Reconstruction and analysis of negatively buoyant jets with interpretable machine learning

TL;DR

This study employs interpretable machine learning models to predict and analyze the behavior of negatively inclined buoyant jets from wastewater discharge, aiming to reduce environmental impact through optimized design.

Contribution

The paper introduces a machine learning approach using multiple models, especially ANNs with SHAP interpretation, to predict jet characteristics based on OpenFOAM simulations and experimental data.

Findings

ANN achieved R2 0.98 and RMSE 0.28 in predictions.

SHAP analysis identified key parameters influencing jet behavior.

Machine learning models can effectively replace extensive simulations and experiments.

Abstract

In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. To minimize harmful effects and assess environmental impact, a detailed numerical investigation is necessary. The selection of appropriate geometry and working conditions for minimizing such effects often requires numerous experiments and numerical simulations. For this reason, the application of machine learning models is proposed. Several models including Support Vector Regression, Artificial Neural Networks, Random Forests, XGBoost, CatBoost and LightGBM were trained. The dataset was built with numerous OpenFOAM simulations, which were validated by experimental data from previous research. The best prediction was obtained by Artificial Neural Network with an average of R2 0.98 and RMSE 0.28. In order to understand the working of the machine learning model and the influence of all parameters on the geometrical characteristics of inclined buoyant jets, the SHAP feature interpretation method was used.