The Hartley transform, a real-valued alternative to the complex Fourier transform, is presented as an efficient tool for the analysis and simulation of earthquake accelerograms. This paper is introduced a novel method based on discrete Hartley transform (DHT) and radial basis function (RBF) neural network for generation of artificial earthquake accelerograms from specific target spectrums. Acce...

In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and t...

In this paper, parametric and numerical model of the DC motor, connected to aircraft propellers are extracted. This model is required for controlling trust and velocity of the propellers, and consequently, an aircraft. As a result, both of torque and speed of the propeller can be controlled simultaneously which increases the kinematic and kinetic performance of the aircraft. Parametric model of...

porosity is one of the most important properties for comprehensive studies of hydrocarbon reservoirs. for determination of porosity in a rock, that is the ratio of volume of voids to the total volume of the rock, there are two conventional methods: in the first method, direct measurement of porosity is carried out by testing drilling cores. in the second method, porosity is determined indirectl...

this paper proposes a two-step approach for characterizing the reservoir properties of the world’s largest non-associated gas reservoir. this approach integrates geological and petrophysical data and compares them with the field performance analysis to achieve a practical electrofacies clustering. porosity and permeability prediction is done on the basis of linear functions, succeeding the elec...

This paper focuses on using the numerical finite volume method (FVM) and artificial neural network (ANN) in order to propose a correlation for computing the entrance length of laminar magnetohydrodynamics (MHD) channels flow. In the first step, for different values of the Reynolds (Re) and Hartmann (Ha) numbers (600<ReL increases.