جستجوی مقالات مرتبط با کلیدواژه "مدل سازی" در نشریات گروه "فنی و مهندسی"

Most of the noise pollution in big cities is related to traffic, and the construction of highways and traffic flow with high volume and speed creates problems for the nearby residents. Calculating the amount of noise pollution through modeling makes it possible to reduce the effects of noise pollution in the design of highways and surrounding uses. This research, which was conducted on the Hashemi Rafsanjani highway (Misthaq) in Mashhad, by measuring the volume of sound, traffic information, weather information and surrounding uses, using the American federal highway management model (FHWA) with TNM 2.5 software models the emission of noise pollution and compares the obtained results with the measurements made through statistical analysis. The following steps were implemented for this research: 1- collecting information about the volume and speed of traffic, 2- collecting information and characteristics related to the road such as road width, road type, surrounding land use map, green space around the highway, height and density of buildings around the highway, meteorological information such as temperature and humidity, 4- determining sensitive points around the area in terms of noise pollution, such as residential, medical, educational centers, etc., and measuring sound in sensitive points as sound receptors, 5- modeling by TNM 2.5 software, AutoCAD map or GIS layers for the scope of the project should be prepared and converted to DXF format, 6- statistical analysis and comparison of the results obtained through the model and measurements and comparison with the standards, and 7- testing the hypotheses through SPSS statistical software and analyzing the results in the Excel environment. The estimated results of the FHWA model were very close to the observed values (The highest value is 85 db and the lowest value is 55 db), therefore, according to the results of this model, it is reasonably accurate for predicting noise pollution, and the average difference of the Leq values ​​calculated with the measured values ​​is ±3.5 dB. The regression coefficient between the measured values ​​and the values ​​calculated by the model was reported as 0.685. The amount of difference of the predicted values ​​is relatively high only in three stations 1, 7 and 24 and is equal to 14.8, 10.7 and 10.7 dB respectively. In examining the cause of this issue, it can be stated that two stations 1 and 7 are located in a position where, in addition to the sound they receive from the studied highway, they are affected by the traffic of vehicles on other routes near them and receive sound. In the study area, due to the fact that the majority of uses are residential, which is considered as a sensitive area, all measured values ​​in the stations were higher than the standard of the Environmental Protection Organization (55 db). The lowest recorded sound is related to station number 36 with a level of 55.4 dB. This station is located inside the barren lands and is connected to the agricultural lands located between Amirieh and Esmatiye boulevards. The average amount of noise pollution in all stations is 74. The highest sound value is related to station number 24 and is equal to 85.1 dB, which is located at the beginning of Hashemi Street 45 and the dominant use around it is residential. Due to the large number of vehicles, including cars, heavy vehicles, buses, semi-heavy and motorcycles, all areas show high noise pollution that can endanger the health of citizens. The effect of car type was investigated by Pearson's test, and there was the highest correlation between motorcycles and noise pollution, as the number of motorcycles increased, the value of the two noise indices LAeq and LAFmn increased with correlation coefficients of 83% and 84%. In this regard, it is necessary to take necessary measures such as replacing with new electric motorcycles. Allocation of land around highways to sensitive uses, including medical and educational centers, has not been approved, and the effects of noise pollution can be reduced through appropriate planning. There are several solutions to reduce the effects of noise pollution, based on priority, it is possible to correctly locate the uses, consider the distance between highways and sensitive uses, assign this distance to uses such as linear green space, footpaths and bicycles, the creation of barriers that reduce sound waves such as embankment, trees and finally the last priority of using technical equipment such as acoustic wall.

