Computational Methods in Engineering

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In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplied by Current-Source-Inverter (CSI). Step response of the system is considered and controller parameters are designed based on multi-objective optimization technique. Rise-time, maximum over-shoot, settling time and steady state error are considered as objective functions. The simulation results of the new method for induction motor speed control and optimization of two nonlinear mathematical functions are compared with the results obtained from other methods [4,14,15], which shows better performance.

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In the process of the optimum design of aprons, solutions should be found for problems and such issues as the optimum area and dimensions of the apron, including the passenger and the cargo the number and dimensions of the gates on the basis of different types of aircraft parking configuration aircraft simulation and arrangement in different time periods of the given day at the airport. In this research, a mathematical model was developed for the analysis and design of airport aprons based on minimum transportation cost. Some of the parameters of transportation cost include user, capital, and operational costs. Moreover, based on the fundamentals of the mathematical model, a computerized simulation model was developed taking into consideration the actual parameters of design of airport aprons such as stochastic demand, passenger behaviour, and evaluation of analytical model. The results obtained from the computerized simulation model indicate that policies of the airport authorities and air carriers such as flight schedules, gate use strategy, the mix of aircraft fleet during the planning horizon, operational conditions, and economic cosiderations have significant impacts on the design of the aprons. Keywords: Airpornt, Apron, Optimization, Design.

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In this paper, two algorithms have been developed for allocation and size determination of Active Power Filters (APF) in power systems. In the first algorithm, the objective is to minimize harmonic voltage distortion. The objective in the second algorithm is to minimize the new APF injection currents while satisfying harmonic standards. Genetic algorithm is proposed for these two optimization problems. The simulation results for an 18-bus system show the effectiveness of the genetic algorithm for these two optimization problems. Keywords: Genetic Algorithm, Active Power Filter, Harmonics, Allocation, Optimization

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Reductive leaching was used to dissolve metals, especially cobalt, present in Fars Tidar mine,. In this paper, cobalt ore was leached with sulphuric acid in the presence of phenol to determine the effects of various factors on leaching. These factors included temperature, acid concentration, time, phenol content, pulp density, and interaction between some of the parameters. The results indicated that temperature was more effective on SN ratio (Signal to Noise ratio) which was found to be about 80%. The effecst of time and acid concentration on SN ratio were also determined at about 8% and 4 %, respectively. Although the effect of phenol content on cobalt leaching was too low but dissolution of cobalt decreased in the absence of phenol. Therefore, it was concluded that phenol was one of the factors in effective the leaching process. Anyway, three parameters including temperature, acid concentration, and time were selected as more effective parameters. Consequently optimum conditions can be obtained with high levels content of temperature, acid concentration, and time with low levels of phenol and pulp density.

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This paper introduces an accurate, fast, and applicable method for optimization of slip surfaces in earth slopes. Using Genetic Algorithm (GA), which is one of the modern and non-classic optimization methods, in conjunction with the well -known Bishop applied method, the optimum slip surface in an earth slope is investigated and its corresponding lowest safety factor is determined. Investigations have shown that selection of appropriate variables to define and to solve the problem and determination of a good range for these variables have a profound effect on the speed of convergence in the problem. In the present study, appropriate variables have been defined for solving the problem in a way that the number of repetitions required to reach convergence are considerably reduced by up to 50% compared with other approaches. This has led to a drastic reduction in time and the memory required. The accuracy of the method is shown first by solving examples related to search for optimum failure surfaces of some homogenous, non-homogenous, and earth dam slopes and then by comparison of the results with those of other optimization techniques. In order to show the application of the present method in modern geotechnical engineering, a reinforced earth slope is studied and its failure surface is finally optimized

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Marine structures are one of the most important and susceptible facilities in Iran due to corrosion. The two methods of Cathodic Protection, namely, the cathodic protection with sacrificial anodes and cathodic protection using impressed current, are widely used for corrosion protection. According to the former, sacrificial anodes are installed at several points in the structure. Position of the anodes for achieving the required protection is a problem that engineers are very much interested in, and only empirical methods have so far been used to determine these positions. Empirical rules, however, might cause either overprotection or underprotection. A major goal of this research is to develop a systematic way for analysis and automated design of Cathodic Protection systems that not only deliver almost uniformly protected structures but also minimize the costs. To this end, a Genetic Algorithm (GA) routine is used to determine the optimal position of anodes on the structure such that a uniformly protected design with minimum cost is achieved. The percentage of protection in each design has been taken as its fitness criterion. To figure out the situation of corrosion protection on the structure, the entire offshore structure with its complex system at anodes and surrounding electrolyte is modeled and analyzed by a finite element algorithm. Employing GA gradually modifies the generation of designs. The design which completely protects the structure and whose cost is minimum is introduced as the optimum design. To show the capability of the proposed method in achieving the optimum design, two examples are offshore presented.

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Since there is no closed-form formula for designing TMD (Tuned Mass Damper) for nonlinear structures, some researchers have proposed numerical optimization procedures such as a genetic algorithm to obtain the optimal values of TMD parameters for nonlinear structures. These methods are based on determining the optimal values of TMD parameters to minimize the maximum response (e.g. inter story drift) of the controlled structure subjected to a specific earthquake record. Therefore, the performance of TMD that has been designed using a specific record strongly depends on the characteristics of the earthquake record. By changing the characteristics of the input earthquake record, the efficiency of TMD is changed and in some cases, it is possible that the response of the controlled structure is increased. To overcome the shortcomings of the previous researches, in this paper, an efficient method for designing optimal TMD on nonlinear structures is proposed, in which the effect of different ground motion records is considered in the design procedure. In the proposed method, the optimal value of the TMD parameters are determined so that the average maximum response (e.g. inter story drift) resulting from different records in the controlled structure is minimized. To illustrate the procedure of the propose method, the method is used to design optimal TMD for a sample structure. The results of numerical simulations show that the average maximum response of controlled structure resulting from different records is reduced significantly. Hence, it can be concluded that the proposed method for designing optimal TMD under different earthquakes is effective.

