International Journal of Engineering
Volume:37 Issue: 7, Jul 2024

The aim of the current research is to investigate the pile group functions in liquefaction prone soil under the influence of far and near earthquakes and structural responses such as acceleration history, displacement and bending moments. The method of estimating the response spectrum of the building was used according to the distance, size, structural conditions and fault mechanism and measuring the response spectrum of a selected recorded or artificial earthquake. By using opensees software, Midas Gts, different modes of research were formed. In the selection of earthquake accelerometers for the far and near areas, Peer Seismography Center, Iran Accelerography Center and Road and Housing Research Center were used to select and modify the accelerometers. The results of the research showed that increasing the diameter of the pile reduces the possibility of liquefaction, so that by increasing the diameter of the pile from 0.5 m to 1 m, the possibility of liquefaction and collapse of the pile group decreases by about 33%. In addition, with an increase in the distance between the piles, the probability of liquefaction increases, so that the probability of liquefaction of the group of piles and its collapse increases by about 14% when the distance between the piles increases from 1.5 to 5 meters. Finally, using the results of numerical modeling and with coding software, a suitable model for the pile group against liquefaction was presented for earthquakes in the far and near domains.

The flaring of associated gas remains a problem for oil and gas fields that are difficult to access and remote from the infrastructure. Active development of oil and gas production in Eastern Siberia has led to the fact that transportation capacities cannot keep up with field development. Increased flaring of associated  gas leads to a significant increase in greenhouse gases such as carbon dioxide and methane. A possible solution to this problem is to store gas in the aquifer of the field  for its future sale and monetization through the main gas pipeline. This paper analyzes the main technologies of associated  gas utilization and reveals the problem of remoteness from gas transportation infrastructure of hard-to-reach fields. An effective technology to solve this problem is the creation of temporary underground storage of associated gas in the aquifer of the field. The results of hydrodynamic modeling of realization of this technology with partial replacement of cushion gas showed that joint injection of carbon dioxide and nitrogen before hydrocarbon gas allows to increase the ratio between produced and injected gas, which indicates its greater efficiency. It is recommended that in order to implement the technology, when selecting a geological injection site, to focus on aquifers with a temperature above 31.2℃, which will allow carbon dioxide to remain in a supercritical state in reservoir conditions.

The article examines methods for assessing the efficiency of electric mining excavators, emphasizing the inseparability of operational efficiency from the operator-machine ergatic system. It reviews methods for evaluating operator skills via experimental data and proposes a comprehensive approach to assess the excavator's operational efficiency and the operator's skill level. This method includes analyzing the machine's operating time and energy efficiency using a simulator, thereby offering a novel perspective on the dynamic interaction between human operators and automated systems. With the working cycle's duration measured by the ratio of average to nominal cycle times, and energy efficiency assessed through the comparison of specific energy consumption to theoretical values. The findings suggest prioritizing reductions in operating cycle time for suboptimal machine control and focusing on improving bucket fill rates to enhance energy efficiency. Moreover, the study underscores the potential for utilizing these methodologies in real-world applications, aiming to optimize the utilization of mining equipment and thereby significantly contribute to the advancement of operational methodologies in the mining sector.

In this article, the central difference Kalman filter (CDKF) has been used to estimate the parameters of two different models of synchronous generator (SG) in the presence of noise. It should be mentioned that there are different models of synchronous generators with different levels of accuracy for use in estimation algorithms. The estimation algorithm in this paper uses a smaller number of measurement inputs to estimate the states and unknown parameters for two exact models of the synchronous generator. The central difference Kalman filter (CDKF) is a member of the Kalman filter family, which, like the unscented Kalman filter (UKF), uses sigma points to model nonlinear equations. The differential Kalman filter (CDKF) provides better results than the unscented Kalman filter. In this research, by using two synchronous generator models with different parameters in three scenarios, the ability of the Kalman filter of the central difference is challenged, which shows that this method is very efficient and reliable.

