فصلنامه مدل سازی در مهندسی
پیاپی 67 (زمستان 1400)

In this paper, a novel Photovoltaics (PV) system interfaced by a hybrid Active Power Filter (APF) has been proposed with the ability to simultaneously control active and reactive power in order to enhance the power quality. Based on the idea of separation of the high-frequency orders from the lower ones the proposed hybrid filter including Current Source Converter (CSC) and thyristor controlled LC filter (TCLC) is able to reduce the drawbacks of both filters when works alone. The low-frequency orders have been applied to the active part and the higher ones have been considered for TCLC. The proposed scheme not only can reduce the harmonic components of the distribution network and increase the grid power factor but also is able to inject the controlled active power via a PV system connected to CSC without any perturbation during transient intervals. The performance of the proposed structure is evaluated in different operating points by a simulation study performed by PSCAD and MATLAB software.

This paper addresses the problems of multi-hop clustering, Cluster Head (CH) selection, and routing in vehicular ad-hoc networks. We propose an efficient algorithm called Joint Multi-hop Clustering and Routing (JMCR) to improve the performance of the network. JMCR uses only local information to cluster vehicles in such a way that not only reduces the total number of clusters, but also maximizes the stability of clusters. It considers both relative speed and Euclidean distance factors to select an appropriate CH for each cluster. In order to update the routing database of each node, an array of doubly linked list is used in which the creation of different routing paths starts from the boundary nodes and continues to CH. Compatibility with reactive clustering and the ability to dynamically follow the network conditions are another advantages of the proposed intra-cluster routing method. Simulation results conducted in NS2 confirm the efficiency of the proposed method in terms of end-to-end delay, overhead, and packet delivery rate.

Non-orthogonal multiple access (NOMA) as one of the key techniques is nominated in new generation of mobile networks that can improve spectral efficiency by using the successive interference cancellation (SIC) method. By this method, users can use a shared channel. Optimal use of spectral and power resources as well as efficient users clustering are important aspects of green NOMA communication networks. In this paper, we assume different users based on their channel gains are dispersed in three near, middle, and far areas to the base station in a cell. To avoid interference and imperfect SIC at the use of NOMA benefits simultaneously, we consider a scenario including grouping of three users. Saving spectral and power resources is the feature of the green NOMA scenario. In the following, with the aim of optimal resource allocation in the NOMA cellular network, we propose the optimization problem of power allocation coefficients to users under increasing the overall system throughput. Power consumption can be optimized by properly allocating power to the base station and users, and with a certain power, can provide a desirable efficiency in the cellular network. To find the optimal power allocation coefficients to users, we prove that the optimization problem is concave/convex, then we apply Lagrangian and KKT conditions. According to the achieved results, the proposed scheme provides a 6.97% improvement in low SNRs and a 1% improvement in high SNRs compared to NOMA with constant power coefficients in terms of the sum capacity.

In this paper, three different regimes including annular, stratified and homogeneous in the range of 5%-90% void fraction, were simulated by Mont Carlo N-Particle (MCNP) Codes. In simulated structure, a cesium 137 source and two Nal detectors were used to record received photons. In this study, the Fast Fourier Transform (FFT) was applied to the registered signals from two detectors in order to analyze in the frequency domain. Several features of signals in the frequency domain were extracted using average value of fast Fourier transform, the amplitude of dominant frequency, kurtosis, Standard Deviation (STD), RMS (Root Mean Square) and Variance. The same features were extracted from analyzed signals of both detectors in order to find the best separation patterns. These extracted features were used as inputs of artificial neural networks (ANNs) to increase the efficiency of two-phase flowmeters. Two multi-layer perceptrons (MLP) neural networks were implemented in MATLAB software in order to classify flow regimes (annular, stratified and homogeneous) and predict the void fraction. All of the training and testing data were obtained correctly and the mean relative error percentage of the predicted void fraction was 1/15 %.

In this paper, the thermodynamic analysis of a 30 kW gas microturbine combined with a solar parabolic concentrator has been performed under three different radiation conditions in Qom province. In the above system, a heat recovery, recuperator, is also used to increase the efficiency of the cycle. The programming is written in EES software. The effect of influential parameters such as ambient temperature, compressor pressure ratio, inlet gas temperature to the turbine, solar radiation intensity, receiver area and the presence of recuperator on the system performance have been investigated. The results show that with increasing the compressor pressure ratio, the electrical and mechanical efficiency of the system first increases and then decreases so that the overall efficiency of the system has its maximum value in the compressor pressure ratio in the range of 3 to 4. Also, the gas-solar turbine in August reduces the specific fuel consumption by about 58% compared to simple gas microturbines and at the same time increases the electrical and mechanical efficiency of the system by about 20%. In addition, by increasing the area of the receiver per 20 m2, the specific fuel consumption is reduced by about 38%.

