فصلنامه مدل سازی در مهندسی
پیاپی 69 (تابستان 1401)

The increasing capabilities of integrated circuits and the complexity of applications have led hardware design methods and tools to higher levels of abstraction. High-level synthesis is one of the key steps in increasing the level of abstraction, and the more concise the initial description in the intended application, the more efficient the high-level synthesis will be. Arithmetic applications are among the applications in which the initial input is very abstract. In recent years, extensive research has been conducted on the design of arithmetic reconfigurable architectures. Since, on the one hand, the effective use of these architectures depends on the existence of appropriate algorithms and tools to implement the design on the hardware, and on the other hand, research on the development of these algorithms has been very limited, this paper will present methods for optimizing the automated synthesis of arithmetic circuits on a coarse-grained reconfigurable architecture. These optimizations include mapping optimization, delay optimization, and area optimization. The platform chosen to execute the proposed algorithm is the DARA coarse-grained reconfigurable architecture, which is optimized for decimal arithmetic. The results show that implementing the TELCO benchmark on DARA using proposed optimizations entails about 30% gain in the area of the circuit.

Wide-area protection of electric power transmission networks against faults was the main motivation behind the invention of synchronized phasor measurements. However, global positioning system (GPS) signal loss and synchronization errors on the one hand and variability of external network parameters on the other hand have prohibited utilization of wide-area protection of transmission network. This paper introduces a novel approach for wide-area protection in order to address the above-mentioned problems. By utilizing the measurements provided by phasor measurement units (PMU) located at border substations of the internal protected network, the proposed approach will be independent of external network parameters. Moreover the proposed formulation avoids using phase angle of PMU measurements, as opposed to existing methods that are vulnerable to loss or incorrect value of even one of phase angles of voltage/current synchrophasors. Another advantage of the proposed approach is avoidance of zero-sequence network parameters as well as identifying the faulted line regardless of fault type and its resistance. Simulation studies for the 9-bus Western Systems Coordination Council (WSCC) confirm high accuracy of the proposed approach in identifying the faulted transmission line.

In this paper, the parameters of the dynamic model of a three-degree-of-freedom simulator based on air bearing, including moment of inertia, center of mass, using experimental data in a maneuver in two ways: 1- hybrid optimization method of genetic algorithm and second-order programming and 2- The method of least squares error is estimated. To do this, a position maneuver is performed using reaction wheels, and torque values as well as angular velocities around three axes are recorded. The table parameters are then estimated using the stored data and the implementation of the two methods. The results show that with the least squares method, unlike the hybrid optimization algorithm, with a attitude control test from the non-zero initial point to the origin, it is not possible to derive a good estimate and different tests are needed to stimulate all system modes. While in the hybrid optimization method, by performing the same experiment, the desired results of the system estimation can be presented. To validate the implemented algorithm, the performance of the closed loop of the table in the laboratory environment and the simulated model were evaluated and compared, which indicates the appropriate accuracy (less than 5% error) of the estimation methods.

Multi-response problems are always one of the most challenging areas of optimization. In this paper, the ROYKA hybrid method is presented to optimize multi-response problems, which is a combination of two methods of response surface and data envelopment analysis. In the ROYKA method, the efficiency surface is created and optimized, and in fact, one efficiency surface replaces several response surfaces, and the problem is changed from a multi-response mode to a single response. Therefore, in the proposed method, in addition to optimizing the response surface, the efficiency of decision-making units is also maximized. The result of this method is to determine the input and output parameters of the ideal and new decision-making unit, which also has the maximum amount of efficiency.Due to the high importance of the electricity industry and energy production, power plants, which are responsible for a very important part of electricity generation, have to increase the efficiency of their activities in order to make better use of resources. For this purpose, the proposed method of ROYKA for fourteen power plants in the country has been studied, which in addition to determining the efficiency of each power plant to determine the optimal parameters for the construction of a new power plant with maximum efficiency. Finally, in order to validate the proposed method, three other similar approaches have been used for the mentioned problem and the results show the superiority of the ROYKA method.

In this study, a method has been proposed to model doppler ultrasound signals from blood flow passing through stenosed vessels using simulation of RF signals obtained from scattering points (Red blood cells) in different times and depths. In this model, it is supposed that several scattering points are randomly distributed in the vessel. The scattering points can be located in new positions based on their velocity in any time. Therefore, doppler Effect can be observed in anytime, with changing of received signals shape obtained from moving scatterers. The velocity profile of the scattering point was determined by modeling the blood flow pattern through arteries to further elucidate the Doppler spectrum of the applied ultrasound signals. A cosine stenosis shape was considered using Tu & Devil model as it is sufficiently similar to the normal shape of stenosis in the arteries. The input flow to the stenosed zone was the same as the pulsatile blood flow in the vessel, based on the Womersley model. As a result, changing of flow intensity and Reynolds number are very similar to reality. For similarity of considered fluid to blood fluid, the intended fluid is used in the form of non-Newtonian fluid. Investigation of the estimated velocity profile compared with the applied input value led to the error rate of 6% for both normal and stenosed (70%) cases, confirming the accuracy of this model and the method for simulating doppler signal.

