Babanezhad Meisam, Rezakazemi Mashallah, 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.
ACS Omega. 2020 Dec 21;6(1):239-252. doi: 10.1021/acsomega.0c04386. eCollection 2021 Jan 12.
In predicting the turbulence property of gas (bubble) flow in the domain of continuous fluid and liquid, the integration of machine learning and computational fluid dynamics (CFD) methods reduces the overall computational time. This combination enables us to see the effective input parameters in the engineering process and the impact of operating conditions on final outputs, such as gas hold-up, heat and mass transfer, and the flow regime (uniform bubble distribution or nonuniform bubble properties). This paper uses the combination of machine learning and single-size calculation of the Eulerian method to estimate the gas flow distribution in the continuous liquid fluid. To present the machine-learning method besides the Eulerian method, an adaptive neuro-fuzzy inference system (ANFIS) is used to train the CFD finding and then estimate the flow based on the machine-learning method. The gas velocity and turbulent eddy dissipation rate are trained throughout the bubble column reactor (BCR) for each CFD node, and the artificial BCR is predicted by the ANFIS method. This smart reactor can represent the artificial CFD of the BCR, resulting in the reduction of expensive numerical simulations. The results showed that the number of inputs could significantly change this method's accuracy, representing the intelligence of method in the learning data set. Additionally, the membership function specifications can impact the accuracy, particularly, when the process is trained with different inputs. The turbulent eddy dissipation rate can also be predicted by the ANFIS method with a similar model pattern for air superficial gas velocity.
在预测连续流体和液体领域中气体（气泡）流的湍流特性时，机器学习与计算流体动力学（CFD）方法的结合减少了整体计算时间。这种结合使我们能够了解工程过程中的有效输入参数以及操作条件对最终输出的影响，例如气体持留率、传热传质以及流型（均匀气泡分布或不均匀气泡特性）。本文采用机器学习与欧拉方法的单尺寸计算相结合的方式来估算连续液体流体中的气体流动分布。为了在欧拉方法之外展示机器学习方法，使用自适应神经模糊推理系统（ANFIS）对CFD结果进行训练，然后基于机器学习方法估算流动情况。针对每个CFD节点，在整个鼓泡塔反应器（BCR）中对气体速度和湍流涡耗散率进行训练，并通过ANFIS方法预测人工BCR。这种智能反应器可以代表BCR的人工CFD，从而减少昂贵的数值模拟。结果表明，输入数量会显著改变该方法的准确性，这体现了该方法在学习数据集中的智能性。此外，隶属函数规范会影响准确性，特别是在使用不同输入对过程进行训练时。对于空气表观气体速度，湍流涡耗散率也可以通过具有相似模型模式的ANFIS方法进行预测。
