Sound Absorption Optimization of Graded Semi-Open Cellular Metals by Adopting the Genetic Algorithm Method

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
H. Meng ◽  
F. X. Xin ◽  
T. J. Lu
Keyword(s):  
Genetic Algorithm ◽  
Sound Absorption ◽  
Geometrical Parameters ◽  
Impedance Model ◽  
Cellular Metal ◽  
Design Variables ◽  
Genetic Algorithm Method ◽  
Pore Opening ◽  
Cellular Metals ◽  
Good Agreement

Built upon the acoustic impedance of circular apertures and cylindrical cavities as well as the principle of electroacoustic analogy, an impedance model is developed to investigate theoretically the sound absorption properties of graded (multilayered) cellular metals having semi-open cells. For validation, the model predictions are compared with existing experimental results, with good agreement achieved. The results show that the distribution of graded geometrical parameters in the semi-open cellular metal, including porosity, pore size, and degree of pore opening (DPO), affects significantly its sound absorbing performance. A strategy by virtue of the genetic algorithm (GA) method is subsequently developed to optimize the sound absorption coefficient of the graded semi-open cellular metal. The objective functions and geometric constraint conditions are given in terms of the key geometrical parameters as design variables. Optimal design is conducted to seek for optimal distribution of the geometrical parameters in graded semi-open cellular metals.

A Combined Genetic Algorithm-Fuzzy Logic Method (GA-FL) to Design a 6 Bars Planar Mechanism

Volume 1 ◽  
2004 ◽  
Author(s):  
M. A. Laribi ◽  
L. Romdhane ◽  
A. Mlika ◽  
S. Zeghloul
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Mechanism Synthesis ◽  
Planar Mechanisms ◽  
Design Variables ◽  
Solution Methods ◽  
Genetic Algorithm Method ◽  
Fuzzy Logic Method ◽  
Logic Method

This work deals with solution methods of optimal synthesis of planar mechanisms. A searching procedure presents a combined genetic algorithm–fuzzy logic method to solve the problem of path generation in mechanism synthesis. Previous works, dealing with the same problem and using the genetic algorithm method, suffered from the lack of precision, especially for large domain problems. The proposed method is made of a classical genetic algorithm coupled with a fuzzy logic controller (GA-FL). This controller monitors the variation of the design variables during the first run of the genetic algorithm and modifies the initial bounding intervals to restart a second round of the genetic algorithm. For both of these runs, we limited the number of generations to roughly half of the number found in the literature, without reducing the accuracy of the final solution. Compared to previous works on the same problem, our method proved to be more efficient in finding the optimal mechanism. The effectiveness of the proposed method has been demonstrated on a six bars synthesis example.

Genetic algorithm application for optimization of geometry of synchronous electric motor with permanent magnets by criterion of electromagnetic torque

2019 ◽  
pp. 56-62
Author(s):  
I. N. Belezyakov ◽  
K. G. Arakancev
Keyword(s):  
Genetic Algorithm ◽  
Electric Motor ◽  
Permanent Magnets ◽  
Evolutionary Optimization ◽  
Electric Machines ◽  
Geometric Parameters ◽  
Geometrical Parameters ◽  
Electromagnetic Torque ◽  
Automatic Control Systems ◽  
Optimization Of Geometry

At present time there is a need to develop a methodology for electric motors design which will ensure the optimality of their geometrical parameters according to one or a set of criterias. With the growth of computer calculating power it becomes possible to develop methods based on numerical methods for electric machines computing. The article describes method of a singlecriterion evolutionary optimization of synchronous electric machines with permanent magnets taking into account the given restrictions on the overall dimensions and characteristics of structural materials. The described approach is based on applying of a genetic algorithm for carrying out evolutionary optimization of geometric parameters of a given configuration of electric motor. Optimization criteria may be different, but in automatic control systems high requirements are imposed to electromagnetic torque electric machine produces. During genetic algorithm work it optimizes given geometric parameters of the electric motor according to the criterion of its torque value, which is being calculated using finite element method.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansur Mohammed Ali Gamel ◽  
Pin Jern Ker ◽  
Hui Jing Lee ◽  
Wan Emilin Suliza Wan Abdul Rashid ◽  
M. A. Hannan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Waste Heat ◽  
Antireflection Coating ◽  
Base Layer ◽  
Average Percentage ◽  
Cell Efficiency ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Dimensional Optimization

AbstractThe optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 3081-3085 ◽  
Author(s):  
Xin Ying Miao ◽  
Jin Kui Chu ◽  
Jing Qiao ◽  
Ling Han Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Field Data ◽  
Back Propagation ◽  
Earth Dam ◽  
Earth Dams ◽  
Gradient Descent Algorithm ◽  
Levenberg Marquardt ◽  
Lm Algorithm ◽  
Good Agreement

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.

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