Multigene Genetic-Programming-Based Models for Initial Dilution of Laterally Confined Vertical Buoyant Jets

Abstract

A new approach based on the multigene genetic-programming (MGGP) technique is proposed to predict initial dilution of vertical buoyant jets subjected to lateral confinement. The models are trained and tested using experimental data, and the good matches demonstrate the generalization and predictive capabilities of the evolved MGGP-based models. The best Pareto-optimal MGGP-based model is also compared with the model evolved using a single-gene genetic-programming (SGGP) algorithm and an existing regression-based empirical equation. The comparisons reveal the superiority of the MGGP-based model. This study confirms that the MGGP technique is promising in evolving an explicit, accurate, and compact model, and the developed models can be employed to estimate effectively and efficiently the dilution properties of a laterally confined vertical buoyant jet.

Numerical Modeling of Multiple Inclined Dense Jets Discharged from Moderately Spaced Ports

“…However, these methods have not been proven to be effective for multiple inclined dense jets, partially because the mechanics of such jets are very complicated, and are thus difficult to be conceptualized by simplified approaches. Yan and Mohammadian [19,20] successfully predicted the mixing properties of multiple inclined dense jets utilizing the multi-gene genetic programming approach, but models based on artificial intelligence or regression-based approaches still require more experimental or physical-based numerical data for improvement or extension, otherwise their predictions for the cases with parameters out of the training range might be unreliable. Therefore, demonstrating the performance of a numerical model for multiple inclined dense jets, which is a major contribution of the present study, is quite meaningful.…”

“…Compared with multiple horizontal and vertical jets, the mixing processes of multiple inclined jets are much more complicated and less understood, and thus studying multiple inclined dense jets using the existing theoretical, analytical, or simplified numerical approaches is very challenging. Recently, Yan and Mohammadian have successfully applied artificial intelligence (AI) approaches to develop explicit models for capturing the mixing properties of laterally confined buoyant jets [19] and multiple inclined dense jets [20], but the authors acknowledged that the evolved models may require more training data for improvement and extension. The recent advances in numerical and computing techniques provide a new approach for simulating wastewater jets as a three-dimensional phenomenon, and the three-dimensional numerical modeling method has become popular in the past several years.…”

Three-Dimensional Numerical Simulations of Buoyant Jets Discharged from a Rosette-Type Multiport Diffuser

“…Third, applying the previous models to complicated practical cases is not feasible. Although Yan and Mohammadian [18][19][20] have demonstrated the capability of the multi- The downward-inclined buoyant jets behaved like reversed upward-inclined dense jets. In the initial dilution region where the jets were falling, the vertical velocities of the jets decreased because of the buoyancy effects and momentum transfer between the fluids.…”

“…Third, applying the previous models to complicated practical cases is not feasible. Although Yan and Mohammadian [18][19][20] have demonstrated the capability of the multi-gene genetic programming approach for wastewater jets, numerical simulations are still necessary to provide reliable data for model training and validation. It should be noted that the AI-based models may provide comparable or even better predictions than numerical models within the range of the training data, but their predictions out of the training range should be used with caution.…”

“…However, the uncertain parameters, such as the contraction coefficient, involved in the model may result in unacceptable uncertainties, and utilizing the model for complicated cases may not be feasible. Recently, Yan and Mohammadian have successfully utilized an artificial intelligence approach based on multi-gene genetic programming for buoyant jets subjected to lateral confinement [18], multiple inclined dense jets [19], and multiple vertical buoyant jets [20]. However, it has been stated by the authors that this type of model requires extensive reliable data for further training and generalization.…”