One of the key elements to achieve economic growth and development of the country is financial stability. According to the empirical evidence in recent years and the intertwining of the financial system and the economic development of countries, high economic growth cannot be considered, but it was not successful in the financial markets. Therefore, considering the importance of the combined index of financial and economic stability in the economic growth and development of the country and especially the road transport sector, in this study the role of the combined index of financial and economic stability in the road transport sector of the country during the period of 1370-1401 has been examined. In the first step, the combined index of financial and economic stability was calculated with the help of the cumulative distribution function method, and then to investigate the effects of the combined indicators of financial and economic stability on the road transport sector, the Autoregressive Distributed Lag model was used to achieve the research findings. The results show that both in the short term and in the long term, the combined index of financial and economic stability has a positive effect on the added value of the road transport sector. The short-term and long-term elasticity of added value of the road transport sector is 0.074 and 0.153 respectively, which shows that its long-term elasticity is more than the short-term elasticity. Also, the results of the stability and diagnosis tests show that the coefficients of the variables are stable in the long term..

In this study, a model for pricing taxi services is presented with the goal of making the model fair and transparent in a way that reflects the value of the services the taxi provides to the customers while also taking into account various factors that influence the cost of the services. The major pricing strategy in this model bases the taxi fee on the distance covered during the journey. Additionally, the duration of the trip is taken into account because some people may choose a longer journey others may the length of the trip can be longer than normal because of the traffic. The hourly rate of time is taken into account to account for this anomaly. By dividing the hourly rate of travel time by the additional cost, the duration of the journey is determined based on the conditions anticipated by the model. Different pricing strategies are utilized for various hours and designated time periods to control the demand. Price discrimination is the foundation of this tactic. By managing demand, it makes cabs available even during rush hour. In order to accurately and fairly compute the cost based on the real time and distance traveled, the pricing model developed in this research takes into account all the aspects impacting the service for four different modes of operation. Comparing the predicted costs using the research's methodology reveals that practically current fares are unprofitable.

The prevalence of infectious diseases in the community depends on various factors, including the severity of the disease, compliance with health and communication protocols, vaccination rate, impact factor and duration of its effectiveness on immunization, and effectiveness of treatment protocols and average duration of treatment. Having a model based on which the behavior of the disease can be predicted according to various parameters, can help community leaders to deal with these types of diseases. In the studies conducted so far in this field, the impact of some factors such as compliance with health guidelines, the latent period of the disease and immunity after the disease at the same time as the main factors such as the prevalence rate and vaccination rate have not been fully considered. In this paper, a new and comprehensive model based on Markov theory is presented for predicting the behavior of the Covid-19 disease. This model can imitate the behavior of the disease in different conditions by receiving the given parameters. Numerous simulations with different values of input parameters and their similarity to the actual behavior of Covid-19 disease show the accuracy of the model.

Various methods have been proposed for modeling solar panels, but modeling solar power plants using them is associated with challenges. In equivalent circuit-based methods, the modeling depends on factory data that changes over time. Modeling of voltage-current characteristic using intelligent methods such as neural network was less considered due to the low accuracy of modeling. In this article, a method independent of the manufacturer's data for modeling the solar power plant is presented, so that it is possible to accurately model the solar power plants that have been installed for several years. The proposed method consists of two steps, in the first step, open circuit voltage, maximum power point and short circuit current are modeled according to atmospheric conditions using neural network. In the second step, the unknown parameters of the equivalent circuit are determined by circuit analysis relations and using neural network outputs. Finally, to evaluate the proposed method, a 3-kW solar power plant was modeled, and the results show the appropriate accuracy of the proposed method for modeling the solar power plant.

