International Journal of Engineering
Volume:36 Issue: 10, Oct 2023

The purpose of this paper is obstacle avoidance and moving target tracking for a quadrotor. Solving the obstacle crossing problem for the quadrotor includes two parts. The first part is controlling the attitude and position. The second part is path planning to pass obstacles. In this paper, the attitude and position of the quadrotor are controlled by super-twisting sliding mode control (SMC) and non-singular terminal super-twisting SMC. The simulation results of these two methods were compared. In the non-singular terminal ST-SMC method, the convergence time was approximately 5% less than the super-twisting SMC method. Also, the non-singular terminal ST-SMC method has more ability to remove disturbances. Because of the better results, the non-single terminal ST-SMC was used to control the position and attitude of the quadrotor to cross obstacles and track the target. In the second step, to cross obstacles, the potential field path planning algorithm is used. This method is a combination of attraction towards the target and repulsion from obstacles. The results of the simulation of crossing the obstacles were presented in four missions. In the first mission, the obstacles and the target are static, and in the second mission, the obstacles are static, and the target is moving. In the following, the obstacles and the target are dynamic; in the last mission, a combination of static and dynamic obstacles is considered, and the target is moving. The simulation results of four missions show that the quadrotor does not hit obstacles and reaches the desired goal, till the applied method is successful.

Fault detection and its restoration is the major challenge for the smooth functioning of the Multi-Level Inverter (MLI). In this paper, fault detection and its clearance scheme for an Open Circuit (OC) fault on a 3-level 5-level Cascaded H-Bridge Multi-Level Inverter (CHBMLI) has been developed and tested to improve the reliability and suitability of the system. An accurate and fast detection, isolation and bypassing of faulty bridges enhance the reliability, suitability, and acceptability of CHBMLI in any domestic, industrial drive applications. To reschedule the line voltage and current value close to the pre-fault level, a Neutral Point Shift (NPS) technique is presented in this paper. The desired output voltage is governed by Level Shift Pulse Width Modulation (LSPWM) technique. The proposed scheme is developed in MATLAB/Simulink environment and results are validated by using Opal-RT simulator. Simulation results has confirmed the performance and Opal-RT simulator results shows feasibility and applicability of the proposed scheme.

Collapsible soils pose significant challenges due to their open structure, which causes settlement when exposed to moisture. Failure to identify these soil types can lead to structural damage when they become saturated or experience changes in moisture content. The presence of such soils in various regions, including Iran, necessitates greater attention and investigation into their behavior and properties. This study examines the impact of butadiene rubber on the stabilization of these soils. Fine-grained soil samples were collected from two different sites in Kerman province (Kerman City). The samples were injected with 2%, 3%, 4%, 5%, 6%, and 7% butadiene rubber for stabilization periods of 4, 7, 14, and 28 days, resulting in a total of 72 tests. The stabilized soils were evaluated using a double consolidation test (ASTM D5333) on intact soil samples. The penetration of butadiene rubber and the resulting rubber columns reduced the degree of collapse. In all cases, the collapse was reduced by more than 88%. The highest reduction was observed with a 7% additive after 28 days of stabilization. Given the increasing use of intelligent systems in predicting the behavior of stabilized collapsible soils, a model was developed to predict the degree of collapse for samples stabilized with butadiene rubber using an adaptive network fuzzy inference system (ANFIS). The accuracy of the model was evaluated, and it successfully predicted the collapse degree. Addition of styrene butadiene rubber additive in the tested soils led to a decrease in the plasticity index of clays with high liquid limits and an increase in the plasticity index of silts with low liquid limits. These changes varied depending on the mineral type. Subsequently, a model was developed to predict the plastic properties of the soil using a fuzzy inference system. The results demonstrate acceptable consistency between the training and prediction data (R2=0.93).

