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

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