International Journal of Engineering
Volume:36 Issue: 4, Apr 2023

The main objective of this paper was to propose the real model of the high speed train regarding the side wind disturbance. In the first part of this article, the turbulent air flow around a simplified design of a high-speed vehicle was numerically analyzed using finite volume method and four RANS turbulence models, including k-ω SST, k-ε RNG, Spalart-Almaras, and Launder and Sharma. The results of numerical simulations regarding the wall function were validated by experimental works and it was shown that in the area near the wall, the SST k-ω model had the best simulation for the horizontal component of the velocity (21% error). The results of the lift coefficients showed that at short distances from the train floor to the ground due to relatively strong wind with angles less than 0.2 radians, the lift coefficient was negative and as a result, the forces acting on the train were downward. Finally it was shown that by increasing the wind angle, this coefficient gradually becomes positive, which can disrupt the stability of the train. It can be concluded that in trains whose floor to ground distance is more than 0.136 of the train height, the balance is maintained only in the absence of side wind.

To probe advantages in Fe-N-SiO2/TiO2 nanocomposite system, the visible photocatalytic degradation of the nonbiodegradable antibiotic oxytetracycline (OTC) by unsupported TiO2 and its modified composites by incorporating each of the Fe, N, and SiO2 dopants under a series of conditions were investigated. The structural and optical properties as well as the morphology of the prepared nanocomposites were also characterized applying Fourier transform infrared (FT-IR), X-ray diffraction, photoluminescence spectroscopy, UV-visible diffuse reflectance spectra and field emission scanning electron microscopy/Energy-dispersive X-ray spectroscopy (FESEM/EDX). In order to develop two models portraying appropriate functional relationships between two main responses (OTC removal efficiency and its specific removal rate (SRR)) and four numerical variables (OTC concentration, catalysis loading, initial pH and reaction time), two separate multivariate analysis pathways under response surface methodology (RSM) were taken. The results obtained all came down to the maximum SRR (220 OTC mg OTC removed/g cat. h) found at the maximum catalyst dosage of 1.5 g/l, and acidic pH of 3 after 0.5 h. Furthermore, the Fe-N-SiO2/TiO2 proved a stable photocatalytic activity during three subsequent reusability experiments, shedding light on its reliable potential for future application.

In this paper, a new model of linear induction motor including the impact of the end-effect on the motor performances is proposed. Moreover, a new strategy of control approach based on the Field-Oriented Control (FOC) technique is suggested and investigated. The proposed approach can provide a robust control strategy and overcome the limitations imposed by FOC technique, which suffers with some drawbacks in linear induction motor (LIM) such as sensitivity to parameter variations and imperfect dynamic tracking performance. In this context, the developed technique combines the benefits provided by the both approaches polynomial (RST) regulator and Model Reference Adaptive System (MRAS) observer, in order to achieve a robust controller by minimizing the external disturbances effects and reducing the influence of parameter variations. Moreover, it is revealed that the proposed conrol approach enables  an improved  rotor speed response with a reduced number of overshoot values as function of mass variations, where the reccorded maximum overshoot value is 8%. Besides, the devopped controller demonstrates a reducded rise and settling time values under wide applied external force conditions. This confirms that the proposed MRAS-RST technique offers a good dynamic response against the parameter variations. The accuracy and control performance of the proposed technique is checked and validated using Matlab/Simulink environment software tool. Simulation results show the effectiveness of the proposed estimator with improved better robustness for RST controller for different reference tracking and disturbance rejection parameters. These significant results make the proposed approach a promising technique dedicated to the design of high-performance controller, which is highly suitable for industrial and electrical applications.

The flood hazard parameter of peak discharge and prolonged inundation due to change in dam regulation is estimated for a water front Geosynthetic Reinforced Soil (GRS) wall on the downstream of large dam. Dam regulation rule level is pre-emptying the reservoir with lower peak discharge to protect the expanding downstream city intruding the flood plain. Present study is to test the hypothesis that rule level has significantly changed peak discharge and inundation duration and also to estimate these two important parameters of flood hazard for different flood return periods. The methodology consists of three parts: first is the categorization of flood data according to major event of flood regulation, second is the distribution test and third is the estimation of design maximum peak discharge and duration of flood. The estimated value of peak discharge and flood duration clearly indicate the implication of dam regulation rule level; the estimated value of peak discharge for 200 years return period is 13.9 lakh Cusecs, which is lower than the flood discharge for year 1968, which was 15 lakh Cusecs, and the estimated flood duration for 10 years return period is 41 days. The most important finding of this study is the substantial increase in duration of flood due to implementation of rule level which will add one more flood hazard parameter for water front geotechnical structure that is prolonged inundation.

