Modeling in Engineering
Year:2024 | Volume:22 | Issue:79|Papers Count:20

In this paper, the effect of a fixed twisted-tape placed in the inner tube of a double-pipe heat exchanger is investigated using numerical simulation. A twisted-tape with three and four blades with different twist ratios is placed in the inner tube of the heat exchanger, and the Nusselt number, friction coefficient, and overall performance coefficient of the heat exchanger are studied. The flow velocity in the outer tube is considered constant, but the Reynolds number of the flow at the inlet of the inner tube is changed in the range of 5000 to 12000. The results showed that in all cases, placing a twisted-tape inside increases the Nusselt number and friction coefficient. The highest increase of these two parameters is 244 and 490 percent, respectively. By increasing the Reynolds number, the friction coefficient and the overall performance coefficient decreased, while the Nusselt number increased. This increase reaches about 80% in the case that has a twisted-tape with a twist ratio of 4. Also, reducing the twist ratio increases the turbulence of the flow and thus increases the Nusselt number, pressure drop, friction coefficient, and the overall performance coefficient. The maximum increase in the friction coefficient due to the reduction of the twist ratio from 8 to 4 was equal to 43%, occurred in the Reynolds number of 5000 and the three-bladed twisted-tape. Increasing the number of blades of the twisted tape has a direct effect on the pressure drop and friction coefficient and causes a decrease in the overall performance coefficient.

In recent years, the Internet has become part of the requirements of human life. With the widespread use of the Internet, the Web, and online social networks, the number of vulnerabilities and security threats has increased significantly. Various types of malwares (worms and viruses) have become a major threat to the security of systems and networks. In this regard, researchers are looking for ways to identify malware and fight against them. One of the methods used in this field is to model the malware propagation in order to identify and combat malware by modeling its behavior. In this article, a malware propagation model based on the propagation of epidemic diseases in a heterogeneous network structure, considering the devices connected to the network and the Internet, is introduced. Modeling is done based on the Susceptible-Exposed-Infected-Recovered epidemic model for devices and Internet networks. The results show that the speed of malware propagation in the proposed HD-SEIRS model is significantly reduced compared to the SEIR model. Also, in this article, the basic reproduction ratio (R_0 )  is calculated for the proposed model and the effect of parameter changes on the proposed model is investigated.

One of the most important and influential ways to diagnose breast cancer, especially in the early stages of the disease, is mammography. Mammography images are usually of low quality due to the complexity of breast tissues, the similarity between cancerous masses and normal tissues, the different sizes and shapes of the masses, and X-ray radiation. Therefore, it is very difficult to detect lesions, especially in the early stages; Because some mass lesions are embedded in natural tissues and have weak margins or vague margins. The proposed method in this study is to present an architecture based on a deep convolutional neural network to detect cancerous masses in mammography images, which ultimately leads to classifying the masses into normal and abnormal classes. The training of the proposed network begins with the modification of the images in the pre-processing stage in order to perform more accurate drawings with high resolution on the images and finally to improve the accuracy and sensitivity of separating the mass from the breast tissue for correct diagnosis. Python programming language and TensorFlow library have been used in the Windows environment to implement the proposed method. To ensure the performance of the proposed method, the cross-validation method was used and the obtained results were evaluated by the criteria of precision, accuracy, and sensitivity. The results obtained with an accuracy of 97.67% indicate the improvement of the diagnosis accuracy and the cost reduction in the diagnosis process

Detecting and locating unwanted structures or anomalies in the image is one of the important issues in machine vision and industrial inspection. The complexity and variability of data distribution and the lack of labeled data are among the challenges of detecting anomalies in images. In recent years, deep learning methods have provided promising results for solving anomaly detection problems in any data types, especially in images. In this paper, the DFDA-AD architecture, which is an unsupervised approach based on deep learning, is proposed for anomaly detection in industrial images. DFDA-AD consists of dual feature extraction from images by pre-trained DenseNet121 and ResNet50 networks. Two attention mechanisms are improved and developed in this paper, which provide more important feature maps for clustering by K-means algorithm. The evaluation of the model's performance was done on the MVTec AD data set, and the results of the evaluations for anomaly detection and localization were satisfactory compared to several other approaches that have been recently proposed.

