Engineering reports : open access最新文献

The rising expenses associated with car ownership have driven individuals to seek more affordable alternatives, such as car rentals. However, conventional car rental services often come with high costs due to leasing companies' overhead expenses. Consequently, car sharing has emerged as a popular and cost-effective solution that reduces expenses and promotes eco-friendliness by reducing the number of vehicles on the roads. Nonetheless, centralization and reliability remain persistent challenges in car-sharing implementation. To address these issues, we propose a decentralized crowd car sharing and renting platform called CROWDCARLINK, leveraging blockchain technology's power. This innovative platform enables individuals and leasing companies to rent vehicles while securely recording each car's maintenance and lease history on the blockchain. Within CROWDCARLINK, garages are pivotal contributors, adding vehicle information in a reliable and immutable manner. By utilizing blockchain technology, our platform ensures transparency and fosters trust, effectively overcoming the limitations imposed by centralization. Our architectural design incorporates smart contracts, which help streamline processes and facilitate seamless transactions within the platform. To demonstrate the feasibility of our approach, we have developed a prototype utilizing a private Ethereum blockchain with Proof of Authority (PoA) consensus. We believe that the architectural design and the practical solution presented here will play an integral role in shaping the future of smart transportation. Our platform aims to benefit individuals and the environment by offering a cost-effective and efficient solution, paving the way for a more sustainable and advanced transportation ecosystem.

The need to use rotations occurs very often in different domains. We present a basic extensive treatment of rotations in 3D. The results are presented and derived in a coordinate-free setting, where no frames are required and no components of any matrix are manipulated. We start with the direct problem of establishing the finite rotation formula. Then we consider the composition and the vector decomposition of finite rotations. We conclude the paper by considering the inverse problem namely finding the axis of rotation and the angle of rotation from its effect on vectors.

2205 dual-phase stainless steel is widely used in petrochemical equipment. This paper simulates welding, post-weld heat treatment, and operation conditions of 2205 steel. Microstructure evolution and properties were analyzed via metallography, impact testing, SEM, EDS, and electrochemical methods. Results showed microstructure degradation after 580°C heat treatment, with precipitate formation causing toughness loss. Precipitate diameter was ˜38 nm. Critical corrosion temperature and pitting resistance decreased with increasing heat treatment temperature. It is suggested that 2205 dual-phase stainless steel should avoid post-welding heat treatment during the fabricating process. For 2205 dual-phase stainless steel equipment or parts that must or have completed post-welding heat treatment, the risk assessment should be carried out based on the service temperature, etc. If necessary, solution treatment should be carried out to maximize the recovery of material properties.

Wounds contribute to 30%–42% of hospital visits and 9% of deaths but remain underreported in Africa. Diseases and surgeries increase wound prevalence, especially in rural areas where 27%–82% of people live, and health facilities are poor or non-existent. This research aims to design a disease-related wound classification model for online diagnosis and telemedicine support for traditional health practitioners and village health workers. This paper focuses on wounds from diabetic ulcers, pressure ulcers, surgery, and venous ulcers. The approaches used included Contrast Limited Adaptive Histogram Equalization (CLAHE) with machine and deep learning models, Discrete Wavelet Transformations (DWT) with a novel Gated Wavelet Convolutional Neural Network (CNN) model, and FixCaps, an improved version of Capsule Networks utilizing Convolutional Block Attention Module (CBAM) to reduce spatial information loss. The performance metrics showed similar results for the first two approaches, but FixCaps was the most proficient, with accuracy, precision, recall, and F-score of 93.83%, 95.41%, 88.63%, and 90.93% respectively. FixCaps had trainable parameters of about 8.28 MB compared with the 195.64 MB of the Gated Wavelet CNN Model.