Ethylene is a very important material in petrochemical industries, whose chief application is producing polymers. The steam cracking of naphtha or ethane is usually applied to produce ethylene. A small amount of acetylene is produced in this process. The amount of acetylene in the product stream should not exceed 1 ppm, because it is harmful to polymerization catalysts in downstream units. The acetylene hydrogenation unit is designed for acetylene removal in industrial plants. In this unit, the removal of acetylene up to 1 ppm in the product stream and ethylene’s selectivity are of great importance. In this paper, the dynamic optimization of acetylene hydrogenation reactors of Marun petrochemical complex with considering catalyst deactivation is presented. In this study, the differential evolution (DE) method is used as a powerful method for determination of a dynamic optimal temperature profile to achieve maximum of ethylene’s selectivity in a period of 720 operating days. Then, the optimal results are compared with the condition wherein the inlet temperatures of the reactors are maintained constant at 55 ˚C and with the condition wherein the inlet temperatures of them increase linearly from 55 to 90 ˚C. The results showed when the inlet temperatures are kept 55 ˚C, the outlet acetylene exceed 1 ppm, but the best selectivity is achieved. With a linear increase in the inlet temperatures, the outlet acetylene is below 1 ppm but the selectivity is decreased. An optimal temperature profile maximizes the selectivity when the outlet acetylene is below 1 ppm.

One of the recent challenges in modeling is to determine and explain the behavior of a viscelastic material such as soft tissue. In order to correlate the soft tissue stress and strain, fractional order calculus is used as a promising approach. The tendency of soft tissue to behave as a pure viscous or pure elastic material is expressed by a parameter called order. Although stress and strain, as well as the order, are all functions of time, the purpose of this paper is to identify the order as a function of the state of the material. Therefore, it is necessary to somehow eliminate the time relationship between the order and the stress and strain pair and express the fractional order as a function of the momentary stress-strain. Here, this functionality considered as a dynamic model. To validate the proposed model, a state-space model is extracted using the results of previous studies estimated the order as an explicit function of time. The results of this novel dynamic model are then compared with the former methods. The obtained results show the efficiency of the proposed model.

This paper presents a graphene-based polarization-insensitive metamaterial perfect absorber at terahertz frequency range. The absorber consists of a graphene pattern layer at the top of the structure, a dielectric spacer layer, and an Au layer at the bottom of the structure. In the proposed structure, the ability of perfect absorption is investigated by the complete suppression of radiation and reflected light and the complete dissipation of incident energy. At the first, the proposed structure and its design process are verified and it is shown that in the three bands, the results of perfect absorption at frequencies of 2, 2.95 and 3.75 terahertz are achieved with absorption rates of 93.5%, 99.8% and 98.1%, respectively. Also, the physical mechanism of structure performance is investigated by the surface distribution of the electric field, as well as the geometric changes of the structure. In addition, the design of the proposed structure with graphene provides this advantage so that the resonant frequency can be adjusted by changing the Fermi energy level and graphene relaxation time without changing the proposed structure again. With the study, it is found that the proposed metamaterials perfect absorber is not sensitive to polarization and is more tolerant to the angle of incident radiation. Accordingly, the proposed broadband absorber in this paper has potential in imaging, detection, filtering, solar tracking and other applications.

One of the main processes in most natural language processing (NLP), is named entity recognition (NER). In this regard, some machine learning techniques have been presented that traditionally use manual features. Also, in recent years, deep neural network-based models have been proposed that achieve higher accuracy without relying on huge computations for feature engineering. Thus, in this article, we employ a combination of two deep learning models to capture the properties of the input sentence, including: long short term memory (LSTM) and convolutional neural network (CNN). In this architecture, extracting local features along with global features, more information is acquired for more accurate classification. We evaluate the performance of this architecture on two datasets CoNLL2003 and ACE05; and demonstrate that by adding a word level CNN, useful local properties are extracted that enhance the accuracy of the performance. Finally, we compare the performance of our system with competitors and our superiority is reported.

The goal of production planning in a refinery is to produce as many valuable products as possible such as gasoline, jet fuel, diesel, etc., while meeting market demand and other constraints. Crude oil refining is one of the most complex chemical industries; Therefore, optimizing the production planning of an oil refinery is considered as one of the most difficult and challenging issues in this field. Due to rapid changes in industry-related technologies such as the construction of new catalysts, the design of more flexible process units, the flexibility of refineries is increasing rapidly. With the flexibility of refineries, their production planning requires a mathematical model that can be used to make the best decision at the right time to meet market demand at the lowest production cost. In this paper, the production planning of a flexible refinery is modeled using mathematical relationships between macro parameters such as product demand, production conditions, fixed and variable production costs, and inventory costs of petroleum products. To solve it, a hybrid algorithm of combining genetic algorithm and fix and optimize is proposed. The results with using of 63 simulated problems in small, medium and large dimensions show that the near-optimal solution obtained from the hybrid method deviates 0.23 and 0.12 percent of the exact solution of the problem on average in small and medium dimensions respectively. Also in large dimensions where it was not possible to calculate the exact answer by the computer, this algorithm can answer in an average of 87 seconds.

