Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Adsorption. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Mixed convection flow caused by an oscillating cylinder in a square cavity filled with Cu–Al2O3/water hybrid nanofluid

Abstract: The aim of this paper is to examine the effects of Cu–Al2O3/water hybrid nanofluid and Al2O3/water nanofluid on the mixed convection inside a square cavity caused by a hot oscillating cylinder. The governing equations are first transformed into dimensionless form and then discretized over a non-uniform unstructured moving grid with triangular elements. The effects of several parameters, such as the nanoparticle volume fraction, the Rayleigh number, the amplitude of the oscillation, and the period of the oscillation of the cylinder are investigated numerically. The results indicate that the motion of the oscillating cylinder toward the top and bottom walls increases the average Nusselt number when the Rayleigh number is low. Furthermore, the presence of Al2O3 and Cu–Al2O3 nanoparticles leads to an increase in the values of the average Nusselt number Nuavg for cases of low values of the Rayleigh number. It is found that the natural convection heat transfer rate of a simple Al2O3/water nanofluid is better than that of Cu–Al2O3/water hybrid nanofluid.

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

Abstract: The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.