Genetic Algorithm Based Design Optimization Research Articles

Genetic algorithm based design optimization of crashworthy honeycomb sandwiched panels of AA7075-T651 aluminium alloy for aerospace applications

Crashworthiness studies, for decades have been an area of tremendous interest in aerospace and automotive sectors, as ensuring the safety of the occupants in the vehicle is a primary goal. Honeycomb sandwich panels are generally used for building floor, ceiling and other important fuselage parts in a commercial airliner to make the structures shock-resistant. In this work, optimization of the design of crashworthy sandwiched honeycomb structure was carried out using a non-dominated sorting genetic algorithm (NSGA) . The stated algorithm had actively monitored the variation of the geometric parameters. To study the shape effect of the cells which absorb impact energy, three cellular shapes namely hex-grid, ortho-grid and iso-grid structures were considered for analyzing their deformation behavior under impact loading. A multi objective function in the genetic algorithm was implemented in MATLAB, where the objectives were to minimize weight and to maximize the absorbed impact energy while constraining the maximum transmitted force and displacement due to crushing. Sandwiched aluminum (AA7075-T651) honeycomb structures were modeled in ABAQUS using python scripts , with the number of core cells and the wall thickness of each core cell being optimized through genetic algorithm in MATLAB using an interactive mechanism. Significant improvements were observed in terms of reduction of weight and increase in the ability to absorb the specific energy during crushing in the sandwiched panels after 450 iterations of optimization cycles. A novel methodology in the domain of crash analysis of aerostructures was established with design optimization of honeycomb sandwiched panels.

Optimization of Specific Power of Surface Mounted Axial Flux Permanent Magnet Brushless DC Motor for Electrical Vehicle Application

Optimization of specific power of axial flux permanent magnet brushless DC (PMBLDC) motor based on genetic algorithm optimization technique for an electric vehicle application is presented. Double rotor sandwiched stator topology of axial flux permanent magnet brushless DC motor is selected considering its best suitability in electric vehicle applications. Rating of electric motor is determined based on vehicular dynamics and application needs. Double rotor sandwiched stator axial flux PMBLDC motor is designed considering various assumed design variables. Initially designed axial flux PMBLDC motor is considered as a reference motor for further analysis. Optimization of the specific power of electric motor for electric vehicle applications is a very important design issue. The Genetic Algorithm (GA) based optimization technique is proposed for optimization of specific power of axial flux permanent magnet brushless DC motor. Optimization with an objective of maximum specific power with the same torque rating is performed. Three-dimensional finite element analysis is performed to validate the proposed GA based specific power optimization. Close agreement between results obtained from finite element analysis and analytical design establishes the correctness of the proposed optimization technique. The performance of the improved motor is compared with the initially designed reference motor. It is analyzed that the specific power of axial flux PMBLDC motor is enhanced effectively with the application of GA based design optimization technique.

Genetic Algorithm Based Design Optimization of a Passive Anti-Roll Tank in a Sea Going Vessel

This paper presents the development of a numerical optimization tool for the design of a passive U-tube type anti-roll tank (ART) system to mitigate the roll motions of a vessel. A Genetic Algorithm (GA) based optimization scheme has been developed to minimize the objective function, which in the present investigation is chosen to be the area under the roll response transfer function curve. A practical nonlinear time-domain based body-exact strip theory technique is used to solve the fully coupled ART–ship motion problem. In the optimization process, the GA is linked to the fluid solver to approach an optimum ART design. The optimization variables are chosen to be the principal dimensions of the anti-roll tank, which have bearing on the roll reduction characteristics of the vessel. The study is implemented in the case of a coastal research vessel requiring passive stabilization via an ART. The results are validated using laboratory scale models to quantify the tank dynamics and effectiveness in isolation as well as when deployed in the vessel model and subjected to waves. The results demonstrate the validity and efficiency of the entire computational scheme.

