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.

The optimal Design of structural systems represents a pivotal challenge, striking a balance between economic efficiency and safety. There has been a great challenge in balancing between the economic issues and safety factors of the structures over the past few decades; however, development of high-speed computing systems enables the experts to deal with higher computational efforts in Designing structural systems. Recent advancements in computational methods have significantly improved our ability to address this challenge through sophisticated Design schemes. The main purpose of this paper is to develop an intelligent Design scheme for truss structures in which an Optimization process is implemented into this scheme to help the process reach lower weights for the structures. For this purpose, the Artificial Rabbits Optimization (ARO) algorithm is utilized as one of the recently developed metaheuristic algorithms which mimics the foraging behaviour of the rabbits in nature. In order to reach better solutions, the improved version of this algorithm is proposed as I-ARO in which the well-known random initialization process is substituted by the Diagonal Linear Uniform (DLU) initialization procedure. For numerical investigations, 5 truss structures 10, 25, 52, 72, and 160 elements are considered in which stress and displacement constraints are determined by considering discrete Design variables. By conducting 50 Optimization runs for each truss structure, it can be concluded that the I-ARO algorithm is capable of reaching better solutions than the standard ARO algorithm which demonstrates the effects of DLU in enhancing this algorithm’s search behaviour.

A numerical Optimization is undertaken for preliminary aerodynamic Design of axial compressors. Initially, Design process is performed based on calculations along mean line. Then, results of geometric parameters and gas dynamic characteristics are extended to different radial positions distributed from blade root to its tip using well-known radial equilibrium law. In the process of Optimization, pressure ratio of each stage of multistage compressor has been taken as Design variables. Constraints consist of well known De-Haller number of each stage as a criterion for flow failure. Main input data consist of overall pressure ratio, mass flow rate and rotational speed. Compressor efficiency is treated as the objective function which is a nonlinear function of the variables. The main objective of present research work is to optimize initial pressure ratio distribution in order to maximize the overall isentropic efficiency. The Optimization approach is based on the use of Reduced Gradient Projection Method. The proposed method was tested on a known example of a compressor Design from the literature, and is shown to enhance the efficiency with good agreement with the published results.

In recent years, the use of air taxis as a suitable solution for transporting cargo and passengers, has been considered especially in short distances and in the city. Complex systems, such as air taxis, are involved in several subsystems with interacting and sometimes conflicting effects are difficult to be derived. Modern optimal Design methods such as multidisciplinary Design Optimization can derive the optimal Design while satisfying all the constraints and limitation. In this article, multidisciplinary Design Optimization of an air taxi is discussed. The Optimization framework is selected based on AAO by considering structure, aerodynamics, flight mechanics, propulsion and electrical power. Total mass of air taxi is selected as cost function. Finally, the optimal results are compared and evaluated with the results of two classical Design methods including "weight estimation" and " sensitivity of Design coefficients". The results confirm the improvement of optimal solution with compare of classical methods.

In this paper a novel non-parametric approach for Designing geodetic networks based on  evolutionary algorithms is proposed. Since conventional analytical Optimization methods are based on gradient approaches, they have some drawbacks including approximate parameters requirement and inability to work with discrete objective functions and/or constraints e.g. visibility matter. To prevent from above problems, the standard genetic algorithm method is utilized. Primary tests demonstrate the capability of proposed method to Design the optimal network without approximate values and with visibility constraint satisfaction.