A pervious mortar filter (PMF) is a modification of pervious mortar and pervious concrete designed as a water filter that, based on its physical characteristics, can reduce turbidity and bacteria. However, chemically, it contains minerals that can dissolve upon contact with water and be found in effluent. This study aimed to determine the performance of PMF in treating surface water by reducing turbidity and Escherichia coli and to assess its leaching potential. PMF specimens were created by mixing sand (0.6–0.85 mm), cement, and water with sand-to-cement ratios (M) of 4 and 5 and a water-to-cement ratio (w/c) of 0.4. Each mixture was then molded into pipes with a diameter of 8.2 cm and different thicknesses: 3, 5, and 10 cm. Raw surface water was used for the performance and leaching tests. Results showed that PMF effectively removed 95% turbidity and 99.71% E. coli, which increased with the filtration duration. PMF reduced E. coli more effectively when designed with a thickness of 10 cm than 5 or 3 cm because it would provide more surface areas for suspended solids and bacteria to attach and be retained. More substantial increases (mean %) of pH, hardness, calcium ions, and TDS were observed in PMF M4 with a thickness of 10 cm than in thinner ones because it contained more cement that would dissolve when in contact with water.

Air, liquid, and oil are commonly used for the cooling of electric vehicle motors. Phase change materials (PCM) are not extensively used apart from electronic components. In general, coolants like air, liquid, and oil were separately used as independent coolants for the motor. In this research, two cooling channels were added to the motor cooling. The liquid is used as a primary coolant, while PCM is used as a secondary coolant. This novel method of cooling helps to keep the bracket temperature under the allowable limit even though the liquid cooling is operating at its lowest operating point. In the study, the primary focus was on the PCM coolant channel by keeping the liquid coolant under one particular operating condition to study the PCM in detail. The thickness of the PCM had an influence on the motor cooling. Three different PCM channels were studied with thicknesses of 6 mm, 8 mm, and 10 mm. The best cases were identified with a 6 mm PCM thickness, which is better in terms of heat transfer improvement of 6% observed.

A multi-phase permanent magnet synchronous motors (PMSM) has applied popularly in the field of industry (e.g. trucks, ship propulsion, mining, etc) due to its high torque, efficiency and reliable operation.  So far, many researchers have studied the multi-phase PMSM (e.g, a three-phase PMSM, a six-phase PMSM) for electric vehicle applications. But, there are still significant limitations in the quantity of research on the six-phase PMSMs. Particularly, when researching this type of motor, authors mainly have provided specifications of the six-phase PMSMs and then conducted experiments on these machines without giving the detailed formulations to analytically compute and design dimensions and electromagnetic parameters.  In this research, an analytic model is first developed to determine the main parameters of a six-phase surface-mounted PMSM (SPMSM).  The finite element method (FEM) is then introduced to simulate and compute electromagnetic parameters, such as the current waveform, back electromotive force (EMF), flux density distribution, output torque, cogging torque, torque ripple and harmonic components. The development of proposed methods is applied on a practical problem of a six-phase SPMSM of 7.5kW.

Hostile post on social media is a crucial issue for individuals, governments and organizations. There is a critical need for an automated system that can investigate and identify hostile posts from large-scale data. In India, Gujarati is the sixth most spoken language. In this work, we have constructed a major hostile post dataset in the Gujarati language. The data are collected from Twitter, Instagram and Facebook. Our dataset consists of 1,51,000 distinct comments having 10,000 manually annotated posts. These posts are labeled into the Hostile and Non-Hostile categories. We have used the dataset in two ways: (i) Original Gujarati Text Data and (ii) English data translated from Gujarati text. We have also checked the performance of pre-processing and without pre-processing data by removing extra symbols and substituting emoji descriptions in the text. We have conducted experiments using machine learning models based on supervised learning such as Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive-Bayes, Logistic Regression, K-Nearest Neighbor and unsupervised learning based model such as k-means clustering. We have evaluated performance of these models for Bag-of-Words and TF-IDF feature extraction methods. It is observed that classification using TF-IDF features is efficient. Among these methods Logistic regression outperforms with an Accuracy of 0.68 and F1-score of 0.67. The purpose of this research is to create a benchmark dataset and provide baseline results for detecting hostile posts in Gujarati Language.

It is critical to evaluate the estimation of the fatigue lifetimes for the piston aluminum alloys, particularly in the automotive industry. This paper investigates the effect of different normalization methods on the performance of the fatigue lifetime estimation using Extreme Gradient Boosting (XGBoost), as a supervised machine learning method. For this purpose, the dataset used in this study includes various physical and experimental inputs related to an aluminum alloy and the corresponding fatigue lifetime outputs. Furthermore, before fitting the XGBoost model, different fatigue lifetime preprocessing methods were utilized and evaluated using metrics such as Root Mean Square Error (RMSE), Determination Coefficient (R2), and Scatter Band (SB). The results indicate that modeling fatigue lifetime with logarithmic values as a preprocessing method excels when XGBoost is trained with 100% of the data. However, other normalization methods demonstrate superior accuracy in estimating test data with a 20% test and 80% train set split.

