Journal of Engineering Research最新文献

In this study, a combination of experimental and statistical methods were employed to precisely determine the micro channel depth in ultra-thin metallic bipolar plates fabricated from 316 stainless steel with a thickness of 0.1 mm. The investigation centers on the rubber pad forming process and its role in the production of these critical components for Proton Exchange Membrane Fuel Cells (PEMFCs). To advance the understanding of this manufacturing process, experimental tests were designed using a Design of Experiment (DOE) technique and subsequently developed a predictive model using Response Surface Methodology (RSM). The research reveals a direct, quantifiable relationship between the channel depth in metallic bipolar plates and the magnitude of the applied force. Also, the pivotal role of rubber thickness as a dominant factor influencing channel depth was explored. Furthermore, it is revealed that augmenting the channel depth in these plates is attainable through a reduction in rubber hardness and an increase in rubber thickness. However, the benefits diminish once the rubber layer thickness exceeds a specific threshold. Moreover, the results underscore that the primary influential factors in this process include force, hardness, thickness, as well as the interplay between force and rubber thickness. The proposed model's accuracy is reaffirmed through an average error rate of approximately 5.39%, signifying its reliability in predicting microchannel depth. This research contributes novel insights into the manufacturing of ultra-thin metallic bipolar plates for PEMFCs, shedding light on the critical parameters influencing channel depth and their interrelationships. The significance and engineering value of this work lies in its potential for enhancing the design and manufacturing of metallic bipolar plates for PEMFCs. Precise control of microchannel dimensions is crucial for enhancing PEMFC performance and efficiency. This research not only advances clean energy technology but also contributes to the broader goal of achieving sustainable energy solutions, making it a noteworthy and timely contribution to the field.

The improvement of fault prediction and diagnosis in industrial systems is crucial to minimize unscheduled shutdowns. However, the predictive performance of current models for thermal power plants is limited due to their reliance on single algorithm approaches. Furthermore, there is a shortage of experiments on thermal fired power plant equipment, as most research focuses on nuclear power plants. In this study, we propose a fault predictive stacking approach for a thermal power plant induced draft fan and evaluate the performance of base learners, including Support Vector Machines (SVM), K Nearest Neighbors (KNN), and Random Forests (RF). Our proposed stacking ensemble approach achieved a prediction accuracy of 99.89 % which demostrated superior prediction performance compared to the base methods.

The Zoom program is widely used to manage meetings around the world, but it is easy to leak the associated Personal Meeting ID. The present study aims at improving the security for Zoom meetings during the transmission process. A new encryption algorithm is proposed to achieve this goal. The system includes three steps namely; separating images, scrambling one-half of the image, encrypting, and substituting the other scrambled half. Implementation of these three steps is achieved by using: cutting the image vertically, Arnold Cut Map, and tRNA respectively. The Bioinformatics technique (tRNA) used in encrypting (red and green) colors, and the Arnold cut map used in the scrambling and shifting process in the encryption method. The performance of the proposed system is measured by using a series of tests such as key sensitivity, histogram analysis, information entropy, correlation analysis, NCPR, similarity, and PSNR test. The results reveal a high sensitivity for initial values, resistant to statistical attack, and have the best performance in the NCPR test. It also passes the evaluation criteria for the both encryption method and S-Box. The complexity of the suggested system is represented by the attacker's need to know many chaos parameters, the logistic method (Arnold cut map), the utilized visual cryptographic techniques, and the methods used to generate the S-Box and keys. The consumed time is fewer than AES algorithm because AES takes huge time in the encryption process especially for big data. The proposed key needs to refresh only one time to make big changed and produces high security.

The combined operation of electricity and gas is an important way of an integrated energy interconnection in the future. To meet the demands of multiple interests in the joint operation of electricity and gas, the master-slave game theory model is developed. In game model the power supply company and the gas company are the master players and the household load aggregation cluster is the slave player. The multi-energy joint load characteristics of household load aggregation and the uncertainty of the price demand response are used to obtain a payment function for each party to the game. The master-slave game model is built with the objective of maximizing the benefits on the supply side and minimizing the payments on the demand side. The master uses price as the strategy set and the slave uses demand response as the strategy set. The tripartite game equilibrium solution is screened by an optimal response function combined with Non-dominated sorting genetic algorithm (NSGA). Through the simulation analysis of an example, the frontier solution set of the tripartite game is obtained. Subjective scoring method and K-means clustering algorithm are used to select the optimal solution and discuss the application of the model under the influence of photovoltaic and gas turbine, the application of master-slave game and traditional NASH equilibrium solution, the application of game and general objective optimization in this paper. The model can be used to optimize the economic benefits of the parties in the long-term operation dispatch, and provide a reference for the optimal operation of market decisions under energy interconnection.