The development of location-based services requires an increasingly accurate positioning system technology. Research on outdoor positioning systems has achieved satisfactory accuracy and has been commonly used in various location-based services. The research trend is now shifting toward the Indoor Positioning System (IPS). One technique that is widely used in Wi-Fi-based IPS is fingerprinting. The fingerprinting technique on Wi-Fi uses the Received Signal Strength Indicator (RSSI) value. The problem that occurs is that the results of RSSI measurements on smartphones of different brands will produce different RSSI values, also known as device diversity. Device diversity will cause a decrease in system accuracy. This study aims to offer a solution to the problem of device diversity in Wi-Fi IPS based on RSSI Fingerprinting, i.e., to get a minor distance error. The proposed solution is to modify the original database radio map into two new databases: the difference database and the ratio database. The Difference Database Radiomap was able to reduce the average value of distance errors by 24.3% in Meizu and 28% in OPPO. Then, using the Radiomap database ratio, the average value of distance errors could be reduced by 13% in Meizu and 24% in OPPO. From the calculation, Radiomap database ratio can provide solutions to the problem of device diversity for an Indoor Positioning System better than the difference database radiomap if we looked at reduced distance error.

In the past two decade, researchers have studied the flexural, shear and bond behaviour of recycled aggregate concrete (RAC) beams. This work intends to analyze the behaviour of RAC beams under pure torsion, despite the lack of information on its behaviour under pure torsion. The coarse recycled concrete aggregates (RCA) extracted from construction and demolition (C & D) waste was used to replace natural coarse aggregates (NCA) in 0 %, 50 %, and 100 % ratio. Their recycling could help preserve the environment and promote sustainability through solid waste management. Six beams, each of size 150 x 250 x 1800 mm were prepared and tested. To detects minor deformation and to achieve the same strength through the out-of-plane direction, 250 mm inbuilt cantilever projections were provided on opposite faces of the beams at a span of 1000 mm along the longitudinal axis. The ultimate torsional capacity of tested beams was lower by 7.41 %, 8.60 % and 13.58 % than ATENA-3D (FEM) for 0 %, 50 % and 100 % RCA. The change in the replacement ratio of aggregate has a low impact on the ultimate torque and angle of twist. Based on the experimental and analytical results, it was established that the torque resistance capacity of the RAC beam was reduced as the % of RCA increased. Similar crack patterns and failure behaviour were observed in RAC and NAC beams in both studies. Therefore, it is practically possible to apply RAC in structural applications under pure torsional loading.

One of the topics that have been studied a lot in the field of airline industry optimization is related to flight planning, and air fleets, and how they relate to each other, which is called airline scheduling. Despite the high importance of this issue in the profitability of airline companies and the proper use of their resources, the high computational complexity of these models has led to considering each of them in a mathematical model separately, and as a result, the accuracy of the final decision will be decreased. So far, many articles have studied various relevant issues, in some cases, efforts to create integration in the process can be observed. However, there is a few operational views of the issue, and some key requirements were neglected due to the simplification of provided models. In this study, an integrated model of the two main stages of airline planning, including fleet allocation and aircraft maintenance routing, is considered simultaneously, and the performance of the developed model is investigated using real data from one of the airlines. Also, a sensitivity analysis of the model to some relevant parameters confirms the validity of the developed mathematical model and the solution algorithm. Then, a comparative study was investigated to compare the performance of the developed model with the operational method, including solving sub-problems stepwise. Also, the results are compared with the developed and similar method from the previous studies. The results confirm the superiority of the developed mathematical model.