A sample of organic soil collected from the Chaharmahal-Bakhtiari Province, Iran, was treated with 0.5, 1, 1.5, 2, 2.5, and 3% of xanthan gum and 1, 3, and 5% of lime. The untreated and the treated specimens were subjected to physical and mechanical tests, including soil classification, pH measurement, compaction test, unconfined compressive strength test, indirect tensile test, and direct shear test. An increase in lime by 3% led to the greatest increase in the compressive strength (5 and 6 times for the 7-day and 21-day samples, respectively) and the tensile strength (3.7 and 4.5 times for the 7-day and 21-day samples, respectively). Xanthan gum also improved the compressive strength (3 and 6 times for the 7-day and 21-day samples, respectively) and the tensile strength (5.9 and 7.5 times for the 7-day and 21-day samples, respectively). Increasing lime up to 3% enhanced the adhesion of the stabilized soil for 3.5 and 7.5 times that of the organic soil for 7 and 21 days of curing, respectively. Also, the friction angle increased by 40% and 68% times with the increase of lime up to 3% during 7 and 21 days of curing, respectively. Stabilization with xanthan gum led to 11.5 and 17.5 times increase adhesion for 7-day and 21-day samples, respectively. Moreover, xanthan gum increased the friction angle by 47% and 75% for 7-day and 21-day samples, respectively. The findings generally suggest that xanthan gum can be a good ecofriendly alternative to lime as a soil stabilizer.

In the present study, the effect of adding Magnesium (Mg) as a doping element on the morphology and surface characteristics of the chromized layer was investigated. To achieve this, chromized layers were coated and doped by a chromizing process in pack-cementation at 1050°C. The thickness of the doped layer was about 26 µm, whilst chromized was approximately 24 µm. The surface morphology and composition of the coatings were analyzed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX). The results showed that a crystalline structure can be successfully deposited by adding Mg as a doping element to the pack mixture. Therefore, Mg acts as a barrier against Cr2O3 formation, resulting in a more rich-chromium-zone and forming protective oxide. Moreover, less carbide is formed in the doped layer. The roughness of the layer is enhanced by adding Magnesium (Mg) and it has a lower average roughness (Ra) 3 times than that of chromized, of about 0.315 µm and 1.039 µm, respectively. In addition, progressive loading scratch was performed at 1N and 20N. The results demonstrated that Mg in the chromized layer increases the ability to with-stand varying levels of mechanical stress with strength adhesion of about 19.21N and can be more protective than Cr chromized.

Improving the tools and mathematical methods to diversify the portfolio of oil and gas assets in the face of limited investment, high market volatility, increasing risks and uncertainties at the current level of technology development is a very urgent task. In order to form an effective investment portfolio, the authors proposed asset diversification using cluster analysis, which implies grouping sample objects according to a set of specific features. The method under consideration involves five stages of asset valuation in order to consider those assets in a three-dimensional space, taking into account the specifics of the oil and gas business, including determination of individual asset trajectory, performing spatial approximation, calculating the clustering coefficient, ranking the resulting pairs, and directly solving the portfolio formation optimization problem. This paper provides a reasonable set of metrics for diversification of the investment portfolio based on cluster analysis: main criterion - ∆PV, additional criteria - ∆Production, ∆OPEX/toe, ∆CAPEX/toe, characterized geological, environmental, social and economic aspects. Thus, the proposed methodology provides an opportunity to identify the most attractive investment projects, thereby allowing large oil and gas companies to diversify their business with minimal risk and maximum return on invested capital.

Energy consumption in the building sector, especially in residential buildings, due to the development of urbanization, has taken the largest share among all consumption sectors. Therefore, it is very necessary to predict the energy consumption of buildings, which has been presented as a challenge in recent decades. In this research, adaptive fuzzy-neural inference system (ANFIS) and MATLAB software have been used for forecasting to supply electrical energy to residential buildings whit random data that collected based on the hourly electricity consumption of conventional residential buildings in Tehran. According to the applied settings for the solar and wind energy production has been done by solar panels and wind turbines. The use of renewable energy is one of the ways that can reduce the consumption of fossil fuels and also reduce environmental pollution. Statistical indicators such az MSE, RMSE, µ, σ, and R were used to evaluate the model performance . The obtained values well show the ability of this model to foresee the generation and utilization of energy in privat reresidential buildings with tall exactness of about 96% and 90%, respectively. Therefore, this model well show the ability of to the needed estimates in the mentioned buildings with high accuracy.

