Efficient design optimization of nonconventional laminated composites using lamination parameters: A state of the art

2019 ◽  
Vol 209 ◽  
pp. 362-374 ◽  
Author(s):  
Mazen A. Albazzan ◽  
Ramy Harik ◽  
Brian F. Tatting ◽  
Zafer Gürdal
Keyword(s):  
Design Optimization ◽  
State Of The Art ◽  
Laminated Composites ◽  
Efficient Design ◽  
Lamination Parameters

Application of lamination parameters to the reliability-based stiffness design of fibrous laminated composites

1992 ◽  
Author(s):  
MITSUNORI MIKI ◽  
YOSHISADA MUROTSU ◽  
NOBUHIKO MURAYAMA ◽  
TETSUO TANAKA
Keyword(s):  
Laminated Composites ◽  
Lamination Parameters ◽  
Stiffness Design

On design optimization for structural crashworthiness and its state of the art

2016 ◽  
Vol 55 (3) ◽  
pp. 1091-1119 ◽  
Author(s):  
Jianguang Fang ◽  
Guangyong Sun ◽  
Na Qiu ◽  
Nam H. Kim ◽  
Qing Li
Keyword(s):  
Design Optimization ◽  
State Of The Art

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

scholarly journals Advanced Kriging response surface approach for efficient design optimization

2014 ◽  
Vol 80 (818) ◽  
pp. TRANS0285-TRANS0285
Author(s):  
Wataru YAMAZAKI
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Efficient Design ◽  
Surface Approach

scholarly journals Laminated Composites Buckling Analysis Using Lamination Parameters, Neural Networks and Support Vector Regression

2015 ◽  
Vol 12 (2) ◽  
pp. 271-294 ◽  
Author(s):  
Rubem M. Koide ◽  
Ana Paula C. S. Ferreira ◽  
Marco A. Luersen
Keyword(s):  
Neural Networks ◽  
Support Vector Regression ◽  
Laminated Composites ◽  
Buckling Analysis ◽  
Support Vector ◽  
Lamination Parameters

scholarly journals Comparing the State-of-the-Art Efficient Stated Choice Designs Based on Empirical Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Li Tang ◽  
Xia Luo ◽  
Yang Cheng ◽  
Fei Yang ◽  
Bin Ran
Keyword(s):  
Discrete Choice ◽  
State Of The Art ◽  
Choice Model ◽  
Orthogonal Design ◽  
Asymptotic Variance ◽  
Parameter Estimates ◽  
Efficient Design ◽  
Stated Choice ◽  
Reliable Parameter ◽  
Efficient Data

The stated choice (SC) experiment has been generally regarded as an effective method for behavior analysis. Among all the SC experimental design methods, the orthogonal design has been most widely used since it is easy to understand and construct. However, in recent years, a stream of research has put emphasis on the so-called efficient experimental designs rather than keeping the orthogonality of the experiment, as the former is capable of producing more efficient data in the sense that more reliable parameter estimates can be achieved with an equal or lower sample size. This paper provides two state-of-the-art methods called optimal orthogonal choice (OOC) andD-efficient design. More statistically efficient data is expected to be obtained by either maximizing attribute level differences, or minimizing theD-error, a statistic corresponding to the asymptotic variance-covariance (AVC) matrix of the discrete choice model, when using these two methods, respectively. Since comparison and validation in the field of these methods are rarely seen, an empirical study is presented.D-error is chosen as the measure of efficiency. The result shows that both OOC andD-efficient design are more efficient. At last, strength and weakness of orthogonal, OOC, andD-efficient design are summarized.

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