In this work, the effect of catalytic surface reaction and different wall boundary conditions for the non-combustion flow of endocrine-methane air mixture with thermoelectric is investigated. To solve this problem, a stable and two-dimensional numerical model with the assumption of incompressible flow with thermal conductivity and constant viscosity for methane-air mixture has been used. To validate this model, the dimensionless temperature changes in terms of dimensionless dimension of the microcontroller are compared with the data that show an acceptable agreement with a maximum error of 8.45%. Increasing the transfer heat transfer coefficient from 2 to 10 watts per Kelvin reduces the mass consumption of methane, but by increasing this coefficient to 20 watts per Kelvin decreases. To validate this model, the dimensionless temperature changes in terms of dimensionless dimension of the microcontroller are compared with the data that show an acceptable agreement with a maximum error of 8.45%. Increasing the transfer heat transfer coefficient from 2 to 10 watts per Kelvin reduces the mass consumption of methane, but by increasing this coefficient to 20 watts per Kelvin decreases. In the wall mode, with a heat transfer coefficient of 10, the thermoelectric output voltage increases by 37% by doubling the velocity from 0.4 to 0.8 m / s. Due to the fact that with increasing the inlet velocity of the methane-air mixture, the efficiency in thermoelectric bases decreases, the thermoelectric power also decreases that the thermoelectric output power changes from the maximum to the minimum inlet velocity equal to 70%.

The mountainous areas that cover most of Iran have always been threatened by slope instabilities and landslides. This phenomenon causes a lot of damage to the roads and lands used by humans in these areas every year. The predisposing factors of slope instability such as slope, soil layers and land use cause a lot of damage to natural resources such as rapid soil loss, destruction of agricultural lands, forests and roads. The purpose of this study is to reach the position, role and application of numerical modeling in predicting landslides. Therefore, for this purpose and in FLAC software environment, modeling and numerical analysis were performed by reverse analysis method for seismic landslide in Latian dam watershed and considering all geotechnical criteria (laboratory and field experiments and factors affecting its occurrence). The results were reviewed and evaluated.Reliability calculations for slope stability as well as measuring the displacement of assumed points on the slope, which was performed on accurate geometric modeling and application of physical and mechanical properties of soil layers; showed that the reduction of soil resistance parameters in the region for reasons such as weathering of soil layers and rainfall have been the main causes of this landslide in which the role of water in reducing resistance (severe reduction of adhesion and internal friction angle) and increasing shear force from The pore water pressure is very prominent due to soil saturation.

Today, one of the best methods to improve heat transfer is using ferrofluid under the effect of a magnetic field. Ferrofluid is a stable suspension containing magnetic nanoparticles and a base fluid that nanoparticles has a higher heat conductivity than the base fluid. In this study, CFD simulation of the effect of a constant magnetic field on the heat transfer rate in the water-Fe3O4 ferrofluid flow in a circular tube under constant heat flux was performed. The simulation was performed in two parts, including the absence of the magnetic field and in the presence of the magnetic field by applying a constant heat flux to the pipe wall. The results showed that the presence of magnetic nanoparticles in water in the absence of magnetic field leads to increasing heat transfer between the tube wall and the fluid. In addition, the presence of nanoparticles in the base fluid increases the Nusselt number compared to pure water. Moreover, applying magnetic field on the tube wall greatly increased the rate of heat transfer to the ferrofluid due to the creation of secondary flows. Finally, the CFD simulation results were compared to the experimental results in a reference and well agreement was seen.

In this paper, the modeling of the industrial cyclohexane dehydrogenation process is done. The proposed configuration consists of a packed bed reactor including Pt/Al2O3 catalyst particles where cyclohexane will be dehydrogenated to significant products such as benzene and hydrogen. Besides, the optimization makes it possible to produce larger amounts of hydrogen as an environmentally friendly fuel, along with benzene, without production of any pollution. The governing equations including mass balance, energy balance, and pressure drop which are of the ordinary type (ODE) are achieved based on the steady-state conditions in which the feed flows are axial throughout the homogenous catalyst beds with variable physical properties. As the result, the one-dimensional homogenous mathematical model and the 4th order Runge-Kutta method is applied to solve them. Finally, the temperature change curves, conversion rates, and molar components of the components along the reactor were plotted and interpreted. After that, the reactor performance was optimized by a genetic algorithm, which resulted in a 0.88% increase in product output.. Afterwards, to approve the correctness and validity of the model, the simulation results are compared with the experimental data and acceptable agreement is achieved.