Sharif Digital Repository

Ultrasonic Testing (UT) is one of the most common types of nondestructive methods that is being used in various industries, especially in the automotive industry. In this paper, qualitative and quantitative control of resistance spot welds on three-sheet joints was studied. Initially, mathematical model of ultrasonic waves was extracted for triple sheet joints. Then, acoustic simulation of ultrasonic testing on spot welds was performed using Finite Element Method (FEM). Afterwards, A Multilayer Perceptron (MLP) neural network was used to classify spot welds based on their diameter. There was a mean error of 20.9 % between peak amplitudes of numerical and theoretical models which the most... 

Oil and gas are likely the most important sources for producing heat and energy in both domestic and industrial applications. Hydrocarbon reservoirs that contain these fuels are required to be characterized to exploit the maximum amount of their fluids. Well testing analysis is a valuable tool for the characterization of hydrocarbon reservoirs. Handling and analysis of long-term and noise-contaminated well testing signals using the traditional methods is a challenging task. Therefore, in this study, a novel paradigm that combines wavelet transform (WT) and recurrent neural networks (RNN) is proposed for analyzing the long-term well testing signals. The WT not only reduces the dimension of... 

The authors studied the efficiency and accuracy of neural network model for prediction of permeability as a key parameter in reservoir characterization. So, some multilayer perceptron (MLP) neural network models with different learning algorithms of Levenberg-Margnardt, back propagation, improved back propagation (IBP), and quick propagation with three layers and different node numbers (3, 4, 5, 6, 7) in the middle layer have been presented. These models have been obtained by 630 permeability data from one of offshore reservoirs located in Saudi Arabia. The accuracy of models was studied by comparing the obtained results of each model with experimental data. So, the neural network with IBP... 

Multilayer perceptron (MLP) neural networks have been used extensively for estimation/regression of parameters. Moreover, recent studies have shown that learning algorithms of MLP which are based on Gaussian function are more accurate. In this paper, the mass attenuation coefficient (MAC) of gamma radiation for light-weight materials (e.g. O-8), mid-weight materials (e.g. Al-13), and heavy-weight materials (e.g. Pb-82) is modelled using Gaussian function based regularization of MLP (i.e. Bayesian regularization (BR)) and by a modular estimator. The results are compared with the Reference results. To show better performance of the utilized algorithm, the results of the different supervised... 

One of the most important challenges in target parameters estimation via model-free methods is selection of the most effective input parameters namely features selection (FS). Indeed, irrelevant features can degrade the estimation performance. In the current study, the challenge of choosing among the several plant parameters is tackled by means of the innovative FS algorithm named ranking of features with minimum deviation from the target parameter (RFMD). The selected features accompanied with the stable and the fast learning algorithm of multilayer perceptron (MLP) neural network (i.e. Levenberg-Marquardt algorithm) which is a combination of gradient descent and Gauss-newton learning... 

In this paper, an efficient stress recovery technique is presented to estimate the recovered stress field at the nodal points. The feed–forward back–propagation multilayer perceptron (MLP) neural network approach is employed to improve the accuracy of the stress recovery method. An automatic adaptive mesh refinement is performed based on a–posteriori Zienkiewicz–Zhu error estimation method. The proposed technique is employed to recover the stress field accurately in the regions with a high stress gradient where the conventional recovery techniques are not able to improve the stress fields efficiently due to the singular behavior of problem. Finally, several numerical examples are solved to... 

Viscosity of mixtures of biodiesels (admixtures) and mixtures of biodiesel/diesel (blends) is a important parameter for determining their combustion behavior. There is no universal and general model for prediction of viscosity of these systems at different conditions. Hence, developing simple, accurate, and general models for prediction of viscosity of these systems is of great importance. In this work, three computer-based models named multilayer perceptron neural network (MLP-NN), radial basis function optimized by particle swarm optimization (PSO-RBF), and adaptive neuro fuzzy inference system optimized by hybrid approach (Hybrid-ANFIS) were developed for prediction of viscosity of blends... 

In this study, the application of data-driven methods for performance prediction of a heat pump assisted humidification-dehumidification (HDH-HP) desalination system was investigated for the first time. Although HDH-HP desalination systems have been widely studied both theoretically and experimentally, the application of data-driven models as a powerful predictive tool has not yet been investigated in these systems. To fill this gap, three data-driven models (MLPANN, RBFANN, and ANFIS) were applied using 180 experimental samples. The gain output ratio (GOR), heat transfer rates of the evaporator Q̇e, and evaporative condenser Q̇c, were considered as outputs. The results indicate that the...