Simulations of the Concentration Fields of Rosette-Type Multiport Buoyant Discharges Using Combined CFD and Multigene Genetic Programming Techniques

Abstract

Rosette-type diffusers are becoming popular nowadays for discharging wastewater effluents. Effluents are known as buoyant jets if they have a lower density than the receiving water, and they are often used for municipal and desalination purposes. These buoyant effluents discharged from rosette-type diffusers are known as rosette-type multiport buoyant discharges. Investigating the mixing properties of these effluents is important for environmental impact assessment and optimal design of the diffusers. Due to the complex mixing and interacting processes, most of the traditional simple methods for studying free single jets become invalid for rosette-type multiport buoyant discharges. Three-dimensional computational fluid dynamics (3D CFD) techniques can satisfactorily model the concentration fields of rosette-type multiport buoyant discharges, but these techniques are typically computationally expensive. In this study, a new technique of simulating rosette-type multiport buoyant discharges using combined 3D CFD and multigene genetic programming (MGGP) techniques is developed. Modeling the concentration fields of rosette-type multiport buoyant discharges using the proposed approach has rarely been reported previously. A validated numerical model is used to carry out extensive simulations, and the generated dataset is used to train and test MGGP-based models. The study demonstrates that the proposed method can provide reasonable predictions and can significantly improve the prediction efficiency.

Concentration Field Estimation on Effluent Mixing and Transport With a Parameter‐Based Field Reconstruction Convolutional Neural Network and Random Forests

“…To the authors' knowledge, no other studies have attempted to predict the concentration field of multiple vertical buoyant effluents using a DL approach. The research group has previously predicted key parameters in the process of effluent mixing and spatial distribution of variables in other problems using genetic programming (GP) and multi‐gene genetic programming (MGGP; Yan, Mohammadian, et al., 2021; Yan, Wang, et al., 2021). However, a preliminary test for this study showed that the RF approach significantly outperformed MGGP in both model accuracy and efficiency.…”

“…Modeling effluent mixing and transport using fully 3D CFD models is becoming mainstream in recent years. For instance, CFD models have been developed for the determination of mixing brine discharges (Didier, 2004;George et al, 2003), seawater desalination discharge in limited disposal areas (Abou-Elhaggag et al, 2011), mixing and dispersion of marine discharges (Robinson et al, 2014), inclined dense jets (Kheirkhah Gildeh et al, 2015), jets subjected to lateral confinement (Yan & Mohammadian, 2017), solvent dispersion in porous media (Peyman et al, 2018), multiple vertical buoyant jets (Yan et al, 2020), inclined dense jets (Gildeh et al, 2021a(Gildeh et al, , 2021b, rosette-shaped multi-port buoyant jets (Yan, Wang, et al, 2021), and the results demonstrated the excellent predictive capability of these models. However, modeling effluent mixing and transport using fully 3D models is very computationally expensive and requires extensive computing resources, especially for the processes in large-scale water bodies.…”

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

“…The output feature was the normalized concentration. The MGGP algorithm was performed using a modified MATLAB script GPTIPS2 [46,66]. The population was set as 500.…”

“…ML-based surrogate models have been successfully established to predict the flow field of a curved open-channel flow [43], density-driven solute transport [44], the spatial distribution of flow depth in fluvial systems [45], etc. In recent years, datadriven models based on ML algorithms have also been proposed for effluent-driven solute transport [30,33,46], but none of them have considered the Coriolis effect. Furthermore, most of these studies on water effluents only predicted certain characteristic parameters instead of the complete concentration fields [30,33] or reconstructed the concentration fields by combining pointwise data instead of directly reproducing the concentration fields [46].…”

“…In recent years, datadriven models based on ML algorithms have also been proposed for effluent-driven solute transport [30,33,46], but none of them have considered the Coriolis effect. Furthermore, most of these studies on water effluents only predicted certain characteristic parameters instead of the complete concentration fields [30,33] or reconstructed the concentration fields by combining pointwise data instead of directly reproducing the concentration fields [46].…”