In image processing, compression plays an important role in monitoring, controlling, and securing the process. The spatial resolution is one of the most effective factors in improving the quality of an image; but, it increases the amount of storage memory required. Based on meta-heuristic algorithms, this article presents a compression model for face images with block division and variable bit allocation. Wavelet transform is used to reduce the dimensions of high spatial resolution face images. In order to identify important and similar areas of identical macroblocks, genetic algorithms and gray wolves are used. A bit rate allocation is calculated for each block to achieve the best recognition accuracy, average PSNR, and SSIM. The CIE and FEI databases have been used as case studies. The proposed method has been tested and compared with the accuracy of image recognition under uncompressed conditions and using the common SPIHT and JPEG coding methods. Recognition accuracy increased from 0.18% for 16×16 blocks to 1.97% for 32×32 blocks. Additionally, the gray wolf algorithm is much faster than the genetic algorithm in reaching the optimal answer. Depending on the application type of the problem, the genetic algorithm or the gray wolf may be preferred to achieve the maximum average PSNR or SSIM. At the bit rate of 0.9, the maximum average PSNR for the gray wolf algorithm is 34.92 and the maximum average SSIM for the genetic algorithm is 0.936. Simulation results indicate that the mentioned algorithms increase PSNR and SSIM by stabilizing or increasing recognition accuracy.

The clean water crisis in Indonesia is increasing every year, and waste from the textile industry sector can also add to this problem. There are various water treatment processes to deal with the clean water crisis, one of which is the desalination process using graphene oxide. With the addition of hydrophilic nanoparticles, graphene oxide (GO) membranes can increase roughness and have good mechanical strength. SiO2 nanoparticles also have a high specific surface to absorb water or are hydrophilic. This study aims to determine the ability of the GO-SiO2/Psf membrane to reject salt (NaCl solution) and filtering of methylene blue solutions. Membrane prepared by variations of Tetraethyl orthosilicate (TEOS) 0.6; 0.8; 1.0; and 1.2 ml for GO/SiO2 composite synthesis. The results showed that the GO-SiO2/Psf membrane could absorb methylene blue solution. The most optimum absorption value occurred at a TEOS concentration of 0.8 ml and had the most effective salt rejection value for NaCl solution equal to 67.22%.

Nowadays, the notion of plug-in electric vehicle (PEV) as a valuable tool of energy management has been extensively employed in smart distribution grids. The main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. Moreover, the specific capability of renewable energy sources (RESs) in terms of contribution in PEV smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. However, the load demand, RES generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. Hence, a non-deterministic optimization model based on information gap decision theory (IGDT) is proposed in this paper to specify a robust PEV smart charging pattern. To solve the multi-objective proposed IGDT-based PEV smart charging (IGDT-PSC) model, the multi-objective version of particle swarm optimization (MOPSO) is utilized to define a set of Pareto optimal solutions. Furthermore, the final solution among the Pareto solutions is selected by means of a linear fuzzy satisfaction rule. The simulation results for a test smart microgrid comprising a PEV, a set of RES units and a load demand verify the effectiveness of the proposed IGDT-PSC model.

In this research, ASTER satellite images and the combined algorithm of band ratios with the method of logical operators with the determination of the threshold limit based on ground, laboratory and experimental studies have been used in order to highlight hydrothermal alterations. Image transformation techniques such as specialized band ratio and principal component analysis are used to map lithologic units and alteration minerals. Supervised classification technique, i.e. Spectral Angle Mapper (SAM) is applied to detect subtle differences between index alteration minerals associated with actual copper localities in the region. The results show that the integration of image transformation techniques and supervised classification of ASTER data with field studies and geochemical exploration is highly effective in targeting new copper mineralization prospects. Copper mineralization is found in siliceous veins that strike from north-south to northeast-southwest across the region. Remote sensing evidence supports the presence of propylitic and argillic alteration, which can be useful for searching for copper-gold type deposits. Kaolinite and pyrophyllite play a role in identifying arzilic alteration zones, and muscoite, epidote and chlorite minerals are very important in identifying pyropilitic and phyllic areas and in mineral exploration. Based on remote sensing processing, according to the detection of various alterations (phyllic, pyrophyllite and argillic) in this area, possible copper mineralization was detected in the central part of the studied area. The approach used in this research provides a quick and cost-effective means of initiating comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar areas.