The increasing global energy demand driven by climate change, technological advancements, and population growth necessitates the development of sustainable solutions. This research investigates the design, modeling, and simulation of a 2.5 MW solar-wind hybrid renewable energy system (SWH-RES) optimized for domestic grid applications. A survey conducted across 450 households identified a total energy demand of 2.3 MW, with distinct day and night usage profiles. In response, a hybrid system consisting of a 1.5 MW solar park and a 1 MW wind energy unit was designed to ensure continuous power supply. The system was modeled and simulated using MATLAB, and its performance was evaluated through a detailed Total Harmonic Distortion (THD) analysis. This research addresses the critical need for a sustainable and high-quality power supply by designing, modeling, and simulating a 2.5 MW solar-wind hybrid renewable energy system (SWH-RES) optimized to meet the energy demand of a surveyed 2.3 MW domestic load, while also reducing THD to acceptable levels for improved power quality and grid stability. The results demonstrated a significant reduction in THD, with voltage THD decreasing from 45.48% to 26.20% and current THD from 8.32% to 2.88% after implementing filtering components. These findings underscore the effectiveness of the proposed SWH-RES in providing stable, high-quality power while addressing the growing demand for sustainable energy solutions.

In the era of Industry 4.0, the New Space Economy, often called Space 4.0, has taken center stage in the satellite industry. The advent of mega-constellations, which entails mass satellite production, necessitates state-of-the-art manufacturing methods. Leveraging Industry 4.0 technologies like the Internet of Things (IoT) and Big Data analytics show great potential for improving the manufacturing, assembly, integration, and testing (MAIT) cycle. This paper focuses on how Industry 4.0 principles enhance data analysis and automation in space manufacturing, illustrated through a case study at an aerospace company, with a focus on the composite sandwich panel manufacturing line. We introduce two key contributions. First, an interactive dashboard is proposed to enhance data analytics capabilities, offering real-time access to insights and key performance indicators (KPIs) for operators and data analysts, and enabling the exploration of customized metrics. This facilitates comprehensive analysis of the entire MAIT process, supporting trend detection, anomaly identification, and areas for improvement to facilitate data-driven decision-making. Second, we present two strategies to tackle the challenges posed by the constrained number of attempts to insert installations on sandwich panels. These strategies are founded on a proposed data analytics approach rooted in Markov chain principles. This approach aids operators in making informed decisions on whether to proceed with additional attempts or discard the insert. By calculating the probability of successful insertions in future attempts, our approach can suitably enhance resource usage and production timelines. The proposed approach is evaluated through stress testing, where three processes insert 212,000 sensor records into Kafka queues at varying throughputs, monitored via Metricbeat for system resource usage. Results show low CPU usage (below 20%), consistent network throughput, and stable average data insertion times after initial peaks, demonstrating the architecture's scalability and efficiency.

With the integration of distributed power generation into the grid, the economic incentive trading market mechanism becomes an effective method to promote carbon emission reduction in microgrids. In this paper, the carbon flow of the integrated energy system is calculated, the carbon emission model and the carbon flow tracing model of the integrated energy system are established, and the optimization model aiming at low-carbon operation of the integrated energy system is constructed based on the master–slave game model. For the user side of the energy system, the dynamic carbon price and electricity price established by the model play a good role in peaking and valley filling for the load part, improving the operation stability of the power system, realizing the optimal scheduling of the power grid system under the background of electric-carbon coupling trading, and encouraging each microgrid entity to participate in the electric-carbon coupling trading actively. The calculation results show that electric-carbon coupling trading facilitates the flexible operation of power grid systems and improves economic benefits. When combined with carbon emission flow in operation, it can promote the low-carbon and clean power system, encourage distributed renewable resources to connect to the power grid, reduce the carbon content of the power system, enable users to actively participate in low-carbon demand response, and promote the effective carbon emission reduction of a multi-microgrid system.

