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 recent decades , the vibration control of the structures subject to some loads , such as winds and earthquakes , etc . , has been widely expressed. Vibration control, which intends to keep the behavior of a structure within a permissible limit , is utilized in several structures , like bridges , tall buildings , wind turbines , foundations with additional vibrations in industry , and even ordinary structures. In this paper , some of the recent studies on optimal control of the structures are reviewed. In other words , a comprehensive review of articles on the optimal design of different known control systems of each category i.e. , Tuned Mass Dampers (Active , Passive , and Semi - active) , Viscoelastic Dampers and Base Isolation (Passive and Semi - active) , Fluid Viscous Dampers (Passive and Semi - active) and the active Tendons , with special attention to studies performed in the past decades, are presented. Also , due to simplicity , reliability , and maximum energy dissipation of the friction dampers as an energy dissipation device , especially for seismic amplification of the engineering structures , a brief overview of the Optimization of the friction dampers is discussed.

This paper investigates the accuracy and convergence rate of different metaheuristic algorithms in determining the stiffness of Structural elements using Structural modal parameters and defining a suitable objective function. To achieve this purpose, three different structures, including a three-story one-dimensional frame, a six-story one-dimensional frame and a two-dimensional truss, were investigated. The metaheuristic algorithms, employed in this study, were Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization. The objective function utilized in this study consists of two terms; the first part involves the squared difference between the first frequency of the structure obtained from the responses of the investigated structure and the first frequency obtained from the hypothetical stiffness matrix in each generation of algorithms. The second part measures the norm of the difference between the first mode shape of the structure obtained from the responses of the investigated structure and the first mode shape obtained from the hypothetical stiffness matrix in each generation of algorithms. By minimizing the objective function, the Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization determined the element stiffness of the three-story, six-story and truss structures, thus demonstrating the high efficiency of metaheuristic algorithms in resolving unknown parameters of structures. The average run time for the Genetic Algorithm was 3.38 seconds, 4.47 seconds, and 15.73 seconds for the three respective problems. For Particle Swarm Optimization, the times were 3.76 seconds, 6.47 seconds, and 16.76 seconds. The Teaching–learning-based Optimization achieved times of 1.92 seconds, 4.51 seconds, and 12.76 seconds. The Teaching–learning-based Optimization exhibited the highest convergence rate and the lowest error compared to the Genetic Algorithm and Particle Swarm Optimization. For example, in the two-dimensional truss, the values of the objective function in the last iteration of the Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization were , and , respectively. The Particle Swarm Optimization demonstrated an acceptable convergence rate and error compared to the Genetic algorithm. The Genetic Algorithm, however, displayed a significant error rate in determining the stiffness of Structural elements compared to the other two algorithms.

The present study is an attempt to propose a mutation-based real-coded genetic algorithm (MBRCGA) for sizing and layout Optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in the design space. The standard deviation of design variables is used as a key factor in the adaptation of mutation operators. The reliability of the proposed algorithm is investigated in typical sizing and layout Optimization problems with both discrete and continuous design variables. The numerical results clearly indicated the competitiveness of MBRCGA in comparison with previously presented methods in the literature.

The optimized shape of trusses are found by presenting a new selection and reproduction operator. The stress, buckling an displacement constraints, consistent with design code, are used. Design variables are discrete size variables (member areas) and continuous shape variables (nodal coardinates). The proposed methods, is fast and has a stable convergence. In addition, it results in an optimized structure with low weight.

In this paper, the effect of angle between predictor and corrector surfaces on the Structural analysis is investigated. Two objective functions are formulated based on this angle and also the load factor. Optimizing these functions, and using the Structural equilibrium path’s geometry, lead to two new constraints for the nonlinear solver. Besides, one more formula is achieved, which was previously found by other researchers, via a different mathematical process. Several benchmark structures, which have geometric nonlinear behavior, are analyzed with the proposed methods. The finite element method is utilized to analyze these problems.The abilities of suggested schemes are evaluated in tracing the complex equilibrium paths.Moreover, comparison study for the required number of increments and iterations is performed. Results reflect the robustness of the authors’ formulations.

New penalty functions, which have better convergence properties, as compared to the commonly used exterior and interior penalty functions, have been proposed in this paper. The convergence behavior and accuracy of ordinary penalty functions depend on the selection of appropriate penalty parameters. The Optimization of ordinary penalty functions is accomplished after several rounds of Optimization where, at each round a different but fixed value of penalty parameter is used. While some useful hints and rules for the selection of suitable penalty parameter values have been provided by different authors, the objective of this paper has been to improve this procedure by including the penalty parameter in the design vector, so that it can be modified during the Optimization, automatically, in order to improve the convergence characteristics. This can also help accomplish Optimization in only one round, which is of considerable importance when it is desired to solve a constrained problem by using genetic algorithms. The proof of convergence to the optimum solution of the proposed functions is also included in the paper. Ten-bar and three-bar truss examples are used for illustration through which the convergence of ordinary and new functions are evaluated and compared. The results show that the new penalty functions can outperform the ordinary functions, especially in combination with genetic algorithms.