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In this paper, a new method is proposed for fuzzy structural reliability analysis; it considers epistemic uncertainty arising from the statistical ambiguity of random variables. The proposed method, namely, fuzzy dynamic-directional stability transformation method, includes two iterative loops. An internal algorithm performs the reliability analysis using the dynamic-directional stability transformation method and an external algorithm performs the fuzzy analysis by applying the alpha-cut level optimization method based on the genetic algorithm. Implementation of the proposed method, which solves some nonlinear performance functions, indicates the efficiency and robustness of the dynamic-directional stability transformation method, as compared to other first order reliability methods.

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The behavior of many types of fluids can be simulated using differential equations. There are many approaches to solve differential equations, including analytical and numerical methods. However, solving an ill-posed high-order differential equation is still a major challenge. Generally, the governing differential equations of a viscoelastic nanofluid are ill-posed; hence, their solution is a challenging task. In addition, the presence of very tiny nanoparticles (lower than 100 nm) induces new heat and mass transfer mechanisms which can increase the complexity of the behavior of the viscoelastic nanofluids. Therefore, creating or developing new analytical or semi-analytical approaches to solve the governing equations of these types of nanofluids is highly demanded. In the present study, by using a new idea and utilizing an optimization approach, a new solution approach has been presented to solve the governing equations of viscoelastic nanofluids. By using the optimization method, a basic initial guess was changed toward an optimized solution satisfying all boundary conditions and the governing equations. The results indicate the robustness and accuracy of the presented method in dealing with the high-order ill-posed governing differential equations of viscoelastic nanofluids.

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One of the common approaches for improving the reliability of a specific system is to use parallel redundant components in subsystems. This approach, which is known as the redundancy allocation problem (RAP), includes the simultaneous selection of the component type and its level for each subsystem in order to maximize the system reliability.Traditionally, there are two redundancy strategies, namely active and standby, for the redundant components. Recently, a new powerful strategy called mixed strategy has been developed. It has been proved that the mixed strategy has a better performance when compared to both previous strategies. The main issue in utilizing the mixed strategy is its complicated formulation and sophisticated calculations, leading to a time-consuming procedure for solving the problems. Hence, in this paper, a new formulation based on the recursive approach is introduced to ease the calculation of the mixed strategy. In the new formulation, the complex double integral calculations are removed and the calculation times is reduced. The proposed recursive formulation provides a general statement for the mixed strategy formula which is not changed by altering the number of components in each subsystem. This flexibility and stability in the formula can be very important, especially for large scale cases. In order to evaluate the new approach and to compare its performances with the previous formulation, a benchmark problem with 14 subsystems is considered and the results of the two formulation are compared with each other.

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One of the most important issues in engineering is to find the optimal global points of the functions used. It is not easy to find such a point in some functions due to the reasons such as large number of dimensions or inability to derive them from the function. Also in engineering modeling, we do not have the relationships of many functions, but we can input and output them as a black box. Therefore, the meta-heuristic algorithms are presented.
In this paper, a meta-heuristic algorithm based on the behavior of vortices in fluid physics is presented. Technically, the algorithm is made up of vortices. Each vortex contains some particles. The particles move by the presented rotation matrix. This movement causes the local search. Also by selecting another vortex through the selection algorithm, each vortex attempts to escape the local optima and reach the global optima. The algorithm will explore and exploit the given function using its operators. Another innovation of this paper is the introduction of two new evaluation criteria for optimization algorithms. These two criteria show the behavior and convergence of algorithms along the way to reach the global optimal point or fall into the local optima. The proposed algorithm has been implemented, evaluated and compared with the numerical optimization state of the art algorithms. It was observed that the proposed method was able to achieve better results than most of the other methods in the major of twenty-four standard functions in different dimensions.  (All codes available at http://web.nit.ac.ir/ h.omranpour/.).

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In this paper, a robust optimization method is developed to solve the Satellite Launch Vehicle (SLV) trajectory design problem in the presence of uncertainties using a powerful Particle Swarm Optimization (PSO) algorithm. Given the uncertainties such as uncertainties in the actual values ​​of aerodynamic coefficients, engine thrust, and mass in the ascent phase of a SLV, it is important to achieve an optimal trajectory that is robust to these uncertainties; because it improves the flight performance, reduces the workload of the guidance-control system, and increases the reliability of the satellite. For this purpose, first the optimization problem is considered by using the criterion of minimizing the flight time of the SLV as a cost function, and three-dimensional equations of motion as constraints governing the problem. Then, by adding the mean parameters and the standard deviation of uncertainties in the cost function, a robust optimizer model is developed and the algorithm is used to numerically optimize the model. Monte Carlo's perspective has also been used to analyze the results of uncertainties and their continuous feedback to the optimization model. Finally, the optimal trajectory is obtained that is robust to the uncertainties. The resulting simulation results show the accuracy of this claim.