Minimizing Stress Concentrations in Laminated Composites by Genetic Algorithm

Materials ◽  
2005 ◽  
Author(s):  
H. K. Cho ◽  
R. E. Rowlands
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Laminated Composite ◽  
Layered Composite ◽  
Local Optimum ◽  
Optimization Approach ◽  
Stress Concentrations ◽  
Design Variables ◽  
As Stress ◽  
Stress Variations

Design optimizations using a genetic algorithm (GA) are well suited for problems having many design variables and local optimum design points. Concomitant with recent manufacturing advances, the concept is utilized here to minimize the tensile stress concentration in a perforated laminated composite by orientating the fibers locally both within the plies and from ply-to-ply. The current optimization approach is advantageously conducted in conjunction with FEA. The geometry is discretized into general 3D solid 20-node isoparametric layered composite elements of our own design. Solid, rather than plate, elements enable one to reliably account for features such as stress variations within and between individual plies. A parallel computing scheme is implemented between the FEA and GA optimization. Design optimization variables are local fiber directions within discrete finite elements and within respective plies of the laminate. Since fiber orientations are optimized locally within individual plies, the technique provides more than just a favorable stacking sequence of various rectilinearly orthotropic plies having different fiber orientations.

Structural Optimization of Fir-Tree Root and Groove for Turbine Blade With Splines and Genetic Algorithm

2016 ◽  
Author(s):  
Deqi Yu ◽  
Fengwei Li ◽  
Jiandao Yang ◽  
Kai Cheng ◽  
Weilin Shu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Technique ◽  
Peak Stress ◽  
Optimization Approach ◽  
Stress Concentrations ◽  
Spline Curves ◽  
Design Variables ◽  
Straight Line ◽  
Feature Based ◽  
Profile Optimization

The wide use of fir-tree root and groove in turbine structures prompts the expectation to find optimum configurations, which ensure that stresses are in the safe limits to avoid mechanical failure. To perform the optimization, the reasonable characterization of root configuration is required. The existing researches characterized the fir-tree root with straight line, arc or even elliptic fillet, then the parameters of these features were defined as design variables to perform root profile optimization. However, this feature-based optimization technique yields configuration which is only optimal under the feature assumption, the question why choose these feature and whether there is a better feature modeling technique is difficult to answer. In this work, instead of the feature-based method, spline curves technique is involved to characterize the root and groove configuration, and the horizontal coordinates of the control points are selected as design variables, which are modified in the vicinity of their initial values during optimization process. The objective function is to minimize the peak stress in the root and groove regions. With the Multi-island genetic algorithm, the optimal fir-tree root configuration can be obtained with better stress distributions and low stress concentrations. The proposed spline-based optimization approach may shed lights on the conceptual design of blade root and can be easily extended to other industrial equipment design.

scholarly journals A Comprehensive Optimization Design Method of Aerodynamic, Acoustic, and Stealth of Helicopter Rotor Blades Based on Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingxu Yi ◽  
Yalin Pan ◽  
Jun Huang ◽  
Lifeng Wang ◽  
Dawei Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Integrated Design ◽  
Optimization Method ◽  
Rotor Blades ◽  
Helicopter Rotor ◽  
Optimization Approach ◽  
Design Variables ◽  
Helicopter Rotor Blades

In this paper, a comprehensive optimization approach is presented to analyze the aerodynamic, acoustic, and stealth characteristics of helicopter rotor blades in hover flight based on the genetic algorithm (GA). The aerodynamic characteristics are simulated by the blade element momentum theory. And the acoustics are computed by the Farassat theory. The stealth performances are calculated through the combination of physical optics (PO) and equivalent currents (MEC). Furthermore, an advanced geometry representation algorithm which applies the class function/shape function transformation (CST) is introduced to generate the airfoil coordinates. This method is utilized to discuss the airfoil shape in terms of server design variables. The aerodynamic, acoustic, and stealth integrated design aims to achieve the minimum radar cross section (RCS) under the constraint of aerodynamic and acoustic requirement through the adjustment of airfoil shape design variables. Two types of rotor are used to illustrate the optimization method. The results obtained in this work show that the proposed technique is effective and acceptable.