One of the best solutions to overcome problems caused by fossil fuel consumption is using renewable energy. In this research, effect of flow rate and load on the performance of an Archimedes turbine was experimentally studied. At first, Archimedes turbine with optimal size was made using 3D printer technology. Then it was placed in the river simulation setup. After the calibration and uncertainty analysis, tests were performed for three flow rates and five electrical resistances. The results showed that increasing the flow rate leads to a rise in power, torque, and angular velocity of the turbine, but it leads to a non-linear behavior in efficiency. On the other hand, an increase in electrical load has also led to a decrease in converter performance for all conditions. In addition, by implementing π-Buckingham theory, the converter's hydrodynamic behavior was studied by using Reynolds numbers, dimensionless flow, and power coefficiency. The results showed that an increase in Reynolds number leads to a decrease in power coefficiency. However, an increase in dimensionless flow increases the power coefficiency first and then decreases (nonlinear behavior). In addition, the Archimedes screw turbine was also studied from an economic point of view, and the results showed that increasing discount rate leads to an increase in discounted payback period, and in the worst case, the payback period is 3.09 years, and in the best case, it is 1.6 years. Also, the construction of the Archimedean screw turbine in Iran can save currency for $1439.5.

Seismic excitation can cause significant energy to be released within structures. By using special devices, this energy can be consumed and dissipated without deforming structural members significantly. Due to this, structural damage is minimized, casualties are prevented during earthquakes, and structures are extended in their useful life. Over the past five decades, it has been widely acknowledged that steel yielding dampers are among the best energy dissipation devices. It has been stated that the hysteretic behavior of steel yielding dampers could vary slightly depending on their geometry. From a practical point of view, they are suitable for the improvement of seismic safety in new and existing structures. The purpose of this paper is to present a review related to steel yielding dampers, their development, various types, and applications, in order to help understand the role of these dampers in improving the seismic performance of structures. In terms of their shape, steel yielding dampers can be categorized as steel plate dampers, pipe dampers, curved dampers, and slit dampers. The most common use of steel plate, such as ADAS and TADAS, and pipe dampers is within braced frames, whereas U-shaped, J-shaped, and S-shaped dampers are mostly seen in frames with chevron bracing. Steel curved dampers with a 60° angle in a steel-braced frame, on the other hand, provide the best energy dissipation and frame strength. In this direction, until today, steel slit dampers have been found to be the most commonly used steel yielding dampers.

One of the current approaches for concrete retrofitting is called fiber reinforced polymer (FRP) wrapping. In this study, concrete retrofitting means compressive strength and seismic parameters improvement (such as failure strain, energy absorption, and ductility). Cost analysis may raise issues of concern regarding the economic value of this kind of retrofitting and for this reason, economic analysis was conducted based on experimental works. In this regard, 21 samples were prepared for three compressive strengths of concrete (20, 35, and 50MPa) and warped with different layers of carbon and glass fiber reinforced polymers (0, 1, 3, and 5 layers). Samples were subjected to stress-strain tests and concrete properties were estimated. The results showed that carbon and glass fibers, respectively, are more effective in improving the compressive strength and seismic parameters of concrete. But, the economic analysis indicated that glass fiber is more cost-benefit than carbon fiber in improving the concrete properties, especially for one layer of FRP. The economic analysis was not able to specify the application of FRP for which concrete samples are more economical, and for this reason, statistical analysis was used to respond to this vague and achieve a comprehensive assessment. The analysis indicated that the use of FRP is more cost-benefit for lower concrete strength.

Shape-memory polymers (SMPs) could remember their original shape and then, return to their initial shape upon stimulus. So far, quantities such as fixity and recovery ratio of SMPs have been broadly reported. Nevertheless, one main issue is the existence and use of an appropriate approach to quantitatively estimate the SMPs released energy. In addition, it is hypothesized that the elastic behavior of SMPs plays an underlying role when SMP fibers need to exhibit high-tension and high-elongation capacity as required in synthetic muscles. Here, we present, for first time, SMP trinary bulk and filament systems of acrylonitrile butadiene styrene (ABS)/thermoplastic polyurethane (TPU)/ethylene vinyl acetate (EVA) fabricated under calendering intense shear mixing and hot pressing. A digital blocked force load cell was used to record specimens energy released. The results exhibited high retraction from the specimens secondary shape correlated to the tensile behavior. It was shown the blended system of 50, 25 and 25% of TPU, ABS and EVA, respectively, resulted in ~640% and 3900% increase in the elastic modulus and energy release compared to EVA/TPU systems. The double SMP filaments led to a 50% increase in the energy release compared to single fibers. Nevertheless, the blended binary specimens with the TPU/ABS ratio of 50/50 exhibited 600% increase in tensile strength. It was confirmed the elastic modulus, number of fibers and elongation at break govern the SMP stored. The findings of the research lightened a new class of SMPs to be used as fiber-based artificial muscles and orthodontic products.

