فصلنامه مدل سازی در مهندسی
پیاپی 74 (پاییز 1402)

In the grid-tied PV inverter systems, the design of a proper power conditioning system is an important issue to ensure high-quality power injection to the grid. In the low-voltage distribution network, the grid impedance variations change the resonant frequency of LCL filters. The capacitor current feedback active damping is one the most effective procedures to suppress the resonance of LCL filters. In this paper, A proportional-integral (PI) capacitor current feedback active damping method with positive virtual impedance shaping is proposed. Utilizing the proposed control strategy, the stability of the grid-tied PV inverter system against changes in grid impedance is well maintained. In addition, the system offers good performance against the PV power variations. In order to track the maximum power point, the incremental conductance (IC) procedure along with an integral regulator is utilized. Simulation of the overall system also includes solar panels, maximum power point tracking algorithm, DC-DC boost converter as well as an inverter, and LCL filter to model the grid-tied PV system with the most possible details. Simulations are carried out in MATLAB/Simulink, and it has been proved that the proposed control system maintains its stability against grid parameters variations.

In the upcoming research, the issue of consumption and energy management in residential buildings has been discussed using the energy hub system. The purpose of using energy hub is the simultaneous and optimal use of several energy carriers for the appropriate, reliable and economical supply of consumer needs while complying with the conditions of satisfaction and comfort of the residents. The optimization problem in this study is formulated as mixed integer linear programming. In order to solve the optimization problem of the optimization problem, Gam’s software has been used. During the implementation of problem solving, the effect of three conventional pricing models, including hourly, time of use and instant pricing, was investigated in Iran, and part of the results are presented in the form of tables and graphs. In all cases, the possibility of selling jointly produced electricity to the grid is included; Therefore, it is possible to pay more attention to the energy hub technology even in the climatic conditions and pricing policies of Iran, and by creating a suitable investment environment, especially in large residential complexes and building blocks, to increase security. Energy, reducing the consumption of fossil fuels and expanding the use of various energy sources in different regions helped.

Uncertainties and complexities of the actual control problems, such as unknown dynamics, unmeasurable states, external disturbances, and measurement noise, require powerful control structures capable of handling such complexities. Emotional controllers offer fast system response while also carrying a simple structure. However, the emotional controllers to date have not been evaluated rigorously. Here, the continuous radial basis emotional neural network (CRBENN) is employed to approximate the unknown dynamics in observer-based adaptive control structures for uncertain affine nonlinear systems. The system dynamics are unknown. Also, external disturbance and measurement noise affect system performance. Compared to the previous emotional controllers, the system states are not measurable and are estimated using a state estimator. The H∞ tracking performance is verified using Lyapunov stability theory, and suitable adaptive laws are designed for the weights of the proposed emotional networks that are consistent with the basic brain emotional learning model. Results indicate that the proposed controllers reach a lower tracking error with similar control energy consumption compared to another neuro-controller.

In order to diagnose breast cancer, methods such as mammography, MRI, thermal mammography and detection with a simple breast health test device (Brest Angel) are used. Different image processing methods are among the effective methods for detecting different types of tumors in women's breasts. In this article, two types of artificial neural networks are used. 5 statistical features extracted from thermographic images of women's breasts were used to diagnose cancer in neural networks. In this article, back propagation neural network (network 1) is used with Lunberg-Markudat training method and its results are compared with hybrid back propagation-wavelet network (network 2) to investigate the condition of women's breasts. The outputs of the two neural networks used in the article have 2 nodes, which indicate whether the person in question has breast cancer or not with the information given to the neural networks. In network (1), correlation coefficient (R=0.9831) and root mean square error (RMSE=0.5538) were obtained as the best function for network training. In contrast to the network correlation coefficient (2), R=0.9945 and root mean square error (RMSE=0.4665) was obtained. The training time of neural network 1 was 45.51 seconds and network 2 was 33.68 seconds. The results of the designed wavelet-back propagation hybrid neural network show that the proposed network is effective in detecting breast cancer with 99.5% accuracy and is able to detect the health status of women's breasts.