Reliability-based design optimization of electromagnetic shielding structure using neural networks and real-coded genetic algorithm

2014 ◽  
Vol 228 (18) ◽  
pp. 3471-3481 ◽  
Author(s):  
Heeralal Gargama ◽  
Sanjay K Chaturvedi ◽  
Awalendra K Thakur
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Shielding ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Real Coded Genetic Algorithm

The conventional approaches for electromagnetic shielding structures’ design, lack the incorporation of uncertainty in the design variables/parameters. In this paper, a reliability-based design optimization approach for designing electromagnetic shielding structure is proposed. The uncertainties/variability in the design variables/parameters are dealt with using the probabilistic sufficiency factor, which is a factor of safety relative to a target probability of failure. Estimation of probabilistic sufficiency factor requires performance function evaluation at every design point, which is extremely computationally intensive. The computational burden is reduced greatly by evaluating design responses only at the selected design points from the whole design space and employing artificial neural networks to approximate probabilistic sufficiency factor as a function of design variables. Subsequently, the trained artificial neural networks are used for the probabilistic sufficiency factor evaluation in the reliability-based design optimization, where optimization part is processed with the real-coded genetic algorithm. The proposed reliability-based design optimization approach is applied to design a three-layered shielding structure for a shielding effectiveness requirement of ∼40 dB, used in many industrial/commercial applications, and for ∼80 dB used in the military applications.

The Influence of Estimated Pollution Range on the Groundwater Pollution Source Identification Method Based on the Simple Genetic Algorithm

2014 ◽  
Vol 587-589 ◽  
pp. 836-841 ◽  
Author(s):  
Yu Qiao Long ◽  
Chun Yong Wu ◽  
Jian Ping Wang
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Pollution ◽  
Optimization Model ◽  
Solution Method ◽  
Pollution Source ◽  
Local Optimum ◽  
Optimization Approach ◽  
Optimum Location ◽  
Migration Direction ◽  
Slight Movement

The optimization approach is common approach to solve complex PSI issues. Most researches on the optimization approach focus on the solution method of the optimization model and improving modeling efficiency. In this paper, we give our effort on the influence of estimated pollution range on the groundwater PSI problem and discuss the efficiency and accuracy of 1D and 2D PSI problems. The estimated pollution range of PSI problem could affect how much calculated time would be consumed. The bigger the estimated range is, the more time is consumed. Increasing the dimension of the PSI problem will increase the estimated range greatly, and leads to a great time consuming. A slight movement of the estimated source in the direction perpendicular to the major migrate direction leads to big bias between the calculated source location and the real location. The chance that optimization model falls into the local optimum location is growing in the major migration direction.

Genetic algorithm-based optimization design method of the Formula SAE racing car’s rear wing

2017 ◽  
Vol 232 (7) ◽  
pp. 1255-1269 ◽  
Author(s):  
Hui Wang ◽  
Qiuyang Bai ◽  
Xufei Hao ◽  
Lin Hua ◽  
Zhenghua Meng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Formula Sae ◽  
Design Variables ◽  
Rear Wing ◽  
Sample Points ◽  
Lift And Drag

The aerodynamic devices play an important role on the performance of the Formula SAE racing car. The rear wing is the most significant and popular element, which offers primary down force and optimizes the wake. In traditional rear wing optimization, the optimization variables are first selected, and separately enumerated according to the analyzing experience of the racing car’s external flow field, and thus the optimal design is chosen by comparison. This method is complicated, and even might lose some key sample points. In this paper, the attack angle of the rear wing and the relative position parameters are set as design variables; then the design variables’ combination is determined by the DOE experimental design method. The aerodynamic lift and drag of the racing car for these variables’ combinations are obtained by the computational fluid dynamics method. With these sample points, the approximation model is produced by the response surface method. For the sake of gaining the best lift to drag ( FL/ FD) ratio, i.e. maximum down force and the minimum drag force, the optimal solution is found by the genetic algorithm. The result shows that the established optimization procedure can optimize the rear wing’s aerodynamic characteristic on the racing car effectively and have application values in the practical engineering.

Application of Dual-Adaptive Niched Genetic Algorithm in Optimal Design of Nuclear Power Components

2014 ◽  
Author(s):  
Cheng Wang ◽  
Chang-qi Yan ◽  
Jian-jun Wang ◽  
Lei Chen ◽  
Gui-jing Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Nuclear Power ◽  
Optimization Design ◽  
Population Diversity ◽  
Geometric Constraints ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Benchmark Function ◽  
Nonlinear Constraint