Design Optimization of Axial Flux Surface Mounted Permanent Magnet Brushless DC Motor For Electrical Vehicle Based on Genetic Algorithm

This paper presents the design optimization of axial flux surface mounted Permanent Magnet Brushless DC motor based on genetic algorithm for an electrical vehicle application. The rating of the motor calculated form vehicle dynamics is 250 W, 150 rpm. The axial flux surface mounted Permanent Magnet Brushless DC (PMBLDC) motor was designed to fit in the rim of the wheel. There are several design variables e.g. air gap flux density, slot loading, magnet spacer width, ratio of outer to inner diameter, air gap length, current density and space factor). The main contribution in the present work is to propose the best combination of design variables obtained using genetic algorithm (GA) optimization technique and design of motor based on optimized design variables. Final validation is carried out with the help of 3-D finite element analysis (FEA) for GA based constraint and unconstraint design. The entire procedure based on GA is explained with the help of block diagram. Efficiency of the axial flux surface mounted PMBLDC motor is enhanced from 88.15 to 91.5 % using GA based design optimization. Proposed optimization technique and methodology will be useful for performance improvement of any nonlinear engineering design involving various design variables for specific application.

Genetic Algorithm Based Design Optimization of a Six Phase Induction Motor

An optimally designed six-phase induction motor (6PIM) is compared with an initial design induction motor having the same ratings. The Genetic Algorithm (GA) method is used for optimization and multi objective function is considered. Comparison of the optimum design with the initial design reveals that better performance can be obtained by a simple optimization method. Also in this paper each design of 6PIM, is simulated by MAXWELL_2D. The obtained simulation results are compared in order to find the most suitable solution for the specified application, considering the influence of each design upon the motor performance. Construction a 6PIM based on the information obtained from GA method has been done. Quality parameters of the designed motors, such as: efficiency, power losses and power factor measured and optimal design has been evaluated. Laboratory tests have proven the correctness of optimal design.

Constrained minimization utilizing GA based pattern recognition of immune system

The immune system has pattern recognition capabilities based on reinforced learning, memory and affinity maturation interacting between antigens and antibodies. The paper deals with the adaptation of artificial immune system into genetic algorithm based design optimization. The present study utilizes the pattern recognition from the immune system and the evolution from genetic algorithm. The basic idea is derived from the fact that designs should be distinguished whether they are usable/feasible or infeasible and should be improved towards the optimal solution. For the expression of design solutions, binary coded strings are used to represent antigens and antibodies in artificial immune system and chromosomes in genetic algorithm. The paper discusses the procedure of constrained optimization that does not rely on any detailed mathematical formulation for constraint handling. A number of mathematical function minimization problems are examined for verification, and practical engineering optimization problems including inequality constraints are explored to support the proposed strategy.

Genetic algorithm based design optimization of a permanent magnet brushless dc motor

Genetic algorithm (GA) based design optimization of a permanent magnet brushless dc motor is presented in this paper. A 70 W, 350 rpm, ceiling fan motor with radial-filed configuration is designed by considering the efficiency as the objective function. Temperature-rise and motor weight are the constraints and the slot electric loading, magnet-fraction, slot-fraction, airgap, and airgap flux density are the design variables. The efficiency and the phase-inductance of the motor designed using the developed CAD program are improved by using the GA based optimization technique; from 84.75% and 5.55 mH to 86.06% and 2.4 mH, respectively.

Genetic Algorithm Based Resistive Susceptor Design for Uniform Heating During the Induction Bonding Process

In this research work, cut mesh design optimization for the induction bonding process using genetic algorithms (GAs) is investigated to solve the problem of nonuniform heating, which leads to nonuniform temperature fields and temperature gradients exceeding the process window required for bonding. Cut patterns in the metal mesh can redirect the magnetically induced electric currents generated thus changing the temperature distribution. In this work, the heat generation model for determining current and heat generation distribution for a given coil and mesh size, coded as a Mathematica function, was coupled with a simple genetic algorithm. The cost function to be minimized by the GA was the ratio of the maximum heat generation in the mesh to the minimum heat generation. Two studies were performed with the GA-based design optimization: the first with a six sided square mesh and the second using a ten sided square mesh. The best cut mesh designs obtained from the GA were compared with the globally optimal designs, where available, and with the baseline mesh. The GA could not reach the global optima due to the complex nature of the design search space. However, it was determined that the GA was able to reduce the variations in heat generation in the mesh for all cases and delivered significant improvements over the baseline case in reasonable computational time, evaluating less than 2% of the possible cut mesh patterns. Thus the genetic algorithm based design optimization was proven to be a computationally efficient tool in the generation of good cut mesh designs for the induction bonding process.