Abstract Permeability coefficient of soil (k) is one the most important parameters in groundwater studies. This study, two robust explicit data-driven methods, Including classification and regression trees (CART) group method data handling (GMDH) were developed using characteristics soil, i.e., clay content (CC), water (?), liquid limit (LL), plastic (PL), specific density (?), void ratio (e) t...

Vortex finder is a key part of cyclone separator. In the present study, multi-objective optimization of vortex finder is performed at three steps. At the first step, collection efficiency (η) and the pressure drop (∆p) in a set of cyclones with different vortex finder shape are numerically investigated using CFD techniques. Two meta-models based on the evolved group method of data handling (GMD...

Abstract Purpose The Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock real estate investment trust (REIT) returns some countries region, but not the United States (US) REIT market. primary goal this study is predict US market using GMDH then compare its accuracy with that d...

In the present study, Computational Fluid Dynamics (CFD) techniques and Artificial Neural Networks (ANN) are used to predict the pressure drop value (Δp ) of Al2O3-water nanofluid in flat tubes. Δp  is predicted taking into account five input variables: tube flattening (H), inlet volumetric flow rate (Qi  ), wall heat flux (qnw  ), nanoparticle volume fraction (Φ) and nanoparticle diameter (dp ...

The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalanformations in the South Pars gas field. In the first step, pore facies were determined based on Mercury Injection Capillary Pressure (MICP) data incorporation with the Hierarchical Clustering Analysis (HCA) method. In the next step, polynomial meta...