In the realm of wind power generation, cascaded doubly fed induction generators (CDFIG) play a pivotal role. However, the classical proportional integral derivative (PID) controllers used within such systems often struggle with instability and inaccuracies arising from wind variability. This study proposes an enhancement to overcome these limitations by incorporating a single hidden layer neural network (SHLNN) into the wind power conversion systems (WPCS). The SHLNN aims to complement the PID controller by addressing its shortcomings in handling nonlinearities and uncertainties. This integration exploits the adaptive nature and low computational demand of SHLNNs, utilizing historical wind speed and power data to form a more resilient control strategy. Through Matlab/Simulink simulations, this approach is rigorously compared against traditional PID control methods. The results demonstrate a marked improvement in performance, highlighting the SHLNN's capacity to contend with the intrinsic variabilities of wind patterns. This contribution is significant as it offers a sophisticated yet computationally efficient solution to enhance CDFIG-based WPCS, ensuring more stable and accurate energy production.

The Trans-Sumatra Toll Road (TSTR) located in Indonesia operation which is under the management of State-Owned Enterprises (SOE) faces several challenges and requires alternative sources of financing and income, one of which is Land Value Capture (LVC)-based area development. This research aims to identify and analyze the Critical Success Factors needed to implement land value capture in the TSTR project. The results of this research obtained five success factors with the highest ranking and a model of the relationship between variables in implementing land value capture in SOE assigned toll road operations based on the consensus of the expert. Through literature studies, 40 success factors were grouped into five categories and 14 criteria to successfully implement land value capture on toll road-based infrastructure that experts validated. The validated success factors were processed through a series of expert assessments using the Delphi-Method questionnaire, resulting in five success factors with the highest ranking in each category. The relationships between variables were further obtained from PLS-SEM modeling and were analyzed. The analysis of the relationship model produced relationships between variables including Government Policy, Toll Road Developer Business Model, Asset/Property Management, Investment Supporting Environment, LVC Planning and Specific Project Conditions. The present study result may determine factors that can ensure the successful completion of toll road projects with SOE assignment scheme.

An accurate analytical model is presented for drain current of the heterojunction tunneling field effect  transistor, taking into account the source depletion region, mobile charges and the effect of the drain voltage. This model accurately predicts the potential distribution not only on the surface but also within the semiconductor depth by utilizing newly formulated mathematical relationships. Using the tangent line approximation method and considering the channel region as well as the source depletion region’ We analytically calculate the band-to-band tunneling current from the source to the channel by integrating the tunneling generation rate. Compared to simulation results, the proposed model demonstrates significant accuracy in predicting drain current.

The objective of this study is to investigate the nucleation timing of gas hydrate molecules in oil flows. This research focuses on examining how paraffin particles impact the formation timing of hydrate deposits during the mechanical production of oil. A thorough comprehension and control over the formation of organic deposits within the wellbore can substantially mitigate equipment maintenance expenses, enhance the safety and consistency of production, and bolster the economic viability of extracting hydrocarbons. The initial segment of the paper outlines a methodology for identifying the formation depths of gas hydrates and asphaltene-resin-paraffin deposits (ARPD) in operational oil wells through the resolution of thermobaric differential equation systems. Subsequent laboratory experiments were conducted to assess the nucleation timing of gas hydrates in the presence of paraffin. These tests were performed in a specialized high-pressure autoclave that enables the establishment of requisite thermobaric conditions. An internal agitator in the autoclave facilitates the needed dispersion within the system to emulate well flow conditions. Experimental findings revealed that paraffin particles impede the formation of gas hydrate deposits and decelerate their nucleation process. Notably, a 3% increase in paraffin concentration within the mixture was observed to prolong the nucleation timing of gas hydrates by a factor of nine. Based on the review of available literature, it is deduced that further comprehensive investigations are essential for the advancement of a temporal model governing the operational dynamics of production wells under the influence of gas hydrate and ARPD formation.