In recent years, the scientific community has shown a growing interest in fiber-reinforced concrete (FRC) for modern structures due to its enhanced ductility compared to traditional reinforced concrete (RC). This paper introduces an analytical model that incorporates a comprehensive fiber reinforcing index (RI) to study various types of FRC. The analysis focuses on the compressive and tensile behaviors, damage evolution under cyclic loading, and crack propagation in the concrete matrix. To effectively simulate crack initiation and propagation in FRC structures, the extended finite element method (XFEM) is employed, leveraging its fracture-solving capabilities. Additionally, the XFEM is combined with the concrete damaged plasticity (CDP) modeling approach to examine the quasi-static and hysteretic performance of FRC columns. Three-dimensional nonlinear finite element models are constructed using the commercial software Abaqus. These models incorporate steel fibers, polypropylene fibers, and a combination of both types of fibers in the FRC structures. Furthermore, the accuracy of the XFEM-CDP-based analysis in predicting hysteretic behavior is validated against results from previous research articles, demonstrating reasonable accuracy. It allows engineers to accurately capture the nonlinear behavior of concrete, including cracking, crushing, and plastic deformation, while also considering the complex crack patterns, providing a better understanding of the seismic performance of FRC structures using numerical simulations.

Trajectory tracking and positioning are essential requirements in many areas, including robots and autonomous vehicles. In some cases, such as in areas where GPS signals are weak or not available, trajectory tracking is used as an alternative positioning system. In these cases, simultaneous localization and mapping (SLAM), is of great importance as it does not require prior knowledge and empirical offline fingerprint. SLAM can be combined with signal processing algorithms among which, particle filter stands out. However, challenges exist such as particle weights degradation and particles impoverishment that need to be dealt with. In fact, the loss of particle diversity for estimation has led to the lack of particles. To overcome this problem, one solution is to diversify the selection of particles after resampling. In this paper, we proposed a crow search algorithm (CSA) to overcome these issues and improve position estimation. The simulation results showed that this algorithm greatly improved the performance of fast SLAM.

In the mining industry, cover systems for tailings are an effective means of reducing acid mine drainage. An example of this type of system is the multi-layer cover system that is used in arid climates where the annual evaporation rate is greater than the annual rainfall. For users and designers of mine tailings dams, the current research is aimed at identifying the optimal cover system and engineering of the mine waste disposal site, as well as investigating effective geotechnical parameters in controlling the oxygen gas entering the mine waste disposal site. In this research, after careful examination of scientific literature and data collection, it is first validated using numerical modeling, Finite Element Method (FEM) based on the VADOSE/W software, and then modeling is done based on the collected data. One-dimensional modeling has often been used in studies on the evaluation of cover systems, but in this research, two-dimensional modeling has been used to analyze the behavior of coating systems. The key to the successful operation of cover systems is maintaining the storage layer at a saturation level of about 85% throughout the year. The two cover systems, "storage and release", "optimized storage and release," are ineffective in maintaining the storage layer at about 85% saturation. However, the capillary barrier cover system has worked successfully and maintained the degree of saturation of the storage layer at about 80%. Due to the use of low-sulfide waste material as an oxygen-consuming layer, the performance of all three cover systems was acceptable. However, it is worth noting that the capillarity barrier cover system was able to immediately cut off the diffusion of oxygen due to the high degree of saturation of the storage layer, while in the other two cover systems, this decrease in diffusion and oxygen concentration was gradual. Therefore, the capillary barrier cover system is suggested as the most optimal system according to the weather conditions and the type of waste materials.

This study presents novel research on the seismic behavior of self-centering reduced beam section (RBS) connections in steel structures. Unlike traditional moment steel frames that concentrate non-elastic deformations in energy dissipation devices, the innovative self-centering RBS connections utilize post-tensioning techniques to restore the structure to its pre-earthquake condition. By significantly reducing residual deformations, these connections offer a promising alternative for improving seismic resilience. To validate the effectiveness of the post-tensioned (PT) and RBS connections, advanced nonlinear numerical modeling using the Finite Element Method (FEM) in ABAQUS software is employed. This approach allows for a comprehensive investigation, comparing the numerical results with laboratory data. Furthermore, the study goes beyond existing research by incorporating additional high-strength cables into the RBS connections. This novel configuration aims to assess the impact of post-tensioned cables on seismic behavior, adding a new dimension to the understanding of these connections. Through a rigorous parametric study, the research uncovers crucial insights into the seismic performance of the self-centering RBS connections. Notably, the study reveals the significant influence of the initial post-tensioning force on various aspects, including stiffness, maximum moment capacity, and gap-opening behavior of PT connections. The findings demonstrate the potential of increasing the initial post-tensioning force to enhance the energy dissipation capacity and overall performance of the PT connections. Overall, this study presents pioneering research that advances the understanding of self-centering RBS connections and their potential application in steel structures. By emphasizing the novel aspects of the research, it contributes to the body of knowledge in the field and provides valuable insights for improving the seismic resilience of structures.

