30964116
Numerical probabilistic approach to sensitivity analysis in a fatigue delamination problem of a two layer composite
Fatigue delamination is the primary mode of failure of composite laminates subjected to cyclic loads. Several parameters can affect the fatigue delamination growth in laminates – that includes e.g. constituent material properties and/or composite shape. Knowledge about effects of those parameters can lead to a better understanding of the fatigue delamination behavior and can also pinpoint directions for optimum composite design. Those effects can be elucidated by undertaking appropriate sensitivity analysis. Hence, a probabilistic approach to sensitivity analysis is proposed in this work to study the effects of composite parameters in a fatigue delamination problem of a two-layer composite. This approach combines together a fatigue delamination model, Monte Carlo simulations and the finite element method (FEM). As a result, probabilistic sensitivities are obtained in the form of correlation coefficients and also as trend-line gradients. Accuracy and efficacy of the approach is evaluated based on two numerical examples. The current approach is found to be insensitive to the parameter standard deviation and number of sampling points. Moreover, the current approach can be used irrespectively from the type of the boundary value problem (linear or non-linear) provided that the problem is solvable by the numerical method such as the FEM.
39670549
Some Notes on Numerical Convergence of the Stochastic Perturbation Method
This paper concerns numerical convergence studies of the general nth order stochastic perturbation technique applied frequently to randomize some boundary value of boundary initial problems in computational mathematics as well as to additionally modify numerical methods in engineering. The general scheme of the technique is introduced and invented in terms of the coupled viscous fluid dynamics problem. Symbolic mathematics package MAPLE and its visualization tools are engaged to perform computational studies on convergence of the first two probabilistic moments for the simple unidirectional Couette Newtonian fluid flow. The methodology and its implementations need many further improvements, however their efficient applications in various engineering problems are possible now.
21609553
Multiresolutional Techniques in Finite Element Method Solution of Eigenvalue Problem
Computational analysis of unidirectional transient problems in multiscale heterogeneous media using specially adopted homogenization technique and the Finite Element Method is described below. Multiresolutional homogenization being the extension of the classical micro-macro traditional approach is used to calculate effective parameters of the composite. Effectiveness of the method is compared against previous techniques thanks to the FEM solution of some engineering problems with real material parameters and with their homogenized values. Further computational studies are necessary in this area, however application of the multiresolutional technique is justified by the natural multiscale character of composites.
33895822
Application of the OBD method for optimization of neural state variable estimators of the two-mass drive system
This paper presents neural estimators of the mechanical state variables of the electrical drive system with elastic joints. The non-measurable state variables, as the torsional torque and the load machine speed are estimated using multilayer feed-forward neural networks. The main stages of the design methodology of these neural estimators are presented. The optimal brain damage method is implemented for the structure optimization of each neural network. Then signals estimated by neural estimators are tested in the electrical drive control structure with additional feedbacks from the estimated shaft torque and the difference between the motor and the load speeds. The simulation results show good accuracy of both presented neural estimators for the wide range of changes of the reference speed and the load torque. The simulation results are then verified by laboratory experiments.