Engineering reports : open access最新文献

This study proposes a novel network architecture called SYnergistic CLosed-loop Supply Chain Network Design (SYCLSCND), which incorporates antifragility, sustainability, and agility while considering environmental needs, risk, and robustness. Robust Stochastic Optimization (RSO) and weighted value at risk (WVaR) are recommended for coping with risk and robustness. For the first time, this model includes the expected value and WVaR of cost as an objective function. By including Blockchain Technology (BCT), sustainability (including renewable energy and hybrid vehicles for transportation items), agility (paying attention to demand fulfillment limits), and antifragility (flexible capacity), this research enhances the model. The case study is in the automotive industry. As seen in sensitivity analysis, a main model is 3.78% less than without synergistic. Finally, this study examines the impact of varying demand levels, conservatism coefficient, access level functions, and resiliency scores on cost and time computation. Decreasing demand levels make the use of certain technologies impractical and economically unfavorable. Increasing the conservatism coefficient increases cost and time computation. Different access level functions determine the model's risk-seeking or risk-averse nature. Increasing the resiliency score initially does not affect cost but opens new facilities and increases the cost when it reaches 41%. Increasing the scale of the problem exponentially increases cost and time computation.

Solar energy in urban areas due to excessive air conditioning usage in buildings may significantly reduce the consumption of fossil fuels. This study uses TRNSYS to undertake the thermal performance analysis of solar-driven vapor absorption cooling systems for several urban cities in Pakistan with varying climatic conditions. Two separate solar collectors—flat plate collector (FPC) and evacuated tube collector (ETC)—are used to simulate the cooling system. The system's performance is evaluated based on the solar fraction (SF) and primary energy savings. The results of simulation showed that ETC would be a better choice regarding the selection of solar collector as the system with ETC achieved a higher SF and primary energy saving ($��f_{�\text{saving}�,�\mathrm{shc}�}�$). The SF ranges from 13% to 78% and 13% to 64% for ETC and FPC, respectively. The primary energy saving ranges from 75% to 93% and 75% to 96% with flat plate and ETC, respectively. For both flat plate and ETCs, increasing the collector area increased the SF and primary energy savings while increasing the capacity of thermal storage decreased the SF and primary energy savings. For varying thermal storage volumes, the SF varies very little when using flat plate collector but significantly when using ETC. The best thermal performance was recorded in Peshawar based on the SFs and primary energy saving.

This study introduces a new approach by applying the Whale Optimization Algorithm (WOA) to create construction schedules using geometric data from Building Information Modeling (BIM). The algorithm utilizes 3D model information to establish stability criteria, which are organized in a Directed Design Structure Matrix (DSM). These criteria are integrated into the WOA Fitness function to enhance the constructability of schedules, where each schedule is symbolized as a unique whale. Through iterative WOA computations, the approach consistently achieves maximum constructability scores starting from randomly generated schedules, affirming the efficacy of this method. The results reveal that the proposed algorithm effectively produced fully executable project schedules from diverse inputs. Despite variations in computational times due to different input parameters, the experiments verified the consistent generation of schedules that are 100% executable.

The utilization of waste-based thermal energy-storing building materials in construction is a significant advancement in the reduction of the environmental impact of human activities. Such materials provide thermal comfort, reduce energy consumption, and promote the use of renewable resources. Phase change materials (PCM) have been the subject of these achievements in recent years regarding their heat storage potentials. On the other hand, the utilization of waste is of significant consequence with regard to the sustenance of a circular economy and the alleviation of waste-related environmental concerns. However, the majority of studies on PCM in the literature tend to concentrate on the thermal, physical, chemical, and mechanical properties of the materials, and thus fails to provide a comprehensive perspective on the sustainability of these materials. In this study, 11 articles selected from a total of 178 studies aiming to produce building materials with waste-PCM combinations are analyzed according to five categories related to sustainable built environment, which are resource use, environmental impact, health, comfort, and cost. As a result, it is proved that composite building materials that contain waste and store thermal energy have the potential to contribute significantly to sustainable architecture.

FACTS devices, functioning as controlled series voltage and parallel current source converters in transmission lines, can enhance network flexibility. The currents and voltages are regarded as unknown variables in the impedance estimation equations of the distance relay during fault occurrence. Typically, addressing the issues of protection systems for lines integrated with FACTS devices necessitates the development of a novel protection algorithm informed by the current line topology. A novel approach to address this difficulty involves employing an algorithm to estimate the injected current and voltage parameters of the series and parallel converters in FACTS devices. This research presents an estimation scheme utilizing artificial neural networks to determine the magnitude and phase angle of voltage and current in voltage source converters of the UIPC during short-circuit faults in transmission lines. The proposed approach has been designed for use across diverse locations, phases, resistances, and fault durations on both sides of the UIPC. The voltage and current magnitudes and angles for all three bus sequences on one side of the line, along with the voltage magnitude and angle of the equivalent circuit of the series converters and the current magnitude and angle of the shunt converter, have been recorded. The primary aim of this work is to introduce an estimation model derived from the measurement data of the bus and the UIPC converters. Ultimately, by employing the estimated phasors, the impedance and, consequently, the distance from the fault location to the relay may be accurately determined, eliminating the necessity to alter the configuration and formulation of the distance relay. The approach has undergone testing and evaluation for various short circuit scenarios on both sides of the UIPC under diverse conditions. The successful outcomes demonstrated in the simulation section indicate that the proposed technique effectively estimates the UIPC phasor during a fault.