The management of petroleum wastewater with the aim of protecting the environment in the process of oil and gas extraction is one of the most important actions that has received special attention in recent years.The release of petroleum effluents into the environment causes harmful chemicals to enter nature and motive irreparable damage to the environment. One of the most common methods for separating these chemicals from wastewater is using evaporation ponds, which perform the separation process with the help of water evaporation. However, several factors such as rainfall and lack of access to solar energy have negative effects on the efficiencies of these evaporation ponds. Accordingly, considering the importance of petroleum wastewater management, the purpose of this research is to study the effect of using phase change materials on the amount of evaporated water and the water temperature of the evaporation ponds. For this purpose, initially, the temperature changes of wastewater of one of the refineries are modeled by applying the energy balance in the pond. Then, by adding a tank containing a phase change material in the evaporation ponds, the temperature changes of the effluent are re-examined with new equations.By comparing the modeling results in these two cases, it was found that by adding a tank containing C33 paraffin (as a phase change material) into the pond, 735 kg of the fluid will be evaporated during the night compared to the normal state. Additionally, it was found that if a copper fin is used on the tank, the heat transfer can be increased up to 10 times.

In this study, the distribution of odorant in a natural gas pipeline network is investigated using Computational Fluid Dynamics (CFD) techniques. The focus is on an injection-type odorizer, with the odorant distribution analyzed using finite element methods up to a distance of 1 kilometer from the injection point. Key parameters influencing the performance of the injection pump, such as injection time, injection volume, and the number of pulses, are examined. Moreover, the modeling results indicate that the odorant concentration stabilizes at approximately 200 meters from the pipeline›s origin. Additionally, when the CGS gas station operates at a capacity of 7000 m³/hr, the odorant consumption exceeds the required amount. Even with a reduction in injection volume from 200 mm³ to 100 mm³, the output concentration remains within standard limits. Under lower loading conditions, the final odorant concentration reaches about 85 mg/m³, which is nearly four times than the standard level, resulting in a pronounced odor in the area. Ultimately, for the specified CGS gas station, optimal values for the pump›s performance parameters during both day and night hours are identified. Also, the optimal injection rate of odorant is 16 pulses per minute during the day and 8 pulses per minute at night.

In this paper, the process of designing and construction of three-drive laboratory Drone system with cold gas thrusters as well as modeling and estimation of its model parameters are presented. These three thrusters are designed with an angle difference of 120 degrees to control the pitch and roll angles. According to the structure of the designed platform, a three-inputs-two-outputs linear model is considered for modeling the mentioned system, and all connections between inputs and outputs are second-order linear models. The parameters of the model have been extracted based on the collection of laboratory data and the use of genetic algorithm. The results obtained from the simulation based on the extracted model and the comparison with the results of the practical tests indicate that a suitable modeling and estimation of the system has been provided. The results show that there is 16% error for modeling and estimation around the roll axis and 19% around the vertical axis, so the estimation around the roll axis is better.

In this Paper, modeling, open-loop analysis and design of the centralized control system with the nonlinear control law of the dual-body UAV are discussed. In the two-body configuration, two completely identical aircrafts are connected to each other using a one-degree-of-freedom connection around the longitudinal body axis(hinge at the tips of the wings). The 6DOF dynamics of the of the two-body UAV is modeled using the Newton-Euler method. Changes in the behavior of two-bodied aircrafts in the form of different modes have also been investigated. Although the equations governing the flight of a double-body UAV are fully nonlinear and coupled, the nonlinear control laws for multi-body UAV have not been found in the articles in this field. Also, the control system developed by different teams is only decentralized, which increases the cost of the control system. In the present paper, the centralized control system with the nonlinear control law is proposed to solve the mentioned problems. The numerical simulation results show that the nonlinear control system has a better performance than the linear control and the time response graphs are very smooth and without any overshoots. The deviation angles of the control surfaces and trust levels are also limited and reasonable.

The non-slip condition is one of the acknowledged conditions in the common fluid dynamic applications. In some specific cases, this assumption is challenged. When high speed and pressure occur in a part of the flow near the surface, such as in the lubrication of non-conformal surfaces, there is a possibility of slippage on the surface. In this study, the effect of fluid slipping on the surface in elastohydrodynamic lubrication has been numerically investigated. A numerical model was derived based on the equations governing the flow of the lubricating fluid and with the assumption that the fluid is Newtonian and the flow is isothermal. The geometry includes an upper long cylindrical surface and a lower flat surface. The equations were discretized by the finite difference method and solved by the least squares method. In this investigation, in addition to proving the existence of slip, its effect was considered for various values of problem parameters, such as the speed of the surfaces, load, the ratio of the speed of the surfaces, and the limit shear stress. The results showed that with the increase in speed and load, a significant slip occurs and the no-slip condition causes a deviation of 20% till 90% in the estimation of the friction coefficient. At the speed ratio near one, the slippage is higher. As the shear stress increases, the occurrence of sliding becomes more limited. Slipping reduces the friction of lubrication.

