A GENERALIZED FINITE DIFFERENCE METHOD FOR TRANSIENT HEAT CONDUCTION ANALYSIS-SHORT COMMUNICATION

This short communication presents a meshless local B-spline basis functions-finite difference (FD) method for transient heat conduction analysis. The method is truly meshless as only scattered nodal distribution is required in the problem domain. It is also simple and efficient to program. As it has the Kronecker delta property, the imposition of boundary conditions can be incorporated efficiently. In the method, any governing equations are discretized by B-spline approximation in the spirit of FD technique using local B-spline collocation. It hence belongs to a generalized FD method, in which any derivative at a point or node is stated as neighbouring nodal values based on the B-spline interpolants. Numerical results show the effectiveness and efficiency of the meshless method for analysis of transient heat conduction in complex domain

HIGH ORDER B-SPLINE COLLOCATION METHOD AND ITS APPLICATION FOR HEAT TRANSFER PROBLEMS

High order B-spline collocation for solving boundary value problem is presented in this paper. The approach employs high order B-spline basis functions with high approximation and continuity properties to handle problem domain with scattered or random distribution of knot points.  Using appropriate B-spline basis function construction, the new approach introduces no difficulties in imposing both Dirichlet and Neumann boundary conditions in the problem domain. Several numerical examples in arbitrary domains, both regular and irregular shaped domains, are considered in the present study. In addition, simulation results concerning with heat transfer applications are further presented and discussed

INFORMATIVE BOUNDS OF NEURAL NETWORKS PREDICTION FOR COMPOSITE FATIGUE LIFE UNDER VARIABLE AMPLITUDE LOADING

In this study, the informative bounds of neural networks (NN) prediction with respect to the utilization of less fatigue data for fatigue life assessment of composite material covering a wide range of stress ratios R was examined and investigated. Fiberglass reinforced polyester of [90/0/±45/0]S  lay-up with fatigue data of various stress ratios  (R = 0.1, 0.5, 0.7, 0.8, 0.9, -0.5, -1, -2 and 10)  was examined in the present paper. Multi-layer Perceptrons (MLP) trained with Levenberg-Marquardt algorithm was utilized to result in fast and efficient NN model and Bayesian regularization technique was incorporated to deal with limited training data chosen for the model. The developed NN model was trained with fatigue data from only two stress ratios, where three sets of two stress ratio values were formed and used as the training sets, namely R = 0.1 and 0.5, R = 0.1 and -1, and R = 0.1 and 10, respectively. It was obtained that fatigue data from R = 10 produced the widest bounds of prediction, namely having the highest estimated standard deviation value from the fatigue lives predicted. Furthermore, it is revealed in the current study knowing the fact that fatigue data from R = 10 have the highest estimated standard deviation and subsequently including the fatigue data as one of the training data set, the NN model trained could produce the lowest mean squared error (MSE) value for the results of fatigue life prediction. This is justifying also the selection of training set of R = 0.1 and 10 as best training set in the previous study, which is based on the stress ratios’ better relative positions in the corresponding constant life diagram (CLD).  Finally, taking the highest estimated standard deviation value from fatigue data of R = 10 as the conservative estimated bounds of NN prediction, it was shown that for the NN prediction of fatigue life whose noticeable discrepancies with the experimental data, the discrepancies were well confined within the conservative bounds of prediction.  

EFFICIENT FATIGUE LIFE ASSESSMENT OF COMPOSITE MATERIALS BY USING A HYBRID SURROGATE MODELING

In this study, hybrid surrogate and nonlinear autoregressive with exogenous inputs (NARX) model is developed and presented as data-driven based predictive model for efficient fatigue life assessment of composite materials. Surrogate modeling based upon wavelet neural networks (WNN) is employed to efficiently unveil mathematical pattern in S-N data, but costly to get from experiments. Moreover, the NARX architecture is chosen in order to enable multi-step ahead prediction in fatigue life assessment of multivariable amplitude loadings. By observing fatigue data as dynamic data of stress ratio series, it is shown that the hybrid model produces reasonably accurate fatigue life prediction by using fatigue data from two stress ratio values only. The use of two stress ratio values also allows usage of more limited fatigue data in the lifetime prediction. The WNN-NARX surrogate model is tested with well-known fatigue data in literature. Several composite materials examined in this study show the efficacy and robustness of the proposed model

EFFECT OF POURING TEMPERATURE AND DEGASSING ON THE CASTING QUALITY OF Al 6061: EXPERIMENTAL AND NUMERICAL STUDY

As one of important components of an airplane, body of airplane is required to have high value of strength to weight ratio. In this study, transient heat transfer of Al 6061 in a sand casting process was investigated both experimentally and numerically. The effects of different pouring temperatures (700, 720 and 740 °C) and presence of thin film and H2 inclusions are considered in the present study. Composition, XRD, metallography and tensile strength tests have been carried out to examine the casting product quality, before and after degassing, a process to remove the inclusions from the cast. Correspondingly, heat transfer simulations were carried out by taking into account the variation of pouring temperatures and the presence of inclusions. From the present experimental and numerical study, it was found that: (i) Degassing enhanced significantly the strength of Al 6061 product. The highest tensile strength value has been found to be 64.30 MPa, related to the pouring temperature of 700 °C with degassing, while the lowest one is 35.85 MPa associated with the pouring temperature of 700 °C without degassing. (ii) Pouring temperature did not affect significantly to the strength of Al 6061 product, especially when degassing process was carried out, and (iii) The presence of thin film and hydrogen gas inclusions affected the cooling rate of the metal slab. Overall, the cooling of the metal slab with thin film inclusion became slower, while the cooling of the metal slab with hydrogen gas inclusions became faster

MESHLESS METHODS FOR SOLVING REACTION-DIFFUSION PROBLEMS-A BRIEF REVIEW

Reaction-diffusion equations represent many important and critical applications in engineering and science. Numerical techniques play an important role for solving such equations accurately and efficiently. This paper presents a brief review of meshless methods for solving general diffusion equations, including reaction-diffusion systems

NEURAL NETWORKS AND EVOLUTIONARY OPTIMIZATION TECHNIQUES AND THEIR APPLICATIONS IN FATIGUE LIFE ASSESSMENT OF COMPOSITE MATERIALS-A BRIEF REVIEW

Modeling of fatigue life of composite materials under various loading and environment conditions becomes important and challenging task from viewpoint of performance and reliability as it forms a basis for lifetime assessment of composite structures under complex variable state of stress. Application of soft computing techniques as new approach and route for modelling of composite material fatigue lives has attracted a great interest recently. The applications of soft computing techniques in fatigue life assessment of composite materials are reviewed and discussed in this paper