In this paper, a new dual-stator axial field flux-switching permanent magnet (DSAFFSPM) motor has been proposed to improve the torque density and cost of the machine. In this topology, the 12-pole dual-stator has been located on both sides of one 10-pole inner-toothed rotor. The dual-stator has hosted permanent magnet (PM) type of Bar-PM and the coils. The novelty of this study is development of a technique that can be implemented on PM of the DSAFFSPM structure. In this regard, the proposed analytical design with a sizing equation has been presented and multi-objective optimization is employed to achieve the optimum size by Multi-Objective Genetic Algorithm (MOGA) method. The machine characteristics are acquired and analyzed utilizing the 3D finite element method (3D-FEM). A comparative study has been done to prove the superiority of the performance indices. This topology demonstrates the high-power density and the low vibration and noise due to lower torque ripple and cogging torque. Meanwhile, the Bar-PM topology has lower core loss and thermal stress due to high-efficiency. Consequently, the proposed model provides high torque density and low cost, specifically designed for electric vehicle (EVs) applications.

This study presents the optimized shape and thickness of thin continuous concrete shell structures, minimizing their weight, deflection, and elastic energy change while meeting the performance requirements and minimizing material usage. Unlike previous studies that focused on single-objective optimization, this research focuses on multi-objective optimization (MOO) by considering three objective functions. This combination of objective functions has not been reflected in previous research, distinguishing this study. The computational design workflow incorporates a parametric model, multiple components for measuring objective functions in the grasshopper of Rhino, and a metaheuristic algorithm, the non-dominated sorting multi-objective genetic algorithm (NSGA-II), as the search tool, which was coded in Python. This workflow allows us to perform form-finding and optimization simultaneously. To demonstrate the effectiveness of this metaheuristic algorithm in structural optimization, we applied it in a case study of a well-known shell designed using the physical prototyping hanging model technique. Interpretations of samples of optimized results indicate that although solution 1 weighs nearly the same as solution 2, it has less deflection and strain energy. Solution 3, with a three-fold mass, has significantly less deflection and strain energy than solution 1 and solution 2, with deflection reductions of over 50 and 17%, respectively. Solutions 3 and 4 show better deflection and strain energy performance. Furthermore, a comparison of the MOO results with the Isler shell revealed that this method found a solution with less weight and deflection while being stiffer, confirming its practicality. The study found that MOO is a reliable method for form-finding and optimization, generating accurate and reasonable results.

Banks can design more efficient methods for customer acquisition by utilizing social media platforms. By monitoring information from social media platforms, banks can analyze customers' reactions to offer by competitors and adopt appropriate strategies to increase customer satisfaction and attract new customers. Present research focuses on analyzing the role of social media in the acceptance of mobile banking at Bank Melli of Qazvin Province. This research is of a survey and applied nature. The data collection tool is a survey, and the data collection method is fieldwork. The study population includes all managers and deputies of Bank Melli of Qazvin Province in various positions. The sample size is calculated using the Cochran formula due to the limited population. In this study, the indicators were identified, and hypotheses were formulated based on these indicators. Finally, using statistical techniques, all the proposed hypotheses were proven and the impact of all indicators was confirmed. The results have confirmed the effect of social media performance on the individual recognition of mobile banking consumers.

The paper presents a new scientifically based approach for the selection of sand control technology, which is dedicated to enhance the efficiency of the development of unconsolidated reservoirs. Established laboratory and methodological complexes for physical simulating of the sand producing process were analyzed in order to obtain new knowledge and confirm the available theories. All of them have their advantages and disadvantages, but their simultaneous application revealed characteristic dependencies between the sand production and the studied parameter (grain size distribution, pressure drop, clay content, water cut, gas/oil ratio, etc.). Author proposed concepts of mathematical apparatus improvement to increase the quality of assessing the ability of formation fluids to transport particles of different grain size distribution within the formation, as well as in the inner part of tubing. The effect of each of the characterizing factors on suspended solids concertation (SSC) was studied as a result of more than 300 laboratory experiments. According to the observation, there is a sharp decrease in SSC after the first stage (sampling). Thus, the author determined that the main inflow of mechanical impurities occurs during flow stimulation and after shutdowns. In conclusion, author substantiated the method for limiting sand production using polymers with shape memory based on the results of the performed set of tests. Proposed method allows limited passage of particles with diameter less than 50 µm, which creates conditions for noncolmaticity of screen while maintaining geomechanical stability of bottom-hole formation zone.