In this article the effect of non-uniform air gap on a 4/2 switched reluctance motor is studied. In order to do this, two rotors are designed, optimized and fabricated. The first rotor has uniform air gap in aligned position and the second rotor in half of pole is the same as the first but in another half of pole, air gap can be non-uniform. The purpose of optimization is achieving a desired torque profile which reduces torque ripple in high speeds and at the same time increases the average torque. Finite element analysis is used for calculating torque profile and genetic algorithm is used for optimization, both by MATLAB. Optimization variables are rotor pole arc and width of rotor yoke. For the second rotor, another variable which determines the amount of uniformity of air gap is added. The analysis results show that the motor with non-uniform air gap is more coincident with the desired torque profile. Experimental results of prototype motor confirm the analysis results.

Multiple variables and subsystems increase the complexity of safety-based maintenance modeling in large systems, including power plants. This paper is modeling the power plant maintenance system by considering safety indicators. Then it simulates and analyzes the behavior of the designed model for a numerical sample. Regarding the literature review, a causal loop diagram of the maintenance system for power plant was designed and a safety subsystem was added to the model, based on the experts’ opinion, and finally the stock and flow diagram was formulated using AnyLogic software. The model was simulated in three scenarios including scenario one, training, scenario two, adding new equipment in the seventh year and scenario three, a combination of adding new equipment and training. The first simulation confirmed that increasing the training rate reduced the breakdown rate and equipment failure rate up to the sixth year, as well as reducing the accident rate and cost and increasing safety until the fifth year. Current costs increased. The second indicated that the addition of eight new equipment in the seventh year will improve the model up to 15 years later, and the amount of profit from the second year to the fifteenth year is more than scenario one and three. The combination of scenarios one and two, the optimal scenario, causes a greater amount of safety, a lower failure rate from the sixth year onward, a lower accident rate and cost, and a lower current cost from the fifth year onward.

Induction motors are among the most widely used industrial electric motors. Their most common structure is the cylindrical structure, which has major disadvantages such as low power density and a significant amount of end windings in the stator. However, several Axial-flux structures have already been proposed for these motors that have higher power density than cylindrical structures, but their construction process (especially in slotted structures) is rather complicated and costly. Moreover, the radial electric and magnetic loadings in those machines are non-uniform, which increases their harmonic losses. In order to solve such problems, in this study, an Axial-flux induction motor with a new structure is introduced. In this structure, the stator core, rather than the yoke, consists of a series of separate teeth, each coiled separately. The end windings are thus removed and all parts of the windings will equally contribute to the induced voltage and electromagnetic torque. As well, the stator volume is significantly reduced and the machine's power density increases. Furthermore, the electric and magnetic loadings in this structure are radially uniform. Finally, the validity of the above claims and the performance of the proposed induction motor have been demonstrated through fabricating and testing a prototype of it.

In this study, the aerodynamic performance of a high-speed train against a turbulent air flow is examined numerically from two approaches. First, using computational fluid dynamics, the parameters of aerodynamics and fluid flow are analyzed and then, using Multi-Layer Feed-Forward Neural Network (MLFFNN) Algorithm, a prediction and comparison with the obtained values from the CFD analysis are presented. To achieve this, using Reynolds-Averaged Navier-Stokes (RANS) method with 𝑘-𝜔 (SST) turbulence model, an incompressible turbulent air flow around a high-speed train model by OpenFOAM CFD Software is simulated. In this research, some of the significant and key parameters of fluid flow and aerodynamics as velocity, pressure, streamlines, flow structure, pressure coefficients, drag, lift and side forces for some yaw angles of wind movement and velocity changes are analyzed and compared. In the following, the Multi-Layer Feed-Forward Neural Network which is modified with various data is applied for prediction of the output of the problem. Accordingly, the aerodynamic drag, lift and side forces for the yaw angles of wind movement and velocity changes by this algorithm method are obtained and compared with the obtained results from CFD analysis. The comparisons indicate an appropriate similarity between the CFD data and the used MLFFNN one.