Nowadays, solar arrays and wind energy are considered as many renewable energy sources. These sources can deliver their energy to the grid or directly to the consumer. In power electronics, multi-input converters are commonly used to convert the energy of renewable energy sources into the power required by the grid. In this article, a high-gain non-insulated three-port converter based on a quadratic boost converter has been proposed. This converter includes two power supplies with controllable currents. This improves the performance of the converter and reduces the ripple current of the input sources. In addition, the proposed converter has two operating modes and can work with one or two voltage sources. To design a high-gain multi-input converter, first the structure and performance of the proposed converter are thoroughly analyzed and evaluated Then, using MATLAB software, the stability of the proposed converter is checked and the output voltage and input currents are controlled independently using the decoupling network method. Then the main equations are calculated theoretically and its performance in different duty cycles is investigated. Finally, the simulation results for different modes show the correct operation of the converter and controller.

The Iranian people were confronted with a range of emotions during the Covid-19 crisis, which they shared on social media platforms. Social media played a crucial role in disseminating information and reflecting public sentiment during the pandemic. Consequently, governments and health organizations worldwide recognized the importance of analyzing social media data. Many researchers have examined these data using different approaches worldwide. This study focuses on the polarity analysis and classification of messages posted on social media during the COVID-19 crisis. The study analyzed messages shared by Persian-language users on social networks using natural language processing and deep learning techniques. Various deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and fuzzy-LSTM were used to classify the data as positive or negative polarity. The three-layer deep convolutional neural network achieved the highest accuracy of 72.29%. Finally, a comprehensive comparison of the different networks used was conducted across multiple aspects.

Multi-level inverters (MLIs) have now become an essential component for medium and high power applications with medium voltage levels. Low switches multi-level inverters are very popular due to their high efficiency, low cost, and easy control for output with higher levels. In this paper, a new multi-level inverter structure based on a switched DC voltage source is proposed by reducing the number of switches for single-phase applications. The proposed structure can be used in grid-connected applications, such as grid connections for renewable energy sources. The proposed structure is developed with a higher number of levels at the output using a smaller number of devices. The proposed topology can also be used in symmetric and asymmetric configurations. Two switching methods including pulse width modulation (PWM) switching and ladder switching based on selective harmonic elimination (SHE) have been used to generate the output voltage. Comparative studies with multilevel inverters were presented recently to show the advantage of the proposed structure in terms of reducing the number of devices. Simulation and experimental results are presented to confirm the performance of the proposed topology. In addition, the performance of the proposed multilevel structure for energy transfer from renewable sources to the low-power grid has also been investigated.

Thin-walled energy-absorbing elements in compressive loading are widely used in the transportation industry, especially in automobile manufacturing, airplane manufacturing, and urban and intercity train construction. As a new idea, coaxial double-tube energy absorbers are used in this research. The execution method is based on simulation in ABAQUS explicit finite elements software. Based on the validated model, a parametric analysis has been carried out in order to extract the effect of structure thickness, loading angle and density of polyurethane foam on the amount of energy absorption. Examining the deformed geometry of the sample after loading, the dynamic loading coefficient and the effect of the load angle on the maximum value of the structure collapse to the initial length is one of the topics that has been taken into consideration. In the end, according to the design goals, which include the maximum amount of energy absorption, the lowest amount of initial maximum force and the lowest weight of the structure, the optimization process of the design variables, using the optimization algorithm and formulation of multiple goals and with the help of finite element software data, has been completed.

In this research, the usage possibility of minimum quantity lubrication in grinding super alloy Inconel 738 has been studied empirically to reach improved grinding and lubricating conditions. For reaching this purpose, based on Taguchi design of experiment method grinding variables were set in three levels and lubrications were set in six levels in order to compare conventional, dry and MQL methods. Studies have shown that in grinding this material by MQL method we can obtain the results very close to conventional mode in terms of force and surface roughness even having better surface quality. Results in MQL method by considering various oils with different viscosities show that Behzist 6046 and Canola herbal-based oil are the best replacement of conventional method in terms of force reduction and surface roughness. In fact, in the case of using the MQL method together with Behzist 6046 oil, a 50% reduction in the force output is observed and when using Behzist 6043 oil, there is only a 30% difference in the surface roughness obtained with the traditional method, which creates a better surface smoothness is visible. For all the 100 investigated modes, the results show that the optimal levels for the variables of advance speed, stone wheel speed and chipping depth are level 1, 2046 rpm and 5 microns, respectively. The specific vertical force can be predicted in more than 50% of the tests with the least error (about 20%).

