Blending design of composite panels with lamination parameters

2016 ◽  
Vol 120 (1233) ◽  
pp. 1710-1725 ◽  
Author(s):  
P. Jin ◽  
X. Zhong ◽  
J. Yang ◽  
Z. Sun
Keyword(s):  
Genetic Algorithm ◽  
Least Squares ◽  
Numerical Results ◽  
Manufacturing Constraints ◽  
Stacking Sequence ◽  
Lamination Parameters ◽  
Least Squares Fitting ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Present Algorithm

ABSTRACTIn this paper, a new optimisation method incorporating lamination parameters and a guide-based blending model is proposed. Lamination parameters for a guide laminate and ply number of each panel are employed as design variables for optimisation with a parallel real-coded genetic algorithm incorporating structure behaviour and manufacturing constraints. During the optimisation process, with a form of least squares fitting adopted, another genetic algorithm is used to obtain the guide stacking sequence of the guide laminate from the guide lamination parameters, and then the laminate configurations of each panel are obtained from the guide stacking sequence and number of plies for each panel. The proposed framework is demonstrated via design of an 18-panel horseshoe configuration, where each panel is optimised individually with a buckling constraint. Numerical results indicate that the present algorithm is capable of obtaining fully blended designs.

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.

Structural Optimization of Composite Panel with Lamination Parameters and Equivalent Stiffness Laminate Method

2014 ◽  
Vol 496-500 ◽  
pp. 429-435
Author(s):  
Xiao Ping Zhong ◽  
Peng Jin
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Composite Structure ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Composite Panel ◽  
Analysis Tool ◽  
Equivalent Stiffness ◽  
Lamination Parameters ◽  
Design Variables

Firstly, a two-level optimization procedure for composite structure is investigated with lamination parameters as design variables and MSC.Nastran as analysis tool. The details using lamination parameters as MSC.Nastran input parameters are presented. Secondly, with a proper equivalent stiffness laminate built to substitute for the lamination parameters, a two-level optimization method based on the equivalent stiffness laminate is proposed. Compared with the lamination parameters-based method, the layer thicknesses of the equivalent stiffness laminate are adopted as continuous design variables at the first level. The corresponding lamination parameters are calculated from the optimal layer thicknesses. At the second level, genetic algorithm (GA) is applied to identify an optimal laminate configuration to target the lamination parameters obtained. The numerical example shows that the proposed method without considering constraints of lamination parameters can obtain better optimal results.

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.

Optimization Methodology for Composite Stiffened Panel Based on Genetic Algorithm

2014 ◽  
Vol 952 ◽  
pp. 34-37
Author(s):  
Da Feng Jin ◽  
Zhe Liu ◽  
Zhi Rui Fan
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Design Space ◽  
Design Guidelines ◽  
Stiffened Panel ◽  
Stacking Sequence ◽  
Design Variables ◽  
Composite Stiffened Panel ◽  
Optimization Methodology

A novel optimization methodology for stiffened panel is proposed in this paper. The purpose of the optimization methodology is to improve the first buckling load of the panel which is obtained by finite element method. The stacking sequence of the stiffeners is taken as design variables. In order to ensure the manufacturability of design, the design guidelines of stacking sequence are taken into account. A DOE based on Halton Sequence makes the initial points of genetic algorithm spread more evenly in the design space of laminate parameters and consequently accelerates the search to convergence. The numerical example verifies the efficiency of this method.

scholarly journals Multi-Dimensional Optimization of In0.53Ga0.47As Thermophotovoltaic Cell using Real Coded Genetic Algorithm

2020 ◽  
Author(s):  
Mansur 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

Abstract The 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) under 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 K 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.

COUPLED ALGORITHMS FOR PIEZOELECTRIC ACTUATOR DESIGN OPTIMIZATION FOR SHAPE CONTROL OF SMART STRUCTURES

2009 ◽  
Vol 06 (04) ◽  
pp. 501-519 ◽  
Author(s):  
QUAN NGUYEN ◽  
LIYONG TONG
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Piezoelectric Actuator ◽  
Shape Control ◽  
Convergence Condition ◽  
Numerical Results ◽  
Least Square ◽  
Design Variables ◽  
Linear Least Square ◽  
Actuator Design

This paper presents two coupled algorithms for piezoelectric actuator design optimization for shape control of structures with both applied voltages and the shapes of actuators being treated as design variables. The optimum values for the applied voltages to actuators can be determined using the Linear Least Square (LLS) method, whereas the shapes of actuators can be optimized using the Genetic Algorithm (GA). These algorithms are combined together to develop a GA+LLS coupled algorithm, for design optimization of both actuators' shapes and voltages in either an alternating or a concurrent manner. In the alternating approach, LLS is utilized to determine the optimum voltages with given actuator geometry, and then GA is used to determine the optimal actuator shapes with given voltages; the alternating calculations continue until the selected convergence condition is met. In the concurrent approach, the LLS is embedded in GA to determine optimum voltages and then to modify the associated strings for each individual population. Numerical results are presented to validate the proposed algorithms. It is found that the concurrent GALLS algorithm appears to be most efficient and effective.

Development of Multi-Stage and Hybrid Real-Coded Genetic Algorithms With Active Constraints

2002 ◽  
Author(s):  
Yasunari Mimura ◽  
Shinobu Yoshimura ◽  
Tomoyuki Hiroyasu ◽  
Mitsunori Miki
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Initial Point ◽  
Global Optimum ◽  
Feasible Region ◽  
Active Constraints ◽  
Multi Stage ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Optimum Solution

In this study, we propose multi-stage and hybrid real-coded genetic algorithm. In the proposed algorithm, there are two stages. In the first stage, Real-coded Genetic Algorithm with Active Constraints (RGAAC) is applied to find a solution that is close to the global optimum. In RGAAC, indviduals who are out of the feasible region are pulled back into feasible region. Therefore, the effective search can be carried out even in the constraints problems. In the second stage, Feasible Region Limiting Method (FRLM) is applied to obtain an optimum solution. FRLM uses the solution that is derived in the first stager as an initial point. In this study, RGAAC is applied to solve the truss structure problems. Through these problems, the effectiveness and the searching mechanism of RGAAC is discussed. The, the proposed algorithm is also applied to 2D problem. In this problem, there are about 1000 design variables. The proposed method can derive the reasonable solution. From these results, it is concluded that the proposed method is effective to solve optimzation problems of large scale structures.

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