In this research, the sour water unit and its simulation were investigated. Sour water unit of refineries usually has two stripping towers in series. The results of this study showed that with the increase in the volumetric flow rate of the incoming sour water feed, the molar flow of NH3 in the exit gases from the first tower increases. With increasing feed temperature, the NH3 and H2S do not change much until the temperature of 1050C, and after that, the relative molarity of these two substances in the exhaust gases decreases with increasing temperature. By increasing the reflux to the first tower to about 9 m3/hr, the molar flow rate of NH3 gas in the first tower decreases drastically, which actually increases the purity of H2S in the exit gases of this tower and increases the flow of this component in the exit gases from the second tower. Also, with the increase in the temperature of the end of the tower, the flow of NH3 and H2S increases and at a temperature of about 120 0C, all the H2S in the feed enters the exit gases from the first tower and the exit water from the first tower is free of H2S. As a result, it can be concluded that the best temperature for the bottom of the first tower is about 120 0C so that all the H2S is removed from the water and NH3 does not enter the gases leaving the tower in the first tower.

Weeding in potato fields with the least consumption of inputs (herbicide or mechanical operations) is one of the main goals of sustainable agriculture. In precision agriculture, machine vision-based systems are used as on-the-go sensing units to detect weeds from the main plant. This paper presents a new approach based on deep learning to detect weeds in potato fields. For this purpose, first, a comprehensive database was created, including the acquired images of the potato field (at different stages of plant growth, at different distances of the camera from the ground, at different hours of the day, and in different environmental conditions). Then, the location of the plants in the field (including weeds and potato plants) was determined using the deep YOLOV3 algorithm. Finally, to separate weeds from the main plant as well as to determine the type of weed, three different types of convolutional neural networks were developed. The results showed that the YOLOV3 algorithm is well able to localize the plants in the images. EN-Inception-V3 classifier was able to distinguish weeds from potato plants in the set of test images with 99.42% accuracy. The classification results of 9 different weed species using the developed deep learning models were satisfactory; so that the overall accuracy of EN-Inception-V3, EN-VGG-16, and HCNN models was 99.82%, 99.89%, and 92.83% in the training phase, and 96.69%, 90.32%, and 82.67% in the test phase, respectively. It should be noted that the combination of models leads to 98.2% accuracy in detecting the type of weeds.

In this article, the time-sensitive networking protocol on the MIL-STD-1553 data bus is implemented and evaluated to reduce the average packet reception delay. The most important feature of this bus is its high reliability. One of the parameters addressed in this bus is the certainty of data arrival along with the defined delay, meaning the bus is designed in a way that the probability of creating delays beyond the standard does not exist. However, despite all the good properties of this bus, its low speed is not suitable for modern systems today. In this article, the delay issue and its guarantee based on time-sensitive networking are examined. The proposed method prioritizes packets exchanged in the 1553 data bus. OMNET++ software is used for implementation, and simulation results show that delays have been less compared to the 1553 bus, and while the size of these delays remains constant, their amount is almost constant under conditions where there is an increase in data volume and its transfer rate. The proposed time-sensitive protocol reduces the average maximum network delay by 86 percent.

Considering the wide application of germanium in nano devices and its high density compared to silicon and the possibility of its dislocation and diffusion in grain boundaries, improving its properties is of great importance. In this study, with the help of large-scale molecular dynamics method, improving the mechanical properties of germanium by passing spherical iron particles on it has been studied. In this simulation, the interatomic forces for germanium are defined as TERSOFF and for iron as EAM and the interactions between them are defined with the LJ potential. After balancing the system in the NPT ring and moving the iron nanoparticle at different speeds, a tensile test was performed with a strain rate of 0.01 1/ps. The results show that the van der Waals interaction between germanium and iron particles has been established. These interactions made the bonds between the atoms of germanium stronger, and the Young's modulus and ultimate stress of germanium reached 119 and 24 GPa, which were 10 and 9% better than pure germanium, respectively. Furthermore, by increasing the speed of iron nanoparticles up to the speed of 0.005 Angstroms per picosecond, the atomic attraction increases and this phenomenon causes more mechanical strength in the basic structure.

