论文标题
使用机器学习的点粒子阻力，提升和扭矩闭合模型：分层方法和解释性
Point-particle drag, lift, and torque closure models using machine learning: hierarchical approach and interpretability
论文作者
论文摘要
使用机器学习开发确定性的邻里信息封闭模型，最近从分散的多相流社区中引起了人们的兴趣。粒子分辨数据的可用性阻碍了神经模型对于这一复杂多体问题的鲁棒性。目前的工作通过实施两种策略来解决这种不可避免的数据差异：（i）通过使用旋转和反射等效性神经网络和（ii）追求基于物理的层次机器学习方法。观察到的机器学到的模型可在邻居诱导的力和扭矩波动的预测中达到85％和96％的最大精度，以实现雷诺数的广泛数量和体积分数条件。此外，我们追求力量和扭矩网络体系结构，这些架构提供了跨越雷诺数字（$ 0.25 \ leq req req 250 $）和粒子体积分数（$ 0 \ leq ϕ \ leq leq 0.4 $）的通用预测。该方法的层次结构性质可以通过超越二进制相互作用以包括三分值相互作用来改进对流向扭矩等数量的预测。
Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest in recent times from dispersed multiphase flow community. The robustness of neural models for this complex multi-body problem is hindered by the availability of particle-resolved data. The present work addresses this unavoidable limitation of data paucity by implementing two strategies: (i) by using a rotation and reflection equivariant neural network and (ii) by pursuing a physics-based hierarchical machine learning approach. The resulting machine learned models are observed to achieve a maximum accuracy of 85% and 96% in the prediction of neighbor-induced force and torque fluctuations, respectively, for a wide range of Reynolds number and volume fraction conditions considered. Furthermore, we pursue force and torque network architectures that provide universal prediction spanning a wide range of Reynolds number ($0.25 \leq Re \leq 250$) and particle volume fraction ($0 \leq ϕ\leq 0.4$). The hierarchical nature of the approach enables improved prediction of quantities such as streamwise torque, by going beyond binary interactions to include trinary interactions.
