A predictive model on air entrainment by plunging water jets using GEP and ANN

Plunging water jet flow situations are frequently encountered in nature and environmental engineering. A plunging liquid jet has the ability to provide vigorous gas-liquid mixing and dispersion of small bubbles in the liquid, and enhances mass transfer rate by producing larger gas-liquid interfacial area. This process is called air-entrainment or aeration by a plunging water jet. Advances in field of Artificial Intelligence (AI) offer opportunities of utilizing new algorithms and models. This study presents Artificial Neural Network (ANN) and Gene-Expression Programming (GEP) model, which is an extension to genetic programming, as an alternative approach to modeling of volumetric air entrainment rate by plunging water jets. A new formulation for prediction of volumetric air entrainment rate by plunging water jets using GEP is developed. The GEP-based formulation and ANN approach are compared with experimental results, Multiple Linear/Nonlinear Regressions (MLR/NMLR) and other equations. The results have shown that the both ANN and GEP are found to be able to learn the relation between volumetric air entrainment rate and basic water jet properties. Additionally, sensitivity analysis is performed and it is found that nozzle diameter is the most effective parameter on the volumetric air entrainment rate among water jet velocity, jet length and jet impact angle.