well as heat transfer devices such as pulsating heat pipes. The decision to choose an acceptable polymeric material entails a variety of evaluation criteria because there are numerous competing materials available today, each with its own properties, applications, benefits, and drawbacks. In this study, a comparative hybrid multi-criteria decision-making (MCDM) model is proposed for evaluating suitable polymeric materials for the fabrication of flexible pulsating heat pipes. The decision model consists of fourteen evaluation criteria and twelve alternative materials. For this purpose, three different hybrid MCDM methods were applied to solve the material selection problems (i.e., AHP-GRA, AHP-CoCoSo, and AHP-VIKOR). According to the results obtained, PTFE, PE, and PP showed promising properties. In addition, Spearman's rank correlation analysis was performed, and the hybrid methods used produced consistent rankings with each other. By applying MCDM methods, it was concluded that PTFE is the most suitable material to be preferred for manufacturing flexible pulsating heat pipes. In addition to this result, PE and PP are among the best alternatives that can be recommended after PTFE. The study supports the use of MCDM techniques to rank material choices and enhance the selection procedure. The research will greatly assist industrial managers and academics involved in the selection process of polymeric materials.

Improved Dynamic Event-Triggered Robust Control for Flexible Robotic Arm Systems with Semi-Markov Jump Process

In this paper, we investigate the problem of a dynamic event-triggered robust controller design for flexible robotic arm systems with continuous-time phase-type semi-Markov jump process. In particular, the change in moment of inertia is first considered in the flexible robotic arm system, which is necessary for ensuring the security and stability control of special robots employed under special circumstances, such as surgical robots and assisted-living robots which have strict lightweight requirements. To handle this problem, a semi-Markov chain is conducted to model this process. Furthermore, the dynamic event-triggered scheme is used to solve the problem of limited bandwidth in the network transmission environment, while considering the impact of DoS attacks. With regard to the challenging circumstances and negative elements previously mentioned, the adequate criteria for the existence of the resilient H∞ controller are obtained using the Lyapunov function approach, and the controller gains, Lyapunov parameters and event-triggered parameters are co-designed. Finally, the effectiveness of the designed controller is demonstrated via numerical simulation using the LMI toolbox in MATLAB.

Active balancing control for distributed battery systems based on cooperative game theory

For the smooth integration of renewable and volatile energy sources in the electricity grid, there is a need for scalable and efficient storage systems. Stationary battery systems provide the required efficiency but lack the scalability to meet varying requirements. In this article, we present a novel distributed control approach for active balancing of battery cells facilitating a scalable and flexible battery system design. For the proposed algorithm, the topology of the underlying battery system is assumed to be not predefined and can change at runtime. In such a system, each battery cell may has its own power electronics and processing unit which are used to manage the control actions for active balancing on cell level. Thus, the battery system is objected as a multi agent system (MAS) with battery cells as agents. Each agent locally computes and optimises its own control actions in a leaderless MAS setup based on cooperative bargaining games and the Nash solution. Each cell is required to find a single partner or multiple partners to form coalitions and exchange energy for active balancing. The working principle and capability of the distributed control algorithm is evaluated by simulations and compared to a passive balancing approach and a central optimal control algorithm.