Genetic algorithm (GA) has been widely applied in optimal design of nuclear power components. Simple genetic algorithm (SGA) has the defects of poor convergence accuracy and easily falling into the local optimum when dealing with nonlinear constraint optimization problem. To overcome these defects, an improved genetic algorithm named dual-adaptive niched genetic algorithm (DANGA) is designed in this work. The new algorithm adopts niche technique to enhance global search ability, which utilizes a sharing function to maintain population diversity. Dual-adaptation technique is developed to improve the global and local search capability at the same time. Furthermore, a new reconstitution operator is applied to the DANGA to handle the constraint conditions, which can avoid the difficulty of selecting punishment parameter when using the penalty function method. The performance of new algorithm is evaluated by optimizing the benchmark function. The volume optimization of the Qinshan I steam generator and the weight optimization of Qinshan I condenser, taking thermal-hydraulic and geometric constraints into consideration, is carried out by adopting the DANGA. The result of benchmark function test shows that the new algorithm is more effective than some traditional genetic algorithms. The optimization design shows obvious validity and can provide guidance for real engineering design.

Optimization Design of the Geared Rotor System With Critical Speed Constraints Using the Enhanced Genetic Algorithm

2008 ◽  
Author(s):  
T. N. Shiau ◽  
C. H. Kang ◽  
D. S. Liu ◽  
E. K. Lee ◽  
W. C. Hsu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Critical Speed ◽  
Hybrid Genetic Algorithm ◽  
Transmission Error ◽  
Rotor System ◽  
Gear Mesh ◽  
Design Variables ◽  
Bearing Stiffness ◽  
Gear Mesh Stiffness

This paper presents an efficient enhanced genetic algorithm to minimize the shaft weight, the unbalance response and the response due to the transmission error simultaneously. The minimization plays an important role in designing the geared rotor system under critical speed constraints. In the process of optimization, the design variables consist of shaft inner radii, bearing stiffness and the gear mesh stiffness. The enhanced genetic algorithm of optimization comprises the Hybrid Genetic Algorithm (HGA) and the Interval Genetic Algorithm (IGA). The HGA deals with this optimal design problem and the IGA accomplishes the interval optimization design. The results show that the presented enhanced genetic algorithm can not only effectively reduce the shaft weight and the transmission error response, but also precisely determine the interval ranges of design variables with feasible corresponding objective error.

The Application of Genetic Algorithm in Valve Spring Optimization Design of Internal Combustion Engine

2015 ◽  
Vol 741 ◽  
pp. 810-813
Author(s):  
Shao Ni Wang
Keyword(s):  
Genetic Algorithm ◽  
Internal Combustion Engine ◽  
Optimization Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Technical Problems ◽  
Intelligent Computing ◽  
Application Range ◽  
Valve Spring ◽  
Design Variables

This paper described examples of genetic algorithm in valve spring optimization design of internal combustion engine, which solve the optimal values of the design variables by constructing multi-objective function. The technical problems that complex performance can't be considered in the calculation were solved in designing the spring, the method has many advantages such as simple universal, strong robustness, and wide application range; it is an essential and critical intelligent computing technology in the future mechanical optimization design.

Genetic Algorithm Approach for Optimization of FRP Laminated Cylindrical Shells Under Constraint

1999 ◽  
Author(s):  
Yoshiki Ohta
Keyword(s):  
Genetic Algorithm ◽  
Cylindrical Shells ◽  
Laminated Composite ◽  
Frequency Equation ◽  
Genetic Operators ◽  
Fundamental Frequencies ◽  
Design Variables ◽  
Structural Applications ◽  
Genetic Algorithm Approach ◽  
Laminated Cylindrical Shells

Abstract Fiber Reinforced Plastic (FRP) materials have been increasingly used in many structural applications of space shuttles, airplanes and automobiles, and the structural optimization of FRP laminated composite shells has been studied for stiffer structural design by many researchers. This paper studies the maximization of fundamental frequencies of FRP laminated cylindrical shells under stiffness constraint by using Genetic Algorithm (GA). For this purpose, the frequency equation for simply-supported shells with symmetrically balanced stacking sequence is derived analytically based on Classical Lamination Theory. In optimization the fiber angles and the thickness ratio of each FRP ply, which have continuous real values, are taken as design variables, and fundamental frequency of the shell is maximized under in-plane stiffness constraints. In numerical experiments, extensive numerical calculations are carried out to determine better genetic operators that would be suitable for FRP laminates design, and genetic parameters are tuned for better reliabilities and lower computational costs in the present GA. Optimal design solutions for various laminated cylindrical shells are obtained and then the applicability of the GA to the maximization of frequencies of the shells is studied from numerical results obtained.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

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