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.
翻译:在本文中,观察到了海水淡化等废水排放过程中出现的负倾斜浮力喷气式喷气式喷气式喷气式,为尽量减少有害影响并评估环境影响,需要进行详细的数字调查,为尽量减少这种影响而选择适当的几何学和工作条件往往需要多次实验和数字模拟,为此,提议采用机器学习模型,培训了几个模型,包括支持矢量反射、人工神经网络、随机森林、XGBoost、CatBoost和LightGBM;数据集是用许多OpenFOAM模拟建造的,这些模拟得到了以往研究实验数据的验证;人工神经网络获得的最佳预测平均为R2 0.98和RMSE 0.28;为了了解机器学习模型的运作情况和所有参数对倾角浮喷气式的几何特性的影响,使用了SHAP特征解释方法。