Tajfirooz S, Meijer J G, Kuerten J G M, Hausmann M, Fröhlich J, Zeegers J C H
Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands.
Institute of Fluid Mechanics, Technische Universität Dresden, George-Bähr Strasse 3c, Dresden D-01062, Germany.
Phys Rev E. 2021 Feb;103(2-1):023304. doi: 10.1103/PhysRevE.103.023304.
A statistical learning approach is presented to predict the dependency of steady hydrodynamic interactions of thin oblate spheroidal particles on particle orientation and Reynolds number. The conventional empirical correlations that approximate such dependencies are replaced by a neural-network-based correlation which can provide accurate predictions for high-dimensional input spaces occurring in flows with nonspherical particles. By performing resolved simulations of steady uniform flow at 1≤Re≤120 around a 1:10 spheroidal body, a database consisting of Reynolds number- and orientation-dependent drag, lift, and pitching torque acting on the particle is collected. A multilayer perceptron is trained and validated with the generated database. The performance of the neural network is tested in a point-particle simulation of the buoyancy-driven motion of a 1:10 disk. Our statistical approach outperforms existing empirical correlations in terms of accuracy. The agreement between the numerical results and the experimental observations prove the potential of the method.
提出了一种统计学习方法来预测薄扁球体颗粒的稳定流体动力相互作用对颗粒取向和雷诺数的依赖性。近似这种依赖性的传统经验关联式被基于神经网络的关联式所取代，该关联式可为非球形颗粒流动中出现的高维输入空间提供准确预测。通过对1:10球体在1≤Re≤120的稳定均匀流进行解析模拟，收集了一个由作用在颗粒上的与雷诺数和取向相关的阻力、升力和俯仰扭矩组成的数据库。使用生成的数据库对多层感知器进行训练和验证。在1:10圆盘浮力驱动运动的点粒子模拟中测试了神经网络的性能。我们的统计方法在准确性方面优于现有的经验关联式。数值结果与实验观测之间的一致性证明了该方法的潜力。