The aim of this paper is to propose a new method for controller design using control places in special hybrid Petri Nets called Hybrid-Time Delay-Petri Nets (HTDPN). Most control approaches use the control place of the supervisory control for discrete Petri Nets. However, the new approach uses the place to control the linear dynamical systems which are modeled by the HTDPN tool. This controller consists of control places, transitions, arcs connected to the control place, and weights of the arcs, which are added to the HTDPN model of the system. In this paper, there are three main steps for the controller design. In the first step, the plant is modeled using the HTDPN tool, and in the second step, a controller is designed using the novel method presented. Finally, the weights of arcs connected to the control place are computed using the Lyapunov function theory, which guarantees closed-loop stability. The main advantage of this method is the possibility of using continuous and discrete places simultaneously in nonlinear systems. Unlike most previous approaches, in the proposed method, an expert designer can create a favorite controller in the graphical environment, and then apply changes to the mathematical environment of the HTDPN model. The performance of the proposed controller is evaluated by a comparative study. The comparison criteria in this article are: error criteria (IEA), energy consumption, rise time, settling time and simulation run time. The simulation results showed that the proposed method was 45% and 600% better conditions than the Model Predictive Conrol (MPC) and optimal control methods, respectively.

Different industries, including aerospace, marine, and automotive, widely use titanium alloys such as Ti-6Al-4V. Although, this alloy has excellent properties; it is highly susceptible to corrosion and has low thermal stability and tribological characteristics, limiting its application. In this research, after preparing the Al62.5Cu25Fe12.5 quasi-crystalline (QC) powder mixture and appropriate target, the magnetron sputtering method was employed to deposit the QC coating on the Ti-6Al-4V substrate. The powder mixture and AlCuFe thin films were annealed at 7000C for 2 h. The scanning electron microscope (SEM) analysis and X-ray diffraction (XRD) methods were used to investigate the microstructure and morphology of mixed powders and Al-Cu-Fe QC coatings. NaCl solution (3.5 wt.%) was utilized to conduct the electrochemical measurements. Al-Cu-Fe thin layer deposited on the Ti-6Al-4V alloy surface without any cracks. The XRD patterns related to the annealed powders and the coating after heat treatment indicated the presence of Cu3Al, AlFe3, and quasi-crystalline ternary phases of Al65Cu20Fe15, Al3Fe, AlTi2, and Al65Cu20Fe15 phases, respectively. Based on the polarization test results, the annealed coating at 700°C showed better electrochemical behavior than the Ti-6Al-4V substrate.

The diagnosis of the location of structural damage and its extent after an earthquake using numerical methods is one of the ongoing research topics. After the occurrence of damage in a structure and a reduction in its stiffness, the dynamic characteristics of the structure change, and therefore, assessing the changes in its dynamic characteristics can be used as an indicator for detecting damage. In this article, an advanced technique called Direct Stiffness Calculation (DSC) and a new damage index based on flexural stiffness variations (SVI) are utilized for damage detection in structures. Initially, the proposed technique is examined on a steel beam with known specifications. Then, a reinforced concrete moment frame is modeled, and after extracting its dynamic characteristics, it is subjected to a pushover analysis to create a damage scenario without direct intervention. Based on the analysis results, the plastic hinge formation location at both ends of the beam is selected as the probable location of damage in the floor. By using the modal information of the damaged structure and calculating the SVI in the beams of the floors, it is determined that this index can accurately and significantly distinguish the location of damage only by knowing the first mode of the structure and with sufficient magnification compared to other points. Furthermore, the results demonstrate that with this method, it is possible to accurately determine the location of damage even without knowing the dynamic characteristics of the intact structure and solely with the information of the damaged structure.

