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您的检索式：作者名="Mahmoud M.Selim"
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| 1 | Torsional Wave in a Dissipative Cylindrical Shell Under Initial Stresses显示文摘The dispersion relation of torsional wave in a dissipative,incompressible cylindrical shell of infinite length incorporating initial stresses effects is investigated.The governing equation and closed form solutions are derived with the aid of Biot’s principle.Phase velocity and damping of torsional wave are obtained analytically and the influences of dissipation and initial stresses are studied in details.We proposed a new method for obtaining the phase and damping velocities of torsional wave in a complex form.Numerical results analyzing the torsional wave propagation incorporating initial stress effects are analyzed and presented in graphs.The analytical and numerical solutions reveal that,the dissipation as well as the initial stresses have notable impacts on the phase velocity of torsional wave in a pre-stressed dissipative cylindrical shell.The numerical results reveal that,the initial stresses and dissipation,considerably,effect the phase velocity of the torsional wave.It has been observed that,any change in dissipation parameter(δ)produces a substantial change in damping velocity of torsional wave.In addition,it can be seen that,the phase velocity increases as the initial stress parameter increases.Finally,the result of numerical simulation illustrated the influence of dissipation and initial stresses on damping and phase velocities of torsional wave propagation.The conclusion made shown the consistency with the Biot’s incremental deformation theory,and the effective on model such as engineering mechanics and displacement of particles. | Mahmoud M.Selim Khaled A.Gepreel | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 2 | Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model显示文摘In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties. | Mohamed F.El-Amin Abdulhamit Subasi Mahmoud M.Selim Awad Mousa | 2021 | Computers, Materials & Continua2021，，8： | 0 |
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