Y
Y.N. Al-Nassar
Researcher at King Fahd University of Petroleum and Minerals
Publications - 30
Citations - 453
Y.N. Al-Nassar is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Residual stress & Finite element method. The author has an hindex of 11, co-authored 30 publications receiving 414 citations. Previous affiliations of Y.N. Al-Nassar include University of Colorado Boulder.
Papers
More filters
Journal ArticleDOI
Scattering of lamb waves by a normal rectangular strip weldment
TL;DR: In this paper, a combined finite element and Lamb wave modal expansion method is presented for analysing scattering of time harmonic Lamb waves by material and geometric irregularities in an isotropic linearly elastic infinite plate.
Journal ArticleDOI
Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks
TL;DR: Abductive network machine learning is proposed as an alternative to the conventional multiple regression analysis method for modelling and forecasting monthly electric energy consumption as mentioned in this paper, which requires fewer input parameters, are more accurate and are easier and faster to develop.
Journal ArticleDOI
On the vibration of a rotating blade on a torsionally flexible shaft
Y.N. Al-Nassar,B.O. Al-Bedoor +1 more
Journal ArticleDOI
Investigation of residual stress development in spiral welded pipe
TL;DR: In this paper, a semi-destructive hole drilling experimental technique has been adopted for analyzing the stresses in spiral welded pipe and the distribution of residual stresses is calculated through different mathematical procedures, such as uniform method, power series method and integral method.
Journal ArticleDOI
On the impact-induced damage in glass fiber reinforced epoxy pipes
TL;DR: In this article, a finite element (FE) model of reinforced epoxy (GFRE) pipe is developed and used in conjunction with failure criteria based on three-dimensional state of stress to predict layer damage under low-velocity impact.