Global warming persuades researchers to improve the effectiveness of renewable energy technologies, such as H2 production by methane steam reforming (MSR) an endothermic process. Herein, a nanocatalyst based on open-cell nickel foam 40 (pore per inch) with high thermal conductivity was prepared. The nanocatalyst was synthesized with a chemical stepwise synthesis approach, chemical pre-treatment, pulsed electrocodeposition of Ni-Al2O3(γ) nanoparticles, and calcination. Measurements of thermal diffusivity(α) with flash xenon technique gained 4.41×10-6 m2s-1 and values of specific heat capacity, Cp, by differential scanning calorimetry (DSC) and thermal conductivity(λ) enhanced by 65% in temperature range of 150 to 550°C in Ni-alumina(γ) foam nanocatalyst. Furthermore, characterization and tests for comparing nickel foam and Ni-alumina(γ) foam indicated that the hardness improved from 145 Vickers hardness (HV) to 547 HV and compression strength increased from 1.1 MPa to 5MPa and specific surface area (SBET) from 1.48 m2g-1 to 48 m2g-1. XRD (x-ray diffraction) analysis showed NiO and NiAl2O4 in the structure. The interface between the catalytic component (NiAl2O4), and nickel affected the catalytic ability for MSR, and the efficiency gained at low tempreture 500 °C was the same as reported at 720°C by other investigations.

Earth's natural resources belong to everyone and must be maintained for future generations. Thus, waste management and consumption are researchers' main concerns. Using waste resources to build sustainably. It's growing increasingly popular due to its environmental and economic advantages. This paper uses waste materials to increase the sustainability in construction work with cement-based such as waste concrete as a normal aggregate replacement and ground-granulated blast-furnace slag (GGBFS) as supplementary cementitious materials (SCMs) to improve the sustainability in rigid pavements production, to decrease the use of raw materials, and to reduce the CO2 production in Portland cement (PC) factories. Cubic, cylindrical, and prismatic specimens were prepared in the laboratory with (0%, 10%, 20%, 30%, and 40%) by weight of aggregates waste concrete (WC) as replacements from the natural aggregates. The strength activity index (SAI) of the concrete specimens was in the acceptance strength zone with a slight reduction when compared with the conventional concrete strength. On the other hand, the hybridized effect of using the GGBFS as SCMs with various proportions of GGBFS/PC (0.8, 1.2, 1.6, 2), and WC at (40%, 30%, 20%, and 10%) respectively appeared that SAI was enhanced. Three manufactured specimens in each type of mixture were tested after 7 and 28 days of age curing. The findings from compressive strength, splitting, and flexural tests conducted on mixtures containing recycled aggregate indicate their suitability for use in rigid pavements for secondary roads. It was observed that as the proportion of recycled aggregate in the mixture increased, the strength of the concrete decreased. when ground granulated blast furnace slag (GGBFS) was added to the concrete mixtures, the results varied depending on the ratio of GGBFS present in the mixture. Among the different mixes tested, the mix designated as R30S1.2 was the highest strength load and it can be used for main roads. The results indicate that the use of recycled aggregates at a ratio of 30% and 55% GGBFS gave the best strength results and at the same time reduces the amount of cement and natural aggregates used, and this has an impact on the environment by reducing the presence of waste.

Copper-based alloys are one of the most popular materials in the power distribution, welding industry, hydraulic equipment, industrial machinery, etc. Among different methods for the fabrication of Cu alloys, mechanical alloying (MA) is the major approach due to the fact that this approach is simple, inexpensive, suitable for mass production, and has a high capacity for homogeneous distribution of the second phase. However, the prediction of the hardness of products is very difficult in MA because of a lot of effective parameters. In this work, we designed a feed-forward back propagation neural network (FFBPNN) to predict the hardness of copper-based nanocomposites. First, some of the most common nanocomposites of copper including Cu-Al, Cu-Al2O3, Cu-Cr, and Cu-Ti were synthesized by mechanical alloying of copper at varying weight percentages (1, 3, and 6). Next, the alloyed powders were compacted by a cold press (12 tons) and subjected to heat treatment at 650˚C. Then, the strength of the alloys was measured by the Vickers microscopy test. Finally, to anticipate the micro-hardness of Cu nanocomposites, the significant variables in the ball milling process including hardness, size, and volume of the reinforcement material, vial speed, the ball-to-powder-weight-ratio (BPR), and milling time; were determined as the inputs, and hardness of nanocomposite was assumed as an output of the artificial neural network (ANN). For training the ANN, many different ANN architectures have been employed and the optimal structure of the model was obtained by regression of 0.9914. The network was designed with two hidden layers. The first and second hidden layer includes 12 and 8 neurons, respectively. The comparison between the predicted results of the network and the experimental values showed that the proposed model with a root mean square error (RMSE) of 3.7 % can predict the micro-hardness of the nanocomposites.