论文标题
使用机器学习模型在湍流对流中对雷诺和努塞尔的数字进行预测
Predictions of Reynolds and Nusselt numbers in turbulent convection using machine-learning models
论文作者
论文摘要
在本文中，我们开发了一个多元回归模型和一个神经网络模型，以预测湍流热对流中的雷诺数（RE）和Nusselt数量。我们将它们的预测与早期对流模型的预测进行了比较：Grossmann-Lohse〜 [Phys。莱特牧师。 \ textbf {86}，3316（2001）]，修订后的grossmann-lohse〜 [phys。流体\ TextBf {33}，015113（2021）]和Pandey-Verma [Phys。 Rev. E \ textbf {94}，053106（2016）]模型。我们观察到，尽管所有模型的预测彼此都非常接近，但在这项工作中开发的机器学习模型为实验和数值结果提供了最佳匹配。
In this paper, we develop a multivariate regression model and a neural network model to predict the Reynolds number (Re) and Nusselt number in turbulent thermal convection. We compare their predictions with those of earlier models of convection: Grossmann-Lohse~[Phys. Rev. Lett. \textbf{86}, 3316 (2001)], revised Grossmann-Lohse~[Phys. Fluids \textbf{33}, 015113 (2021)], and Pandey-Verma [Phys. Rev. E \textbf{94}, 053106 (2016)] models. We observe that although the predictions of all the models are quite close to each other, the machine learning models developed in this work provide the best match with the experimental and numerical results.
