scholarly journals A State Space Method for Surface Instability of Elastic Layers With Material Properties Varying in Thickness Direction

2014 ◽  
Vol 81 (8) ◽  
Author(s):  
Zhigen Wu ◽  
Jixiang Meng ◽  
Yihua Liu ◽  
Hao Li ◽  
Rui Huang

A state space method is proposed for analyzing surface instability of elastic layers with elastic properties varying in the thickness direction. By assuming linear elasticity with nonlinear kinematics, the governing equations for the incremental stress field from a fundamental state are derived for arbitrarily graded elastic layers subject to plane-strain compression, which lead to an eigenvalue problem. By discretizing the elastic properties into piecewise constant functions with homogeneous sublayers, a state space method is developed to solve the eigenvalue problem and predict the critical condition for onset of surface instability. Results are presented for homogeneous layers, bilayers, and continuously graded elastic layers. The state space solutions for elastic bilayers are in close agreement with the analytical solution for thin film wrinkling within the limit of linear elasticity. Numerical solutions for continuously graded elastic layers are compared to finite element results in a previous study (Lee et al., 2008, J. Mech. Phys. Solids, 56, pp. 858–868). As a semi-analytical approach, the state space method is computationally efficient for graded elastic layers, especially for laminated multilayers.

1979 ◽  
Vol 101 (2) ◽  
pp. 309-314 ◽  
Author(s):  
M. H. Hsiao ◽  
E. J. Haug ◽  
J. S. Arora

A state space method of optimal design of dynamic systems subjected to transient loads is developed and applied. In contrast to the conventional nonlinear programming approach of discretizing the time interval and constructing a high dimension nonlinear programming problem, a state space approach is employed which develops the sensitivity analysis and optimization algorithm in continuous state space, resorting to discretization only for efficient numerical integration of differential equations. A numerical comparison of the state space and conventional nonlinear programming methods is carried out for two test problems, in which the state space method requires only one-tenth the computing time reported for the nonlinear programming approach.


2020 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
FITRI ANANDA DITA SARASWITA ◽  
I WAYAN SUMARJAYA ◽  
LUH PUTU IDA HARINI

State space is an approach to model and predict together several time series data that are interconnected, and these variables have dynamic interactions. The purpose of this research is to model the number of train passengers in Java and find out the forecasting results using the state space method. The algorithm used to solve the state space model is the Kalman filter. In this research, a suitable final model is local level model with seasonal and produces MAPE value of 2%, this shows that the state space method is very accurately.


Author(s):  
Boris Lobasenko ◽  
Dmitry Borodulin ◽  
Roman Kotlyarov ◽  
Yana Golovacheva ◽  
Igor Bakin

2005 ◽  
Vol 53 (1) ◽  
pp. 145-148 ◽  
Author(s):  
Daniel Alpay ◽  
Israel Gohberg

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