Sharif Digital Repository

We introduce a new robust simulation optimization method in which the probability of occurrence of uncertain parameters is considered. It is assumed that the probability distributions are unknown but historical data are on hand and using φ-divergence functionality the uncertainty region for the uncertain probability vector is defined. We propose two approaches to formulate the robust counterpart problem for the objective function estimated by Kriging. The first method is a minimax problem and the second method is based on the chance constraint definition. To illustrate the methods and assess their performance, numerical experiments are conducted. Results show that the second method obtains... 

In this paper, optimization and control of a tubular reactor for thermal bulk post-polymerization of styrene have been investigated. By using the reactor mathematical model, static and dynamic simulations are carried out. Based on an objective function including polymer conversion and polydispersity, reactor optimal temperature profile has been obtained. In the absence of model mismatch, desired product characteristic can also be obtained by applying the corresponding reactor wall or jacket temperature profile. To achieve this temperature trajectory, reactor jacket is divided into three zones and jacket inlet temperatures are used as manipulated variables. Effectiveness of the proposed... 

Monte Carlo simulations have been carried out for 2-methoxyethanol in an isothermal-isobaric ensemble (NPT) at 298.15 K and 1 atm pressure. The optimized potential for liquid simulation force field parameters has been used for modeling 2-methoxyethanol and the TIP4P model for water. Intramolecular rotations are described by an analytical potential function fitted to ab initio energies. It has been shown that the water molecules can form hydrogen bonds between adjacent O atoms of CH3OCH2CH2OH in aqueous media. The self-association of 2-methoxyethanol in aqueous media has been studied by statistical perturbation theory  

This paper presents artificial neural network (ANN) meta-models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta-models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non-linear nature of the ANN, the SO problem is iteratively approximated via non-linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane-relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition... 

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

In this paper, a flux search controller is proposed to increase the efficiency of a direct torque controlled induction motor with light load. The reference flux value is determined through a two stage minimization algorithm with the amplitude of the stator current as the objective function. In the transient state, a great flux step is used to speed up the convergence. In the steady state, a noise cancellation algorithm is used to let using small flux step and improves the steady state behavior of flux controller. Simulation and experimental confirm the fast dynamics of the proposed method and its superiority compared to the other optimal flux search controllers. © 2005 IEEE  

In this paper, design, simulation and optimization of a novel electrothermally-activated polymeric microactuator capable of generating combination of bidirectional lateral and rotational motions are presented. The composite structure of this actuator is consisted of a symmetric meandered shape silicon skeleton, a SU8 thermal expandable polymer and a thin film chrome layer heater. This actuator is controlled by applying appropriate voltages on its four terminals. With the purpose of dimension optimization, a numerical parametric study is executed. The modeled actuator which is 1560 ?m long, 156 ?m wide and 30 ?m thick, demonstrates a remarkable lateral displacement of 23 ?m at power... 

It is important to test the attitude control systems on satellites before they are launched in space. Traditionally this has been done by dropping the satellite, and firing the thrusters before the satellite makes a soft landing in a net. This method only allows a few seconds of testing and does not lend itself to the measurement of pointing accuracy. A better method is to mount the satellite on a spherical air bearing. In this paper behavior of a semi spherical air bearing is studied and analyzed in various conditions. These bearings are used in different applications such as simulation of approximately frictionless condition which is the satellite's situation in space. In this analysis... 

We study the problem of optimizing the symbol error probability (SEP) performance of cluster-based cooperative wireless sensor networks (WSNs). We propose that to achieve the minimum SEP at the destination, a joint optimization of power allocation and relay selection should be accomplished. To this aim, we reformulate the SEP performance of a simple cluster-based cooperative WSN in the general form and solve the joint optimization problem efficiently. Simulation results demonstrate that the proposed joint optimization can efficiently improve the SEP performance of the network in comparison with the previous disjoint optimal relay selection schemes  

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)...