The lower limbs are one of the most important and essential organs in the process of pushing a bicycle by an athlete. Therefore, understanding the biomechanical mechanism of pedaling is particularly important. The most important joint involved in the process of pushing a bicycle is the knee joint. The main goal of this research is to evaluate the biomechanical lower limb of a cyclist. In this regard, the optimum position of the bicycle saddle in relation to its stirrup was examined and determined in order to exert the least amount of force on the knee. The cyclist's leg while pedaling was modeled as a four-link mechanism. Kinematic and kinetic analysis was used to obtain forces and torques on each member and joint. To solve the final equations, a numerical code was used that was written by the authors of the article with the help of the MATLAB software. The results show that the highest amount of force is applied to the athlete's knee at an angle of 45 degrees and the lowest amount of force is at an angle of 81 degrees. With the help of the numerical code of the present work, the horizontal and vertical distance of the bicycle saddle can be calculated and adjusted for each athlete.

Today, the development and use of information systems is essential for any organization. Business intelligence is a set of tools, processes, and technologies that transform raw data into information and extract knowledge from this information to facilitate and optimize the decision-making process. The purpose of this research was to evaluate the identification of effective drivers of intelligent systems in the field of business process modeling in a media content production company as a real case study; it was carried out with a descriptive-analytical approach in terms of practical purpose and based on library and field studies. To identify the conceptual indicators that explain the main drivers of intelligent systems and business processes, a purposeful documentary and the Delphi method were used. The sample population consisted of employees of the intelligent business system user and experts related to the research topic (30 people). The current research analyzed the components of intelligent systems and business processes by using the cross-effects analysis technique, which is a common and accepted method of forecasting using Micmac software. To achieve the objectives of the research, five components (operating dimension of the intelligent system, meeting the needs of users, meeting the needs of the organization, services, integration ability, and analysis ability) and 18 items regarding intelligent business systems using theoretical foundations were extracted. To analyze the data, the path analysis test using the Smart PLS structural equation model was used. The findings from the prioritization results showed the effective dimensions in intelligent business systems: meeting the needs of the organization (0.732) ranked first in importance, analysis ability (0.461) ranked second, followed by services and integration ability (0.325), operational dimension (0.199) and fulfillment Building users' needs (0.064). Finally, according to the results related to the analysis of cross-effects in identifying the drivers effective in intelligent business management systems, six key factors (system response speed, customer relationship management, mechanization of organization processes, comprehensive analysis, supply chain management, and system security) were identified. The results of this research led to the presentation of a framework for evaluating the performance of the intelligent business system and the optimal performance of this system in the studied company.

Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction. In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24%

