论文标题
重建和分析具有可解释的机器学习的负浮力喷气机
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning
论文作者
论文摘要
在本文中，观察到在废水中出现的负倾斜的浮力喷头，这些喷头出现在诸如淡化等过程中的废水中。为了最大程度地减少有害影响并评估环境影响，需要进行详细的数值研究。选择适当的几何形状和最小化此类效果的工作条件通常需要大量的实验和数值模拟。因此，提出了机器学习模型的应用。培训了几种模型，包括支持矢量回归，人工神经网络，随机森林，XGBoost，Catboost和LightGBM。该数据集是由许多OpenFOAM模拟构建的，这些模拟通过先前研究的实验数据进行了验证。最好的预测是通过平均R2 0.98和RMSE 0.28的人工神经网络获得的。为了了解机器学习模型的工作以及所有参数对倾斜浮力喷气机的几何特征的影响，使用了Shap特征解释方法。
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.