Modern power systems are increasingly challenged by frequency stability issues due to dynamic load variations and the growing complexity of interconnected networks. Traditional PID controllers, while widely utilized, struggle to address the rapid fluctuations and uncertainties inherent in contemporary multi-area interconnected power systems (MAIPS). This paper introduces an innovative approach to Load Frequency Control (LFC) using a Fractional-Order PID (FOPID) controller, optimized by a Neural Network Algorithm (NNA). The proposed NNA-FOPID framework leverages the biological principles of neural networks to dynamically tune controller parameters, significantly enhancing system performance. The solution is tested under various scenarios involving step load changes across multi-area systems. The proposed method demonstrates marked improvements over traditional PID controllers and advanced optimization techniques such as Differential Evolution (DE) and Artificial Rabbits Algorithm (ARA). The comparisons show that the FOPID controller's NNA-based design effectively and successfully handles LFC in MAIPSs for ITAE minimizations, and statistical evaluation supports its superiority.

This research delves into innovative solutions for integrating renewable solar energy into electric vehicle (EV) systems to mitigate limitations associated with battery storage and charging infrastructure. By leveraging advanced technologies, such as phase change materials for efficient thermal management, microgrid-based charging systems for decentralized energy distribution, and cutting-edge materials like aerogel composites and polymer solar cells for enhanced energy harvesting, this study addresses critical challenges in sustainable transportation. Through a holistic approach that combines energy efficiency, advanced material science, and renewable energy integration, the research provides actionable insights to enhance EV performance, extend battery life, and reduce greenhouse gas emissions. The findings hold significant implications for improving energy autonomy, enabling sustainable mobility solutions, and fostering environmental conservation in the transportation sector.

In this paper, the spread of worms as a type of malware is modeled with epidemic models. In the proposed model, we have considered the defense mechanism of the benign worm, link prediction, and quarantine of the infected nodes as methods to reduce the spread of the worm in complex networks. For the benign worm, the parameters of the ability to detect vulnerable nodes and the ability to immunization nodes have been considered, and we have investigated their role in reducing the spread of the worm in the network. The vulnerability list of the benign worm is considered as another characteristic of the benign worm and its role in the spread of the worm is analyzed. In the context of dynamic analysis of the model, we have obtained the initial equilibrium point and the basic reproduction ratio. Finally, the proposed model is evaluated on the Barabasi Albert artificial network and the standard data sets and compared with the SIR and SIRV models.

Modeling lithium-ion battery states plays a crucial role in supporting engineering education, yet applying existing models in the classroom poses challenges. This paper presents an electrochemical/thermodynamic analytical model with 19 parameters for hard-pack lithium-ion batteries, providing efficient and compact MATLAB code as an instructional tool for engineering education. The paper elucidates the mechanisms of electrochemical/thermodynamic behavior evolution in lithium-ion batteries under thermal abuse and develops a state evaluation model based on ordinary differential equations. The highly nonlinear dynamic problem is discretized into a series of static problems, which are solved using the Levenberg–Marquardt algorithm. The MATLAB program is applied to prismatic and cylindrical lithium-ion batteries, yielding critical venting points and state evolution curves, such as temperature, pressure, gas production, heat generation rate, and reaction rate. The comprehensive results vividly demonstrate the evolution of electrochemical and thermodynamic behavior in lithium-ion batteries, aiding students in grasping complex concepts within the course. The modeling and solution process, along with the discussion of algorithm parameters, are expected to enhance students' programming skills and engineering thinking. The proposed algorithm demonstrates second-level efficiency and good convergence, highlighting its potential for classroom applications.

Fuzzy mathematical operations play an important role in the field of decision-making. Decision-making tools are being used in every field of life. Fuzzy operators are the building blocks for making a decision in the realm of uncertain information. The information is often in qualitative form which needs a qualitative approach for decision-making rather than a quantitative one. The linguistic term sets are the mathematical tools to collect the qualitative data from experts of the fields and the conversion of linguistic data in the form linguistic intuitionistic fuzzy data is the more efficient and reliable for the process of decision making. The fuzzy aggregation operators are the best tools for the aggregation of uncertain and vague data. This work addresses a real-world decision-making problem of choosing the best diagnostic approach for the diagnosis of cardiovascular diseases by introducing a novel decision-making technique with fuzzy aggregation operators in the domain of linguistic intuitionistic fuzzy (LIF) sets. Two new operators are used in this method: the Dynamic Linguistic Intuitionistic Fuzzy Dombi Weighted Averaging (DLIFDWA) operator and the Dynamic Linguistic Intuitionistic Fuzzy Dombi Weighted Geometric (DLIFDWG) operator. This work aims to identify an optimal technique for diagnosing cardiovascular illness using Dombi operations in the Linguistic Intuitionistic Fuzzy environment. The Dombi Operations are highly versatile and successful in addressing vagueness and uncertainty, making them crucial in our methodology. To demonstrate the effectiveness of the offered strategies, we have implemented the recommended operators for the selection of optimized diagnostic approach for cardiovascular diseases. This showcases the significance of these strategies in facilitating decision-making. Ultimately, we perform a thorough analysis to showcase the reliability and uniformity of the produced procedures, comparing the provided operators with various current counterparts.