In this study, the bond strength between rebar and high-performance fiber-reinforced cementitious composites (HPFRCC) containing the combination of three types of steel fibers (1% and 2%), polypropylene and polyvinyl alcohol (0.1, 0.2, 0.3 and 0.4%) has been investigated at the temperature of the laboratory, 400 and 600 °C. For this purpose, at first, 19 specimens were constructed and evaluated for compressive strength testing under the mentioned temperatures, as the silica fume was considered 20% by weight of cement. Among the constructed HPFRCC, four superior mix designs were selected for investigating the bond strength between rebar and HPFRCC using pullout test. The results showed that the bond strength between rebar and HPFRCC samples containing 2% steel fibers with PP fiber in such a way that increasing the temperature up to 400 °C, decreased about 38%. while this reduction rate for the samples containing PVA fibers is about 26%, and this means that PVA fibers have a better performance than PP fibers in term of the bond between concrete and rebar when exposed to high temperatures. By increasing the temperature up to 600°c, the bond strength of rebar and HPFRCC continues to decrease until this drop is about 64% for selected samples containing fibers (2% steel and 3% PP) at the laboratory temperature (i.e., 23°c). The reduction for the HPFRCC sample containing 2% steel and 2% PVA fibers is calculated by 62%. The results of this study and the literatures indicated the effect of different parameters on the bond strength, so for further investigation, the bond strength modelled using artificial intelligence models. The results of rebar bond strength modeling in HPFRCC showed that the performance of the adaptive multivariate regression splines based on error statistical criteria was more accurate than the artificial neural network.

The high statistics of accidents compared to the population and vehicle ownership index in Iran has put reducing the number of accidents more than ever before on the agenda of road engineers. Speed is one of the most effective factors in causing accidents. One of the physical tools for controlling the speed of vehicles is the use of speed bumps and humps in the streets. This study examines the effect of proper implementation of speed humps on reducing the speed of vehicles, as well as reducing the number of accidents. To this end, the speed of vehicles before and after installing 64 speed humps in areas 2 and 10 of Mashhad was recorded through field research. In addition, the physical specifications of speed bumps, such as width, height, distance from intersections, number of lanes on the streets, and distance from warning signs to speed bumps, were measured. Then, using statistical analyses, the effectiveness of each of these parameters in reducing vehicle speed was evaluated. Furthermore, based on the accident statistics of the targeted streets, data before and after installing speed bumps were analyzed using SPSS software and regression analysis. After conducting the initial analysis, it was found that none of the linear regression models were suitable for predicting the accidents. Therefore, the data were analyzed using a nonlinear multiple regression model (R^2=0.87). The results of the analysis showed that the use of speed bumps can have a significant impact on reducing accidents in streets and intersections, provided that proper safety measures are taken into account in their design and installation. In fact, speed bumps can lead to a reduction of approximately 17% in accidents.

This study aimed to investigate the relationships between driver locus of control and risky behaviors, as well as traffic violations and accidents. Participants in this study were 289 male and 116 female drivers who completed a form including the newly developed Iranian Driver Traffic locus of Control Questionnaire (TLOC-IR), Driver Behaviour Questionnaire (DBQ), and items related to drivers’ driving records and demographics. The results of structural equation modeling (SEM) showed that most components of the locus of control, except for the sub-section of religious factors and other drivers, have a significant relationship with driving behavior. According to the results of this study, the internal locus of control has a negative relationship with various types of violations and driving errors, and on the other hand, the components of the external locus of control, including luck and destiny, have a positive relationship with more risky driving behavior and accidents. These findings indicate that locus of control is an effective variable in predicting the behavior of Iranian drivers and can be used to determine effective strategies to reduce high-risk driving. According to the results of this study, drivers with internal locus of control show safer behaviors than drivers with external locus of control; Therefore, strengthening the internal locus of control in drivers can lead to safer driving behaviors.