The release of nanoparticles into the environment occurs at different stages during their life cycle, with significant harmful effects on the human (e.g., lung inflammation and heart problems) and the ecosystem (e.g., soil and groundwater contamination). While colloids (particles >1 micrometre) behaviour in porous media is influenced by filtration, nanoparticles (<100 nanometres) behaviour is driven by Brownian motion and quantum effects. Recognising these disparities is essential for applications like groundwater remediation and drug delivery, enabling precise strategies based on the differing transport dynamics of colloids and nanoparticles. The extent of the impact of nanoparticle release on the environment is strongly influenced by their type, size, concentration, and interaction with porous media. The main factor preventing the use of nanoparticles for environmental remediation and other related processes is the toxicity arising from their uncontrolled distribution beyond the application points. Finding a suitable dosing strategy for applying nanoparticles in porous media, necessary for the correct placement and deposition in target zones, is one of the significant challenges researchers and engineers face in advancing the use of nanoparticles for subsurface application. Thus, further studies are necessary to create a model-based strategy to prevent nanoparticle dispersion in a porous media. In general, this review explores the transport of nanoparticles in porous media concerning its application for environmental remediation. The aim of this study is captured under the following:

• a)

  Identifying the properties of nanoparticles and porous media to develop an innovative remediation approach to reclaim contaminated aquifers effectively.

• b)

  Identify critical parameters for modelling an effective strategy for nanoparticle-controlled deposition in porous media. This would require a general understanding of the onset and mapping of the different nanoparticle depositional mechanisms in porous media.

• c)

  Identify existing or closely related studies using model-based strategies for controlling particulate transport and dispersion in porous media, focusing on their shortcomings.

During the evolution towards digital agriculture, the pivotal role of tractor riding necessitates a focus on improving operator performance and well-being. While most research has centered around vibration analysis, tangible solutions to control elevated vibration levels remain rare. The study aims to introduce an intelligent ThingSpeak-Enabled IoT (Internet of Things) solution that provides real-time monitoring and generates prompt warning alerts for tractor operators when vibrations exceed safe thresholds. The initial phase involved the real-time measurement of WBV (whole-body vibration) and SEAT (seat effective amplitude transmissibility). Following this, the secondary phase encompassed the analysis and validation of the system in cases where WBV and SEAT exceeded the recommended limits. The experimental design comprised 135 trials by systematically varying tractor ride parameters, including average speed (m/s), average depth (m), and pulling force (kN) levels. Daily vibration exposure response ranged from 0.43 m/s² to 0.87 m/s² with a mean exposure of 0.64 m/s², surpassing the EAV (exposure action value) threshold of 0.5 m/s². The SEAT values ranged between 91.37 and 133.08 with a mean of 108.35, that indicates insufficient seat isolation capacity, i.e., < 100. Statistically, the study ascertained a significant influence of average speed and average depth WBV and SEAT responses at a 5% significance level. It underscores the potential efficacy of altering speed and depth parameters to attenuate vibration exposure levels. Further, the effectiveness of the system was tested through the automatic transmission of warning alerts via emails, text messages, and flashing red LED light on the IoT system. This critical feature provides considerable utility for tractor operators to adjust ride settings, ensuring that the ride remains within safe vibration limits. Furthermore, adopting such an advanced warning system in tractor manufacturing signifies a pioneering step towards sustainably enhancing operator well-being.

A cutting-edge sustainable airplane can improve performance and enhance profitability are the most significant future concerns for the aviation industry. This research aims to analyse and assess aircraft performance of mid-size transport aircraft through retrofit designs. An optimal design was employed using a non-linear optimization approach under two cases fuselage alone (case 1) and a combination of fuselage-wing-empennage (case 2). These two retrofit concepts were evaluated and compared with the baseline A310–200 aircraft. To compute the objective function for various cases, non-linear solver was used from MATLAB optimization tool box. Additionally, the parametric studies of aerodynamics, structural and operational performance analysis are carried out by using Advanced Aircraft Analysis (AAA) 5.0 software. Sensitivity analyses are performed to assess the overall effectiveness of the optimal design. The key findings of this paper obtained the following parameters as Maximum take-off Weight, empty weight, fuel weight, parasite drag coefficient, rate of climb, take-off distance and landing distance are improved by 0.36%, 26.02%, 10.12%, 4.48%, 27.24%, 13.13% and 9.2% respectively. At the end, the paper provides a clear statement on the managerial perceptions and future insights for the upcoming challenges towards sustainable design.