Many companies and manufacturing industries produce their final product after going through several stages. Normally, in addition to the desirable products, these companies also have undesirable products that are considered as non-retrieval final products. However, by adding a stage for correction and recycling, their undesirable products can be used as resources, which improves economies of scope and cost-effectiveness. The purpose of this paper is to provide models for evaluating economies of scope and cost-effectiveness in two-stage supply chain systems which undesirable products will be given to the second stage for processing and modification and will be returned to the first stage after re-modification. By examining the characteristics of the proposed models, the effect of undesirable intermediate products between stages on cost-effectiveness and economic efficiency has been measured and evaluated. The results show that the proposed models are more accurate than the conventional model and the ability to effectively distinguish between cost and economic efficiency has been significantly improved. It is also possible to study the economic efficiency in the simultaneous production of products in the proposed models.

Wind turbines are exposed to a variety of faults some of which can cause irreparable economic losses. Therefore, identifying the faults in a short time, ensures the correct operation of the system and prevents the mentioned losses. In this paper, using a dynamic model for wind turbines which includes mechanical and electrical parts with appropriate details, an intelligent fault detection and isolation system is designed utilizing recurrent neural networks. The proposed system can identify the occurred faults in pitch sensors and pitch actuators. Then, in order to consider the robustness of the system, it is suggested to use an adaptive fuzzy threshold in decision making block. Simulation results for the fixed threshold, robust thresholds, and the proposed adaptive fuzzy threshold validate that the suggested adaptive threshold reduces the detection time. In addition, the number of false alarms, and the number of missed ones are reduced by using the intelligent fault detection system.

Foroozan platform is one of the most important Iranian offshore facilities lies along the border of Iran and Saudi Arabia. As time passes by, the possibility of presence of some damage in the structural members of this platform increases. As a consequence of these damages, the operation of the platform may be disrupted and if the damage grows, especially in sensitive areas such as deck, joints or splash zone that are prone to failure, cause more damage in the future. This highlights the necessity of structural health monitoring of this platform. One of the most common structural health monitoring techniques is the modal strain energy-based damage index which is known as Stubbs index. In recent years, modifications have been made to original version of this index, one of which is to consider the natural frequencies in determining the location of damage. In this paper, using the improved modal strain energy method and considering the natural frequencies in determining the location of damage, the location and severity of damage in the Foroozan platform have been identified. One of the differences between this study and similar studies is the large number of elements of this real platform. The results showed that the improved method has a higher accuracy in locating the damage than the original method (Stubbs index). Also, single and multiple damages, with low and high intensity, were predicted with appropriate accuracy by this method.

Electrophysiological cell models are used to study and simulate the electrical behavior of cells. These models are presented by considering the characteristics of cells, channels, and ion currents in the cell membrane. This study aimed to provide a minimal model for gastric smooth muscle cells. Using the sensitivity analysis method in this paper, gastric cell current from the colon cell was obtained based on the approach of Yeoh et al. Then, the minimal model was obtained by identifying effective ion currents and eliminating inefficient ion currents. To evaluate the minimal model, the criteria of slow wave index points (initial potential, maximum spike potential, minimum valley potential, maximum plateau potential, and resting potential) and action potential duration of 10, 50, and 90 were used. Then, These values were compared with the slow wave physiological state of gastric smooth muscle cells. Finally, the minimal model obtained was consistent with the physiological model. The largest difference between the index points in both cases was related to the maximum spike potential with 2.21 millivolt and the action potential duration of 10 with 9 milliseconds. The results obtained by comparing two models of gastric smooth muscle cells (physiological and minimal states) showed the same behavior in slow wave.

Cascading failures are one of the most destructive states in the power systems, which may initially start with a small error and then spread with a chain effect like a domino, leading to large-scale blackouts. Therefore, it is necessary to prevent cascading failures, because our daily lives depend heavily on a stable and reliable power supply network. So far different methods have been proposed to identify the initial events of cascade failures, including fault-prone lines and transformers, with the aim of taking remedial action scheme to reduce the adverse effects of cascade failure. In this paper, several well-known methods with the potential to detect initial events that lead to catastrophic cascading failures are first implemented of the IEEE 39-bus test system and the Competencies and shortcomings of methods are compared. In the following, a new method is presented with appropriate speed and accuracy compared to other methods. The enumeration method based on William Fine risk assessment technique is used to validate the results of different methods. The enumeration method is a difficult and time-consuming method. Therefore, in order to reduce the dimensions of the mentioned method in order to validate the methods more appropriately and faster, the William Fine risk assessment technique is used. This technique identifies and ranks the potential and effective initial events in creating the chain of cascading failures in the power system by calculating the risk assessment score of combinations of possible N-K(1<K≤3) contingencies.

