Babanezhad Meisam, Behroyan Iman, Nakhjiri Ali Taghvaie, Marjani Azam, Shirazian Saeed
Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.
Faculty of Electrical - Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam.
Sci Rep. 2020 Oct 7;10(1):16719. doi: 10.1038/s41598-020-72602-6.
Direct numerical simulation (DNS) of particle hydrodynamics in the multiphase industrial process enables us to fully learn the process and optimize it on the industrial scale. However, using high-resolution computational calculations for particle movement and the interaction between the solid phase and other phases in fine timestep is limited to excellent computational resources. Solving the Eulerian flow field as a source of solid particle movement can be very time-consuming. However, by the revolution of the fast and accurate learning process, the Eulerian domain can be computed by smart modeling in a very short computational time. In this work, using the machine learning method, the flow field in the square shape cavity is trained, and then the Eulerian framework is replaced with a machine learning method to generate the artificial intelligence (AI) flow field. Then the Lagrangian framework is coupled with this AI flow field, and we simulate particle motion through the fully AI framework. The Adams-Bashforth finite element method is used as a conventional CFD method (Eulerian framework) to simulate the flow field in the cavity. After simulating fluid flow, the ANFIS method is used as an AI model to train the Eulerian data-set and represents AI fluid flow (framework). The Lagrangian framework is coupled with the AI method, and the particle freely migrates through this artificial framework. The results reveal that there is a great agreement between Euler-Lagrangian and AI- Lagrangian in the cavity. We also found that there is an excellent agreement between AI overview with the Adams-Bashforth approach, and the new combination of machine learning and CFD method can accelerate the calculation of the flow field in the square-shaped cavity. AI model can mimic the vortex structure in the cavity, where there is a zero-velocity structure in the center of the domain and maximum velocity near the moving walls.
多相工业过程中颗粒流体动力学的直接数值模拟（DNS）使我们能够全面了解该过程并在工业规模上对其进行优化。然而，在精细时间步长下对颗粒运动以及固相和其他相之间的相互作用进行高分辨率计算需要卓越的计算资源。将欧拉流场作为固体颗粒运动的源来求解可能非常耗时。然而，通过快速准确学习过程的变革，欧拉域可以通过智能建模在非常短的计算时间内完成计算。在这项工作中，使用机器学习方法对方形腔内的流场进行训练，然后用机器学习方法取代欧拉框架以生成人工智能（AI）流场。然后将拉格朗日框架与该AI流场耦合，我们通过完全的AI框架模拟颗粒运动。亚当斯 - 巴什福斯有限元方法用作传统的计算流体动力学方法（欧拉框架）来模拟腔内的流场。在模拟流体流动之后，使用自适应神经模糊推理系统（ANFIS）方法作为AI模型来训练欧拉数据集并表示AI流体流动（框架）。拉格朗日框架与AI方法耦合，颗粒通过这个人造框架自由迁移。结果表明，腔内欧拉 - 拉格朗日方法和AI - 拉格朗日方法之间有很大的一致性。我们还发现AI概述与亚当斯 - 巴什福斯方法之间有很好的一致性，并且机器学习和计算流体动力学方法的新组合可以加速方形腔内流场的计算。AI模型可以模拟腔内的涡旋结构，在该结构中，域中心存在零速度结构，而在移动壁附近速度最大。