This study has investigated the performance of the car scanner cargo system in determining the linear attenuation coefficient of aluminum metal from the images obtained from a modeled car scanner imaging system. This system is based on a scintillation array detector and a radiation generator based on gamma emitter isotope. This research includes modeling the interaction of gamma radiation with the commonly used metal aluminum and analyzing the properties of its linear attenuation coefficient based on the data obtained from the scintillation detector system. For this purpose, radiographic images of aluminum sheets with different thicknesses were first extracted using modeling. Mcnpx monte Carlo code was used to model the system. The obtained images were processed using the algorithm written with MATLAB. Linear attenuation coefficient histograms were obtained from the processed images. Eight conventional functions were fitted on histogram graphs of linear attenuation coefficient. The parameters of the fitted graphs and the fitting accuracy of each were obtained and compared with each other. The average value of the calculated linear attenuation coefficient was compared with the experimental and modeling results of other researches, which showed a good agreement with each other.

In this paper, a new method is proposed for low fault resistance fault detection and classification in two interconnected microgrid with tie line connection in island situation (Islanded two interconnected AC microgrid). The proposed method classifies the types of faults based on modal and superimposed fault current analysis. In order to analyze the modal, the Clarke transform is used so that the zero-sequence current can be monitored. Also, mathematical morphology method is used to find the amount of injected fault current in signal processing context. In this article, the theory of mathematical morphology is proposed using signal closing filter to identify amplitude changes and distortion in the measured current through time domain analysis. This theory is implemented in this paper to identify and classify all types of faults in a timely manner. Additionally, comparing to another signal processing methods based on frequency domain analysis or frequency-time domain, it has the advantage of requiring a telecommunication system with lower bandwidth and cost. The proposed method for classifying single-phase to ground, two-phase to ground, and three-phase faults in two microgrids connected through a tie line has been simulated as a case study. The numerical results illustrate the efficiency and effectiveness of the proposed method in classifying the types of faults with small resistance.

Island partitioning is an attractive solution to restore service in a smart distribution system that becomes islanded unintentionally. Immediately after the disconnection of the main grid due to a fault, the island subsystem experiences intense voltage and frequency deviations. Providing methods for island partitioning without considering frequency and voltage characteristics cannot lead to reliable solutions. So, in this paper, a two-stage and multi-objective solution is presented to restore the services of the island system in emergencies, considering the frequency and voltage stability. In the first stage, according to the available time to decide on the control measures, the partitioning scheme with the aim of the highest restored load and voltage stability margin is presented with the honey badger algorithm. In the algorihm, voltage stability index, load priority and controllability, frequency response and voltage stability margin are included in load shedding and in finding the borders of the partitions, respectively. In the second stage, optimal power flow is done to set the partitions and satisfy the system operational constraints. Not only does the proposed method create partitions with the allowable bus voltage and frequency, but it also privides the lowest losses, the largest restored load, and the highest restored load compared to other methods. Moreover, the proposed method is more comprehensive due to multi-objective and considering voltage stability, frequency stability, simultaneous faults, controllable and uncontrollable distributed generations, reserve, position of switches, available time to decision making, time delay between control measures, and handling islands with positive and negative power imbalance.

The design and planning of cellular Manufacturing systems in most classical models is based on minimizing production costs or increasing producers' profits. With the expansion of production systems and the increase in demand for products, concerns about environmental issues and excessive consumption of non-renewable resources have increased. On the other hand, paying attention to workers and the safety of the work environment has been introduced as a vital issue in creating a sustainable Manufacturing system. In this article, a new multi-objective mathematical model for creating a sustainable cellular Manufacturing system is presented according to the mentioned items in order to minimize the production costs in the system in addition to optimizing the environmental effects of the Manufacturing system. In the following, the model was solved using the epsilon constraint method and various solutions were presented in the form of a Pareto front for decision making. Due to the NP-hardness of the proposed model and the inability of the GAMS software to find optimal solutions for large-scale problems, a non-dominant sorting genetic algorithm (NSGA-II) is presented to solve it. The results showed that the meta-heuristic method has reduced the solution time by at least three times compared to the epsilon constraint method; Also, reducing the level of environmental risks has led to an increase in production costs. Finally, the applicability of the proposed model in an agricultural equipment production workshop has been investigated as a case study.