Making new materials to control heat transfer has always been of interest. Carbon-based nanomaterials are promising for heat transfer due to their excellent thermal properties. Coiled carbon nanotubes (CCNTs) are among artificial carbon nanostructures due to their special mechanical properties, including high stretchability and good thermal properties are often used in many applications such as making nanodevices or advanced nanocomposites. In this research, using the molecular dynamics simulation technique and non-equilibrium molecular dynamics method, the effect of hydrogen functionalization with hydrogenation percentages of 5, 15, and 30% on the thermal properties of spring nanotubes has been investigated. The results show that the thermal conductivity of CCNTs is strongly affected by functionalization. So that by functionalizing them by 5%, their thermal conductivity coefficient decreases by 50%. Also, unlike other carbon nanostructures, the thermal conductivity of CCNTs does not decrease with the increase in the degree of functionalization, so the coefficient of thermal conductivity of 30% hydrogenated CCNTs is higher than the hydrogenated samples with lower percentages

Classification of isolated digits is a fundamental challenge for many speech classification systems. Previous works on spoken digits have been limited to the numbers 0 to 9. In this paper, we propose two deep learning-based models for spoken digit recognition in the range of 0 to 599. The first model is a Convolutional Neural Network (CNN) model that uses the Mel spectrogram obtained from the audio data. The second model uses the recent advances in deep sequential models, especially the Transformer model followed by a Long Short-Term Memory (LSTM) Network and a classifier. Moreover, we also collected a dataset, including audio data by a contribution of 145 people, covering the numerical range from 0 to 599. The experimental results on the collected dataset indicate a validation accuracy of 98.03%.

This article presents a mathematical model to develop the optimal planning for equipment overhaul under limited access to spare parts, uncertainty, and lack of accurate and complete information. The parameters of repair cost, demand, device efficiency, the selling price of each product unit, and production rate are considered fuzzy parameters. First, the system availability is modeled. The results are used in the development of a non-linear mixed integer programming model so that by solving it, the time of equipment overhaul can be determined in such a way as to minimize the total costs of repairs and operation. The presented approach is implemented for a numerical example, including a hypothetical production system. Finally, with the exact solution, using GAMS software, the optimal answer to the desired problem has been obtained and analyzed. The optimal solution sensitivity analysis facing the change of the main parameters of the model is done, and the results are shown in the graphs. Then the problem is resolved again using the fuzzy optimization approach, and the results are compared with the previous solution method. These results indicate that the fuzzy approach has excellent flexibility in transferring decision-maker's expectations to the modeling process. In this way, the analyst's opinion regarding the prioritization of the objective functions can be well reflected in the mathematical programming model.

The construction industry is one of the most important sectors of the economy all over the world which does not have considerable contribution in the development and use of emerging technologies to improve the productivity of construction projects. The fourth industrial revolution (Industry 4.0) paves the way for promoting the use of emerging information-based technologies such as building information modeling or blockchain technology (BCT) and smart contracts in the construction industry. These types of technologies support cooperation between the parties and balance the influence of stakeholders in the project implementation process. In this study, a decentralized and automatic model and framework based on BCT is proposed for intelligence of the project's time-cost processes by using building information modeling technology. To achieve the goals of the research, BCT and BIM technologies have been studied and the desired model has been developed by creating a relationship between the 3D model, the work breakdown structure and the time and cost areas of the project. Therefore, in this research, the relationship between BCT and Building Information Modeling (BIM) has been investigated and the ability of the proposed model to manage the process of updating the schedule and project costs has been evaluated and confirmed through the preparation of preliminary plans. The present study is one of the first attempts to provide a real-reliable process using the integrated BCT-BIM system for the automation of time-cost domains.

The circular supply chain includes return processes and additional value intends to reduce the waste of resources and improve the efficiency, it plays an important role in reducing costs and increasing the level of sustainability of supply chains. Therefore, in the current research, a multi-objective, multi-level circular supply chain optimization model was presented in in uncertain conditions, which minimizes system costs and environmental impact and maximizes social responsibility. In order to face the uncertainty in demand, a scenario-based approach has been used. Then, the multi-objective model was converted into a single-objective model using the enhanced epsilon constraint method and solved with Gams software. The data of an active company in the MDF industry has been used to examine the application of the proposed model. The results of sensitivity analysis carried out on some important parameters showed that paying attention to the maximum number allowed for the establishment of collection and recycling centers has a significant impact on the costs and environmental effects in the system.