Rapid developments in radio technology have enabled the emergence of small sensor nodes capable of communicating in wireless sensor networks. Nodes in wireless sensor networks work together to transmit information using multipath routing. This partnership has made these types of networks vulnerable to many attacks. In order to determine the reliability of nodes in separating malicious nodes from other nodes, an intelligent trust management scheme must be used. In recent years, trust-based and energy-aware routing protocols have become important tools to increase wireless sensor networks security and performance. In this paper, a trust-based and energy-aware routing algorithm based on a new hybrid fitness function is proposed. This algorithm has two main aspects: one is the selection of secure nodes based on the tolerant constant and the other is to select the most suitable nodes from among the secure nodes to perform routing. The proposed algorithm uses a multipath routing technique with an intra-cluster and inter-cluster multi-hop communication mechanism. In addition, the optimal and secure route is selected based on a combined fitness function with parameters of Energy, Reliability, Quality of Service, Connectivity, Distance, Hop-Count and Network Traffic. The simulation is based on evaluation criteria such as throughput and detection rate in the presence of Denial-of-Service attack. The experimental results show that the evaluation criteria in the proposed algorithm have improved compared to other secure routing algorithms.

Since reducing or increasing proteins could be the signs of diseases in the body, they could be used as biomarkers for diagnosing some diseases. This study investigated small protein strings by computing the magnetic resonance spectrum of the nucleus. Amino acids are the building blocks of protein in the body, and the body does not produce the essential amino acids. They must reach the body through the food chain. Because essential amino acids are required for vital processes such as protein production and hormone synthesis, they are of great importance and are studied in this study. First, strings of two to six amino chains with the same monomers were modeled using Gauss View software, and their magnetic resonance spectrum was calculated using Gaussian09w software. These structures are modeled to investigate factors such as the type and length of the amino acid chain on the amplitude and location of the maximum peak in the magnetic resonance spectrum of the nucleus. The results show each amino acid has a unique own magnetic resonance spectrum and these amino acids in a polypeptide chain affect magnetic resonance spectrum. The ability of the nuclear magnetic resonance method to diagnose and identify a variety of diseases, its non-invasive nature, as well as the possibility of repeatability of experiments, in addition to reducing the cost of experiments, make this method as one of the novel and advanced diagnosis.

In the present study, the performance of the pervaporation membrane reactor during the esterification reaction of levulinic acid with ethanol to produce ethyl levulinate was modeled based on computational fluid dynamics (CFD) method. Global warming due to the greenhouse effect is now recognized as an important environmental issue. Important factors in the greenhouse effect are the production of sulfur oxides, nitrogen oxides and carbon dioxide, which are produced after the combustion of fuel. Therefore, one of the solutions to solve this problem is a biomass-based additive that leads to better performance of the fuel during combustion and prevents the production of these gases in the atmosphere. Therefore, a symmetric two-dimensional model is provided for the pervaporation membrane reactor (PVMR). In this regard, after modeling and simulating the performance of a fixed bed reactor and comparing its results with laboratory data, it was observed that a good agreement (1% error) was obtained between the theoretical and laboratory results. In order to better understanding of the efficiency of the pervaporation membrane reactor during the esterification reaction, the effect of different operating parameters (reaction temperature, feed flow rate, feed molar ratio and catalyst loading) on the levulinic acid conversion and water removal percentages have been investigated. As a general result in all operating conditions, the pervaporation membrane reactor has performed better than a conventional fixed bed (TR) reactor.

In this study, modeling and fault detection of a novel faulty quadrotor is presented. It is assumed that a quadrotor vehicle has encountered a fault during a flight accident, and as a result, one of the rotors does not operate vertically. Although the rotor's rotational axis has deviated from the vertical direction, the amount of produced thrust remains constant. Detecting this fault along with utilizing a proper controlling approach can reduce the risk of failure in the vehicle. Based on this statement, the procedure of this study has been developed in three main stages. First, the kinematic and dynamic equations governing the faulty system are driven using Newton's second law and Euler's principle. Then, equations governing the faulty system and the Thau observer are employed to calculate the residual value. This parameter is calculated based on the differences between states’ measurement and estimation. Eventually, by comparing the computed residual value with the assumed threshold, thrust deviation in the shortest possible time has been detected.