Spam is one of the problems that has plagued human societies. Although a lot of research has been done in this field, because spammers keep changing their methods like viruses, so there is always a need to provide new solutions in this field. The purpose of the research is to use image texture features to detect image spam. So far, 22 features of image texture have not been used in one place to detect image spam. In this paper, a hybrid method is used to extract key features. In the proposed hybrid method, the co-occurrence matrix of the gray level and chi-square and the threshold of changes in the value of the features are used. The steps mentioned have a great impact on the performance of the categories and improve the accuracy of detection. In the classification stage, the most widely used machine learning algorithms are used to detect image spams, and after obtaining the results of each category, the output of the algorithms used on spam and valid images is examined and compared. The obtained results show that with the help of the proposed method, good detection accuracy can be achieved compared to other methods. Among the reviewed algorithms, the neural network algorithm shows the best performance. The assumed algorithm in other articles shows a lower detection accuracy than the present article, but in the proposed method, it reaches 99.29% detection accuracy.

By taking local indention effect and foam core strength into account in the overall deflection process, a novel theoretical model is investigated to predict the large deflection with local indentation of sandwich beam under quasi-static lateral loading. The theoretical and numerical models of lateral crushing force and total plastic energy are proposed and the theoretical results agree well with numerical and experimental results of previous researches. The results conclude that local indention phase plays an important role in initial collapse deflection of sandwich beam when maximum deflection is greater than face sheets thickness. In addition, the theoretical results with and without local indention phase of total plastic energy are compared and it shows that total plastic energy of sandwich beam will be overestimated if local indention phase is neglected. The present analytical model can predict the large deflection behavior with local indention of sandwich beam reasonably.

Using a dredger to remove mud and garbage accumulated in the river or sea docks is a common and important process. In order to monitor the condition of sediments on the sea bed, it is necessary to use an online measurement system for the concentration of transfer slurry from the floor to the surface. The nuclear-based systems have this important and operational capability to provide the possibility of online monitoring of the passing slurry by installing it on the pipeline without causing any disruption in the transfer process. The concentration measurement system of the cutter-suction dredger uses the combination of two nuclear and non-nuclear parts to measure the fluid parameters desired by the client, such as the volumetric and mass flow rate of the two-phase fluid and its components. In using hybrid systems for this purpose, basic conditions such as online measurement capability, non-physical, chemical or environmental interference in the passing flow, compatibility with the operational conditions of temperature, pressure, humidity and vibrations in the dredging site, measurement accuracy should be met and safety principles should be considered. In this study, using Monte Carlo simulation with MCNPX code, the effect of different geometrical and dimensional parameters of output gamma ray collimator on the accuracy of measuring the concentration of passing slurry was investigated, and with the considerations related to personnel radiation safety, the final optimal mode was proposed. The decrease in the placement depth of the horizontal spring inside the beam is associated with the simultaneous maximum of gradient and count statistics. In all of the mentioned situations, the opening of the conical beam-forming aperture has a higher sensitivity and resolution compared to a rectangular one.

The architecture of water distribution networks is inherently intricate, encompassing the strategic management of pumping mechanisms, modulation of reservoir water levels, and ensuring the delivery of water to consumers with adequate flow and pressure. A pivotal element of operating these systems is the strategic planning of pump schedules, which exerts a considerable influence on the operational expenditures. The present study introduces an optimization framework designed to schedule pump operations with the objective of minimizing energy consumption. This framework is equipped with the capability to regulate the frequency of pump activation and deactivation cycles, employing the operational durations of the pumps as binary variables and the volumetric flow rates within each conduit as continuous variables throughout the scheduling horizon. The development of this framework commenced with the formulation of the issue as a mixed nonlinear programming problem, which subsequently underwent transformation into a mixed linear programming model through the application of linearization methodologies. An empirical case study was conducted to assess the efficacy of the proposed model. The examination of the results indicates that the use of the proposed optimization method leads to a reduction in energy costs by approximately 16 percent and an increase in the efficiency of the water distribution system.