Power system stability is crucial for the reliable and efficient operation of electrical grids. One of the key factors affecting power system stability is the frequency of the alternating current (AC) system while connected with High Voltage Direct Current (HVDC) transmission system. Changes in load demand can lead to frequency deviations, which can have detrimental effects on the stability and performance of the power system. Frequency should therefore be controlled within predefined limits in order to prevent unexpected disturbances that may cause problems to connected loads or even cause the entire system to fail. A broad simulation model of the HVDC transmission system is developed using MATLAB software to evaluate the effectiveness of the proposed controllers such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and optimization of Proportional-Integral-Derivative (PID) controller using Particle Swarm Optimization (PSO) based control strategy for addressing the frequency instability problems. To assess how well the ANFIS, ANN, and PID-PSO controller controls frequency in HVDC transmission system, several situations were simulated, including load disturbances and changes in operational circumstances. The result reveals that the ANN controller performs more accurate results in HVDC transmission system than the other proposed control and, displaying its capacity to successfully reduce frequency deviations and maintained a controlled frequency 50 Hz. Adopted method suggested the easy integration of HVDC with AC grid and enhances the system power quality and stability.

Notwithstanding the abundance of different expository efforts by the engineers and scientists of the aerodynamic—transonic domain for the ever-beseeched question of whether a supercar needs a rear spoiler or a rear wing, the query requires further exploration to come to any satisfactory conclusion. This state-of-the-art bottleneck area is considered as the prime motivation of the paper as it seeks to investigate the aerodynamic phenomena of a notchback type supercar configuration with two different series of modifications—one with a rear spoiler attachment and the other one with a rear wing attached. A simplified Nissan Skyline GT34R CAD model is taken as the base ground vehicle for the computational study while the slant angles of both the rear wing and rear spoiler are changed within the range of 10° to 45°. RANS based k-omega SST turbulence model is used to find the optimal slant angles for the corresponding rear wing and spoiler in terms of drag and lift coefficients, drag and lift force and rolling moment. The modified models with optimally slanted rear wing and rear spoiler along with the base configuration are inspected under different visualization techniques for understanding the aerodynamic phenomena. Contour and vector plots reveal that rear wing attached model has the maximum pressure drop along and over its body showing a sharp increase of 13.7% when compared with the rear spoiler attached model. Aftereffects show that both the turbulent intensity and Y+ wall shear stress is found to be most economical for the rear spoiler attached model, approximately 1.52% and 0.98% less than the base notchback model. Considering, overall aerodynamic performance of all the three proposed notchback type supercar configurations—the one with a rear spoiler attachment is found to be the most equipped one to integrate into future research.

Sudan relies heavily on refined petroleum products for electricity generation, excluding hydropower, contributing to environmental degradation through petroleum combustion. This challenge underscores the need to expand renewable energy utilization. This paper reviews the current status and future potential of renewable energy in Sudan. While hydropower generates approximately 54.6% of Sudan's electricity, other renewable sources contribute only 0.78% to the national grid. Sudan's hydropower capacity stands at 1907 MW, with plans to add 2197 MW. As a Sunbelt country, Sudan has immense solar energy potential, yet it has only constructed a 10-MW solar PV plant (5 MW on-grid). Two additional 10-MW solar projects are under construction, and the government aims to install 2190 MW of grid-connected solar PV and 50 MW of solar thermal energy by 2035. Wind energy remains underutilized, with a single 0.8-MW wind turbine connected to the grid, although a 100-MW wind power plant is under construction. The government envisions 1550 MW of wind capacity by 2035. Bioenergy capacity, predominantly from sugar industry co-generation, totals 199 MW, with less than 20 MW on-grid. Plans aim to install 270 MW of grid-connected bioenergy by 2032. Despite possessing geothermal potential in the Red Sea region, no geothermal plants have been installed. However, 54 MW of geothermal projects are planned by 2030. Additionally, Sudan's nuclear energy program targets two 600-MW reactors by 2030, while tidal energy projects could contribute 1.2 TWh annually to the grid. These initiatives aim to diversify Sudan's energy mix and enhance the country's sustainability.