The COVID-19 virus, which was discovered in December 2019 in the city of Wuhan, China and quickly spread throughout the world, continues to be an important threat to the health of the world. Despite all the strategies used to deal with the spread of COVID-19, more contrivances are still needed to deal with its consequences. In this research, the clinical characteristics of people have been used as input data to diagnose a person with COVID-19, which is the result of collecting information from similar studies. Also, various algorithms including support vector machine, logistic regression, k nearest neighbor (k=9), simple bayes, random forest, LightGBM, XgBoost and CatBoost have been used, among which the CatBoost algorithm, with a sensitivity of 97.97%, accuracy 97.72% and 96.96% accuracy showed the best results. In this algorithm, the trial and error method has been used to adjust hyperparameters as accurately as possible to achieve the desired results, and SHAP is used to interpret the results and determine the impact of features on the output.

In many real-world applications, data is dynamic and noisy. In such a situation, anomaly detection should be performed with an online and robust model against noise. In recent years, recurrent neural networks have been used on data sequences and have achieved good performance in this field. The existing methods do not have sufficient robustness against noise. This paper presents a method for anomaly detection in dynamic graph data using recurrent neural networks that are robust against noise and have sufficient adaptivity to changes in the data pattern. The proposed robust recurrent neural network extracts and introduces anomalies for the purpose of noise management. At the same time, it learns the original patterns in an online manner and is adapted to the changes. To evaluate the proposed method, some experiments are presented that measure its ability in anomaly detection in addition to the learning and adaptation ability in comparison with the existing methods. The results have confirmed the superiority of the proposed method.

This research seeks to present a new model of influencing factors on formation spatial structure of middle class housing in Tehran by using the complex systems theory paradigm. Understanding of changes in spatial structure of housing due to transformations of internal and external factors at design time has provided necessary background for this research. The research has been done with a practical-developmental purpose and descriptive-analytical method. Data has been collected in a combined (qualitative and quantitative) manner. In this research, layered models have been presented by review of complex systems paradigm concepts and matching it with housing spatial structure factors. Spatial structure models consist of three main factors: design dimensions, rules and regulations and building elements. The components of rules and regulations have been identified by review of documents content and library studies, components of spatial structure design dimensions have also been expressed accordance with theoretical foundations of sustainable development and design. Importance of design dimensions has been measured by asking experts in field of housing design by using fuzzy hierarchical analysis method. Analysis show that priority and importance each of design dimensions: environmental/ climate, cultural/ social and economic/ technological are variable in each housing spatial structure. The results of research show that change of any spatial structure under influence of internal and external transformations in different locations causes change of all spatial structures. To achieve quality of residential spaces based on transformations, the model provides necessary data for planning and intelligently usage at the time of design.

This paper presents a finite control set model predictive control for a four-level nested neutral point clamped converter (4L-NNPC) in electric drive application. Controlling the voltage of the flying capacitors (FCs) in this converter, especially at low output frequency, is challenging due to the lack of complete switching states to control the FCs voltages. In the proposed method, the main control objectives of the electrical motor and converter, i.e., the control of the torque, flux, and voltage of the FCs, have been formulated. A modified weighting factor for FC voltage objective in the cost function is introduced, which provides the full possibility of controlling voltage fluctuations of FCs, especially in low-frequency operation. The proposed control method, in addition to controlling the electric motor's required objectives, can control the voltage of the FCs of the converter in the entire operating frequency range. The performance of the proposed method has been simulated and verified on a 4L-NNPC converter using a 1500 HP, 4160 V electric drive in the MATLAB/Simulink software environment.

The use of renewable energy has recently become very common in most countries of today's society. Among these renewable energies, wind energy is one of the most attractive methods of mechanical energy production, and different methods of flow control, including active, semi-active and passive, have been investigated by various researchers. To control the fluid flow in an active way on the wind turbine blade, the corona discharge actuator based on plasma is considered the most appropriate method to reduce the fluid flow separation on the wind turbine blade. In this paper, we present a numerical simulation to integrate active load control using a corona discharge based on plasma actuators over the roughness blade. Effects of roughness, actuators voltage and frequency on aerodynamics parameters such as separation point, lift and drag coefficients have been showed. Present results showed that, the lift coefficient increase with increase in the voltage and frequency of plasma actuators. Overall, using the roughness for outer surface of blade would decrease the critical pressure coefficient by approximately 50% compared to that for the smooth surface.