Journal of Engineering Research最新文献

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.

The objective of this study is to present the electromagnetic and the thermal multi-field coupling model of the induction heating process for numerical simulation based on the finite element method. One of the most important difficulties encountered in the induction heating processes is to ensure homogeneous temperature distribution throughout the part. To improve the uniform temperature distribution in the sheet, the induction heating system is modelled with the ANSYS software taking into account some operational and geometrical parameters including current density and coupling distance between induction coil and sheet. Induction heating simulations were performed for all simulations at 20 kHz frequency ANSYS Maxwell. The numerical model has been verified by the conducted experiments for Ti6Al4V at the current of 50 A, 125 A, and 200 A, and the 1 mm and 3 mm gap distances. The relative error of the maximum temperature between the experiment and simulation was found around 14 % recorded at 25 s measurements. In addition, the effects of the current and the frequencies on the induction heating were evaluated by the verified numerical model for epoxy/carbon fiber (UD prepreg) and epoxy/carbon fiber (Woven prepreg) plates. The results show that the induction heating model is suitable and efficient to determine the temperature distribution within the thin plates by the finite element method.

Power prediction in solar powered electric vehicle (EV) charging stations is very essential for smooth and uninterrupted operations due to the high oscillatory output of renewables and their dependence on various atmospheric factors. The need for early prediction helps EV stations improve their power performance and utilize available power by designing intelligent charge scheduling algorithms. This study introduces a novel design approach for an off-grid photovoltaic (PV)-powered EV charging station, which involves three main stages: evaluating and analyzing different solar irradiance prediction models (theoretical, empirical, and artificial neural network (ANN) models), forecasting day-ahead solar power profiles, and optimizing charge scheduling for pre-booked vehicles using energy storage systems (ESS). The effectiveness of various solar irradiance prediction models is assessed to identify the best-performing model. The proposed approach employs a novel algorithmic procedure to fine-tune the selected model using a basic dataset. Power prediction simulations are conducted using MATLAB, while Python is utilized for model development. The feed forward neural network (FFNN) model for irradiance prediction has a 0.88 R² score; the anisotropic general regression neural network (AGRNN), isotropic GRNN both have 0.94 and 0.95 R² values for direct PV current prediction, providing a strong base for reliable forecasting models. The significance of ESS backup for effective charging stations is clearly demonstrated by a remarkable 20 kW peak shaving.

Natural fiber has earned great attention for its discovery as a green material in dielectric composites. Their excellent dielectric properties have granted them the great capability to be used in high dielectric composites. Hence, this study attempts to determine the most influential factor contributing to the permittivity of pineapple leaf fibers and to examine the morphological structure of the developed fibers. The two-level factorial analysis was applied to determine the significant, influential factors and the best conditions contributing to the permittivity value of fiber. The factors include the pineapple leaf-to-soda ratio (1:5 and 1:10), soda concentration (5–10 wt%), temperature (60–100 ℃), and pulping time (45–75 min). The fiber was extracted from the pineapple leaf through the soda pulping method, and the content was analyzed by the Kurschner-Hanack method. Based on the analysis, the pineapple leaf-to-soda ratio was observed as the most significant factor contributing to the permittivity value of fiber, with an 8.86% contribution. The best conditions were suggested at a 1:10 pineapple leaf-to-soda ratio, 5 wt% soda concentration, 100 ℃ temperature, and 45 min of pulping time, contributing to the 1.85 permittivity value of pineapple leaf fiber. The scanning electron microscope images of the material under test indicate that the morphological structures play a crucial part in determining the permittivity value of fiber. Therefore, with suitable processing factors, pineapple leaf fiber can be a great dielectric material used in many engineering applications.

A regenerative plant is examined with respect to cost and thermodynamic optimization using the power-quantity ratio (PQR) and the classical thermal efficiency. The PQR is compared with the thermal efficiency index in adaptive response intended for low carbon footprint in power production. The Dataq PLC is employed for smart embedded PC control. The ranges of values of the vaporizer pressure and temperature are 5–20 MPa and 350 – 580 ^oC, respectively. By specifying the values of the thermodynamic variables in the Engineering Equation Solver (EES) software environment which was used to computerize the thermodynamic relations, the heat input and work output as well as the classical efficiency index are calculated.