With the introduction of additive manufacturing (AM) methods, processes with the capability of 3D printing of metal materials were noticed. In general, AM processes with the ability to 3D print metals are more complex and costly in terms of equipment than processes that 3D print other materials (polymers and ceramics). Therefore, the use of 3D printing devices with less complexity and cost, such as extrusion-based 3D printers, for metal printing has attracted the attention of researchers. In this research, an extrusion-based 3D printer, equipped with a direct granule extruder system, was used for 3D printing of the feedstock of the metal powder injection molding (MPIM) process (with 93.7 % by weight of 440 C stainless steel metal powder). The optimal parameters of printing including linear speed (10 mm/s), temperature at the beginning (140 $℃$) and end of the barrel (180 $℃$), chamber's temperature (60 $℃$), and nozzle diameter (0.5 mm) were determined experimentally. The debinding and sintering process was performed on 3D-printed samples. The samples are brittle after the sintering process, in such a way that the failure of the samples occurs by applying a small amount of force to them. According to the scanning electron microscope (SEM) micrograph of the fracture cross-section of the sintered samples, the number of cavities and defects in the samples was negligible and the presence of residual carbon can be seen in the sintered samples. According to the results of X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDAX), the oxidation of iron and chromium elements as well as the presence of residual carbon (7.8 %), are the main factors for the incomplete sintering of the 3D printed samples.

The knowledge classification technology has significant implications for the intelligent research of industries. In the field of whole-process engineering consulting, manually reading and processing large amounts of text data is both time-consuming and laborious. Knowledge classification technology can automatically classify these text data and extract key information, which can improve industry work efficiency. In this study, a deep learning-based text knowledge classification method is proposed to address the large-scale text classification problem in the whole-process engineering consulting field. Firstly, pre-trained language models such as RoBERTa, BERT, and Longformer-RoBERTa are used to extract features from text. Secondly, a multi-label classification model is used to classify the text. Experimental results show that the proposed method performs better than other commonly used models in both overall classification performance and individual category classification performance. Moreover, when the text knowledge classification model is integrated as a text representation module with common classification models such as CNN and LSTM, its performance is inferior to that of a pure classification model. The proposed text knowledge classification method is of great significance for the application in the field of whole-process engineering consulting and provides an effective solution for intelligent research in engineering consulting.

Landfill leachate is generated as a consequence of water percolation through the solid waste, oxidation of the waste, and corrosion of the waste. Under-designed landfill sites allow the leachate to easily pass through the soil strata. To identify the effect of contaminants on the engineering properties of the clay liner, a pilot-scale lysimeter was developed. Experiments on two types of soils polluted with leachate were conducted which included silty clay and soil enhanced by chemical stabilization. Lime and silica fume are used as reactive barriers in landfill earthen liners. The leachate generated was monitored periodically and sampled for (pH, Electrical conductivity, Chemical oxygen demand, Chloride, Total dissolved solids, and total suspended solids). Lime (L), silica fume (SF), and lime-silica fume (L-SF) mix have been used for stabilizing soil. Three percentages were used for lime (2%, 4% and 6%) and three percentages were used for silica fume (3%, 5% and 7%) and the optimum percentage of silica fume (5%) was mixed with the proportions of lime. The optimum lime values are 4% from the research and 5% for silica fume. Soil samples were analyzed over a maturation period of 135 days for each run to identify the chemicals and their effect on the soil characteristics. The plasticity index of the stabilized soil continues to decline until it reaches 36% over the 135 days of the maturing period. Furthermore, an increase in silty clay soil hydraulic conductivity was observed from 3.3 × 10⁻⁶ cm/sec to 5 × 10⁻⁶ cm/sec at 135 days of maturation. Whereas there is a sharp decrease in hydraulic conductivity to achieve a maximum value in the stabilized soil (1.25 ×10⁻¹⁰ cm/sec). Based on laboratory test results, it can be concluded that soil stabilization can improve the geotechnical properties of landfill earthen liners due to pozzolanic reactions.