The bat algorithm is an example of meta-heuristic algorithms from the collective swarm intelligence, which is based on the echolocation behavior of bats. This algorithm preserves the diversity of the solution by using a frequency tuning method that can quickly and efficiently shift from exploration to exploitation. Therefore, when a fast and accurate solution is needed, this algorithm becomes an efficient optimizer for any application. Although the bat algorithm has many practical benefits, it also has some disadvantages. One of these disadvantages that reduces its efficiency is being trapped in the local optimum. To solve the mentioned problem in this research, the position and speed of the initial population is updated in three ways with different formulas, this makes the final answer of the problem not trapped in the local optimum and diversity occurs in the population. In this article, the performance of the improved bat algorithm on 11 sample objective functions has been investigated and compared with other similar algorithms, and finally the results show the superiority and accuracy of this algorithm compared to similar samples.

Detecting and monitoring radioactive contamination is very important. It ensures public safety and environmental protection. However, exploring out-of-control radioactive sources in crowded places is hard. This is true, for example, among passengers or cars. This study proposes a new approach. It is based on data fusion and machine vision methods. The approach detects radiological contamination among similar moving objects. At first, we use the motion algorithm to define 5 moving objects. They are of the same shape and size and in a two-dimensional plane. Their motion equations were inspired by the small wheeled robot. These objects move with the same speed in the plane. Next, with another algorithm based on the KLT method, we extracted related features and tracked the same objects from the image data. The algorithm combines the beam detection system's data and machine vision. It finds one or more infected targets. It successfully detects the infected moving object. This research shows a promising approach to improve monitoring of radiation environments. It suggests integrating surveillance camera images and radiation detection systems for public and large areas.

Aluminum-composite hybrid structures have the ability to absorb high energy under crushing axial load. In this research, by using the advantages of adhesive bonding, the local reinforcement of square aluminum energy absorber has been done by composite. In this regard, a theoretical analysis has been proposed to predict the average collapse force of hybrid structures. The validity of the theoretical model has been confirmed by experimental results. In this study, four L-shaped composites are locally attached to the rectangle from inside the aluminum structure using Araldite 2015 glue. Finite element model is developed to analyze these hybrid structures. Five different modes of fiber arrangement angle [15,-15], [45,-45], [90,90], [0,90] and [0,90] are considered for the composite. Similarly, the number of composite layers varies from 2 to 8 layers. The proposed theoretical model can quickly predict the average collapse force and dissipated energy of the structure when the geometrical parameters and mechanical properties of the composite and metals are given. The results showed that compared to aluminum and aluminum-composite energy absorbers, due to the adhesive connection between aluminum and composite in the locally reinforced aluminum-composite sample, the composite absorbed energy by following the aluminum collapse pattern and creating continuous failure and collapse modes. It increased only 155.75% for aluminum and 22.99% for aluminum-composite. Among the selected cases, the optimal number of composite layers for locally reinforced aluminum-composite energy absorber is 4 layers and the optimal angle for internal reinforcement is [0,90].

Network reliability is one of the main effective indicators for designing and managing water distribution networks in normal or critical operating conditions. Occurrence of transient flow and sometimes the water column separation phenomenon is one of the factors of reducing reliability in water distribution networks. The increase in pressure caused by the mentioned phenomenon can cause financial and life damages, which makes it necessary to discuss the reliability of the network, occurrence the transient flow and water column separation. In this article, with the help of the Monte Carlo method and the dimension reduction method, the reliability of the water supply network has been investigated, taking the conditions of the occurrence of the water column separation phenomenon. For this purpose, the Loop system of Wang et al. (2017) was simulated in ANSYS Fluent software and the results of these simulations were called as input data for the mentioned methods in Matlab. Examining the reliability results showed that the occurrence of the water column separation phenomenon reached the probability of network failure to 0.247. Also, the comparison of the probability of failure of variables effective in creating pressure shows that the probability of failure caused by the valve closing time and the distance from the upstream valve with amounts 0.209 and 0.0158 have the highest and lowest values, respectively. The result of this research shows that the dimension reduction method, along with the Monte Carlo method, is a practical solution for calculating and analyzing the reliability of the water supply network.