In water-cooled and moderated reactors, radiolysis is the source of hydrogen, deuterium, and oxygen production, as well as hydrogen peroxide (deuterium peroxide). Modeling the radiolysis phenomenon from a safety perspective is crucial for predicting the production of corrosive or explosive compounds. In this study, a gamma radiolysis model was developed for light and heavy water, and ode15s was selected as the appropriate solver for the model. The validation results of the developed model indicate an error of less than 5%. The absorbed dose effect in the range of 0.5 to 100 Gy/s, temperature in the range of 20 to 70 °C, and the effect of initial hydrogen (deuterium) concentration in the range of zero to 50 ppb were investigated for both light and heavy water. The results showed that gamma radiolysis has more obvious effects on light water compared to heavy water, and the rate of change in the concentration of oxidizing compounds, oxygen, and hydrogen (deuterium) peroxide, is lower than the rate of change in hydrogen (deuterium) concentration. The maximum difference between the final concentration of hydrogen and deuterium occurs at an absorptive dose of 100 Gy/s, where the produced hydrogen concentration is 3.5 times higher than the produced deuterium concentration from radiolysis. As the temperature increases from 20 to 70 °C, the stable concentration of radiolysis-produced compounds decreases by a maximum of 70% for hydrogen and 50% for deuterium. Increasing the initial concentration of hydrogen (deuterium) always reduces the concentration of oxidizing species (oxygen, hydrogen (deuterium) peroxide). However, the graph of produced hydrogen (deuterium) exhibits an optimal threshold (20 ppb for hydrogen and 10 ppb for deuterium). Therefore, by controlling the absorptive dose, temperature, and initial hydrogen concentration to the optimum values, corrosion resulting from the production of oxidizing agents and hydrogen (deuterium) explosions can be prevented.

Without hydrological models the investigation and management of watersheds can be time-consuming and costly. This study aims to explore various hydrological factors including runoff, sedimentation, evaporation, and transpiration in Pasikhan watershed located in Guilan province. The study also examines the impact of these factors on the Anzali Wetland. To achieve this, the research utilizes the SWAT model. Data on flow discharge, precipitation, and sedimentation for three hydrometric station in the Pasikhan watershed from 1999 to 2020 were obtained from the regional water company of Guilan province. The necessary maps for model execution were obtained using established methods. Predictions for various factors, such as flow rate, sedimentation, surface runoff, evaporation and transpiration, deep aquifer recharge, curve number, deep infiltration, and watershed runoff, were made for the years 2040 and 2071. Discharge-sediment from 1999 to 2011 is used for calibration and for validation used the data from 2012 to 2022 is used. The study also assessed changes in hydrological components caused by climate change using the RCP8.5 scenario and land use changes. The findings demonstrate variations in multiple hydrological elements and precipitation patterns within the catchment basin. The Nash Sutcliffe criterion yielded values between 0.60 and 0.80 for both the calibration and validation phases of the model in terms of flow rate and sedimentation. This indicated that the implementation of the SWAT model is satisfactorily accurate. Results showed that the maximum surface runoff within the basin will reach 485 mm in 2055, coinciding with a rainfall of 1431 mm. Additionally, the study identified the highest levels of evaporation and transpiration as 1707 mm within the RCP8.5 scenarios in 1996. Overall, the research emphasizes the negative impact of sediment generated within the Pasikhan watershed on the Anzali Wetland, highlighting the need for effective control measures to mitigate wetland degradation

Landslides are a common natural hazard around the world, but the accuracy of the maps produced can be impacted by various adverse effects and uncertainties. Researchers have continuously sought to improve the accuracy of landslide susceptibility maps. This study aims to create a landslide susceptibility map for Austria using t-test and random forest models. Nine criteria for landslide occurrence, including elevation, slope, aspect, distance to drainages, distance to faults, distance to roads, land cover, lithology, and precipitation, were used. In the t-test model, the weight of each criterion was calculated using the t-statistical test and then combined with each other using the Simple Additive Weighting technique to draw the final landslide susceptibility map. The random forest model was trained using multiple decision trees and based on the landslide occurrence points and criterion layers, the relative weight of each layer was calculated, resulting in the final landslide susceptibility map. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to compare the two models, with the results showing that the random forest model performed better with an AUC of 0.893, compared to the t-test model with an AUC of 0.852. The importance of different criteria was assessed, and it was found that slope and precipitation were the most important factors in the occurrence of landslides in both models. The results showed that both models have unique advantages in landslide susceptibility mapping. Accordingly, the higher accuracy of the random forest model, and the possibility of weighting the criteria and sub-criteria in the t-test model, make both models practical in this field.