The economic production quantity (EPQ) model considers the production rate, demand rate, setup costs, holding costs, and shortage costs to find the production quantity that minimizes the sum of these costs. The goal is to balance the costs associated with production, holding inventory, and potential shortages. In this paper, two objectives include the costs of production and ordering and others in a separate objective function. In the objectives of the other costs, The cost of storage space as a supply is defined to be minimized. This study considers scrap and reworks in the EPQ model. This inventory model accounts for many items on a single machine. The production capacity is reduced, and there are shortages when only one machine exists. By determining the quantities of the products produced by the manufacturing facility, the storage space for each product, cycle time, and product scarcity, we can reduce both the overall cost and the supply cost of warehouse space due to non-linearity and the inability to solve commercial software in large dimensions, a multi-objective meta-heuristic algorithm, namely the non-dominated sorting genetic algorithm (NSGA-II), is used. The findings are further validated using the non-dominated ranking genetic algorithm (NRGA). Also, the obtained Pareto front is studied with several indicators. To perform these two algorithms at the best condition, we employed the Taguchi approach and related orthogonal arrays and performed algorithms for each array considering several factors. Also, to validate the mathematical model, we used the augmented epsilon-constraint method executed in the GAMS environment. It is clear that GAMS commercial software yields better results; however, these two algorithms are justifiable when the problem becomes bigger. Finally, by performing a sensitivity analysis for these indicators and the objective functions, the behavior of the proposed algorithms is compared and examined in detail. Also, the superior algorithm is chosen using the TOPSIS as a multi-criteria decision-making method. Numerical examples show how the presented model and the proposed algorithms may be used efficiently. A surveying literature review clarifies that the related objective functions, constraints, and solution approaches have not been investigated until now.

The absence of active export consortia and the lack of a technical, serious, and codified plan for their development are among the most important reasons for Iran's small and medium-sized enterprises (EMSs) remaining in the country's export coordinates. In this study, the data are collected and analyzed with a mixed (qualitative-quantitative) approach, which is a critical paradigm. The data are collected using library research and field methods. In the field section, structured, exploratory, and collaborative interviews are used in the qualitative phase, and the researcher-made questionnaires are used in the quantitative phase. The data are analyzed using grounded theory, brainstorming sessions, fuzzy cognitive map (FCM), fuzzy inference system (FIS), and system dynamics modeling (SDM). According to the results, "features of consortium members", "export operational plan", "consortium strengthening factor", "recognition of export support", "transnational factors", "government factors", and "product features" are the seven main success factors of private sector export consortia in Iranian industries. Furthermore, identifying a suitable promoter, identifying potential members, conducting the desired study and contacting interested companies, appointing representatives, holding meetings between potential members, conducting a feasibility study and preparing a business plan, officially forming a consortium, and following up on consortium affairs are eight steps for establishing private sector export consortiums in Iranian industries.

The relentless growth of global energy consumption poses a multitude of complex challenges, including the depletion of finite energy resources and the exacerbation of greenhouse gas emissions, which contribute to climate change. In the face of these pressing environmental concerns, the manufacturing sector, a significant energy consumer, is under immense pressure to adopt sustainable practices. The critical intersection of energy consumption management and production operation scheduling emerges as a pivotal domain for addressing these challenges. The scheduling of common operations, exemplified by the cutting stock problem in industries like furniture and apparel, represents a prevalent challenge in production environments. For the first time, this paper pioneers an investigation into an identical parallel machine scheduling problem, taking into account common operations to minimize total energy consumption and total completion time concurrently. For this purpose, two bi-objective mixed integer linear programming models are presented, and an augmented ε – constraint method is used to obtain the Pareto optimal front for small-scale instances. Considering the NP-hardness of this problem, a non-dominated sorting genetic algorithm (NSGA-II) and a hybrid non-dominated sorting genetic algorithm with particle swarm optimization (HNSGAII-PSO) are developed to solve medium- and large-scale instances to achieve good approximate Pareto fronts. The performance of the proposed algorithms is assessed by conducting computational experiments on test problems. The results demonstrate that the proposed HNSGAII-PSO performs better than the suggested NSGA-II in solving the test problems.