This study presents an efficient path planning method for mobile robots in a dynamic environment. The method is based on the rapidly-exploring random tree (RRT) algorithm. The two primary processes in mobile robot path planning in a dynamic environment are initial path planning and path re-planning. In order to generate a feasible initial path with fast convergence speed, we used a hybridization of rapidly-exploring random tree star and ant colony systems (RRT-ACS). When an obstacle obstructs the initial path, the path re-planner must be executed. In addition to the RRT-ACS algorithm, we proposed using a rule-template set based on the mobile robot in dynamic environment scenes during the path re-planner process. This novel algorithm is called RRT-ACS with Rule-Template Sets (RRT-ACS+RT). We conducted many benchmark simulations to validate the proposed method in a real dynamic environment. The performance of the proposed method is compared to the state-of-the-art path planning algorithms: RRT*FND and MOD-RRT*. Numerous experimental results demonstrate that the proposed method outperforms other comparison algorithms. The results show that the proposed method is suitable for the use on robots that need to navigate in a dynamic environment, such as self-driving cars.

The evolution and improvements of deep learning are being used to tackle any research obstacles that could be converted into classification problems in all spheres of life. Each Deep convolutional neural network (DCNN) design's output is determined by the depth and value of the hyperparameters, which explains why so many of them have been proposed. These DCNN architectures must be created entirely from scratch, and they can only be used for the applications for which they were intended. Transfer learning may be used to modify these pre-trained networks so they are more appropriate for particular purposes. This article aims to evaluate the empirical performance of the applicability of pre-trained DCNN models to identify human face presentation threats (FPAD). Human FPAD is one of the most significant and crucial areas of research right now because of the introduction of ambient computing, which necessitates contact-free identification of persons with the help of their biometric traits. Six pre-trained DCNN models are taken into account for an experimental evaluation in human FPAD alias VGG19, VGG16, DensNet121, MobileNet, Xception, and InceptionV3. The investigation makes use of the NUAA and Replay-Attack benchmark FPAD datasets. Thepade's sorted block truncation coding (SBTC) 10-ary features are merged with deep learning features produced from the finest performing finetuned DCNNs to enhance the FPAD capabilities of analyzed machine learning (ML) classifiers. The integration of features of Thepade's SBTC 10-ary and DCNN has considerably increased the FPAD accuracy of ML classifiers with slightly more computations of feature extraction.

In Indonesia, the construction of the road has challenges because the road was built on soft clay soil. The vacuum preloading method was used to improve the shear strength and compressibility properties of soft soil in this project. Moreover, what needs to be a concern for practitioners is the issue of increasing simulation accuracy in predicting soil settlement in a vacuum preloading system. The research objective of this study was to determine changes in soil settlement behavior that occurred from the vacuum preloading system using a numerical simulations Geostudio with the 2D Multi Drain-Plane Strain approach and the settlement result of the simulation will be compare with instrumentation data. In this study the vacuum pressure distribution is modeled using water total head negative pore water pressure and the pressure value used following vacuum gauge data in the field with distribution approach is 100% at the surface of the sand platform, 85% to a depth of 5 m, then 60% to the end of the PVD. Based on the simulations, the conlusion is the vacuum pressure applied along the vertical drainage is not modeled constant, but changes with depth, the value of 60% at the bottom of the vertical drainage is quite representative of the conditions in the field and the settlement from the simulation is quite good at approaching the field observation with a prediction of the settlement due to vacuum preloading of ±0.93 m, when compared to the field observation data there is a difference of about 1.6%.