In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex optimization problem that guarantees the global optimal solution. The convex optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

In this paper, the structural optimization of a space capsule has been discussed by approximating a thin-walled cylindrical shell with a certain length under the axial compression force and constant lateral pressure. Design variables include the outer diameter and cylinder thickness. The purpose of optimization is to minimize the mass and maximize the frequency of the first vibration shape mode of the cylinder. The design constraints include the buckling load multiplier (buckling safety factor) above 1. 5 and Von Mises stress below 100 MPa. In this article, first, according to the permissible limits of the design variables, a design of experiment (DOE) and then a sensitivity analysis was carried out to check the sensitivity of the objective functions and constraints to the design variables. After numerically solving the output values with the help of Ansys software and preparing the response surface, the optimal design point has been identified with the help of the two objectives optimization Genetic algorithm. Then, with the numerical simulation of the optimal point, the accuracy of the values obtained from the response surface method was checked and their accuracy was confirmed. The results show that at the selected design point, Von Mises stress becomes less than its allowed value, i. e. 100 MPa, and also the buckling load factor is more than twice its minimum allowed value. However, this point has the smallest distance from the origin and the optimum point has been chosen as the knee point.

As agriculture consumes the most parts of water resources, management and control in this sector plays a significant role in water resources management. In this study, simulation-optimization approach was applied using soil and water assessment tool (SWAT) in combination with non-dominated sorting differential evolution (NSDE) algorithm to find the best operation curve for wheat irrigation in Mahabad basin. The wheat production in 2011 to 2013 was considered for SWAT calibration and validation. According to the hedging rule, a twoobjective function was used to increase the crop yield and reduce the irrigation volume. The optimum results showed by reducing the annual irrigation rate from 200 mm to about 100 mm, the wheat production will be 2. 114 ton/ha which is equal to the current irrigation pattern yield. This approach could maximize the economic cost by introducing the best irrigation pattern and consequently reduce the groundwater recharge and surface run-off 34% and 13%, respectively.

One of sheet severe plastic deformation (SPD) operation, namely constrained groove pressing (CGP), is investigated here in order to specify the optimum values for geometrical variables of this process on pure aluminium sheets. With this regard, two different objective functions, i. e. the uniformity in the effective strain distribution and the necessary force per unit weight of the specimen, are selected to be minimized. To examine the effects of the sheet thickness, die groove angle and the die-tooth number on these objective functions, several finite-element (FE) analyses of the operation are carried out. Using the values of objective functions attained via these numerical simulations, an artificial neural network (ANN) is trained with good regression fitness. Employing a twoobjective genetic algorithm (GA), a series of optimum conditions is obtained as a Pareto front diagram. The best optimum point in this diagram is the closest one to the origin which, at the same time, makes both the objective functions smallest. With this regard, a sheet thickness of 2 mm, a groove angle of 25 and an 8-tooth die are found to be an appropriate optimal condition for performing a CGP process. The finite-element simulation with these enhanced geometrical variables is conducted and the values of the objective functions gained from the numerical analysis is found to be in good agreement with those obtained from the genetic algorithm optimization.

Numerous algorithms have recently been invented with varying strengths and weaknesses, none of which is the best for all cases. Herein, a hybrid optimization method known as a PSOHHO optimization algorithm is presented. There are two methods for combining algorithms: parallel and sequential. We adopted the parallel method and optimized the algorithm's performance. We cover the weaknesses of one algorithm with the strengths of another algorithm using a new method of combination. In this method, using several formulas, the top populations are exchanged between the two algorithms, and a new population is created. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In this method, no changes are made to the algorithms. The main goal is to use existing algorithms. This method aims to attain the optimal solution in the shortest time possible. Two algorithms of particle swarm optimization (PSO) and Harris Hawks optimization (HHO) were used to present this method and five truss samples were considered to confirm the performance of this method. Based on the results, this method has rapid convergence speed and acceptable results compared to the other methods. It also yields better results than its basic algorithms.