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 and Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam.
Sci Rep. 2021 Jan 13;11(1):902. doi: 10.1038/s41598-020-79628-w.
Heat transfer augmentation of the nanofluids is still an attractive concept for researchers due to rising demands for designing efficient heat transfer fluids. However, the pressure loss arisen from the suspension of nanoparticles in liquid is known as a drawback for developing such novel fluids. Therefore, prediction of the nanofluid pressure, especially in internal flows, has been focused on studies. Computational fluid dynamics (CFD) is a commonly used approach for such a prediction of fluid flow. The CFD tools are perfect and precise in prediction of the fluid flow parameters. But they might be time-consuming and expensive, especially for complex models such as 3-dimension modeling and turbulent flow. In addition, the CFD could just predict the pressure, and it is disabled for finding the relationship of such variables. This study is intended to show the performance of the artificial intelligence (AI) algorithm as an auxiliary method for cooperation with the CFD. The turbulent flow of Cu/water nanofluid warming up in a pipe is considered as a sample of a physical phenomenon. The AI algorithm learns the CFD results. Then, the relation between the CFD results is discovered by the AI algorithm. For this purpose, the adaptive network-based fuzzy inference system (ANFIS) is adopted as AI tool. The intelligence condition of the ANFIS is checked by benchmarking the CFD results. The paper outcomes indicated that the ANFIS intelligence is met by employing gauss2mf in the model as the membership function and x, y, and z coordinates, the nanoparticle volume fraction, and the temperature as the inputs. The pressure predicted by the ANFIS at this condition is the same as that predicted by the CFD. The artificial intelligence of ANFIS could find the relation of the nanofluid pressure to the nanoparticle fraction and the temperature. The CFD simulation took much more time (90-110 min) than the total time of the learning and the prediction of the ANFIS (369 s). The CFD modeling was done on a workstation computer, while the ANFIS method was run on a normal desktop.
由于设计高效传热流体的需求不断增加，纳米流体的传热增强对研究人员来说仍然是一个有吸引力的概念。然而，纳米颗粒悬浮在液体中所产生的压力损失是开发这种新型流体的一个缺点。因此，纳米流体压力的预测，特别是在内流中，一直是研究的重点。计算流体动力学（CFD）是进行这种流体流动预测的常用方法。CFD工具在预测流体流动参数方面非常完美和精确。但它们可能耗时且昂贵，特别是对于复杂模型，如三维建模和湍流。此外，CFD只能预测压力，无法找出这些变量之间的关系。本研究旨在展示人工智能（AI）算法作为与CFD配合使用的辅助方法的性能。以管道中Cu/水纳米流体的湍流加热作为物理现象的一个样本。AI算法学习CFD结果。然后，由AI算法发现CFD结果之间的关系。为此，采用基于自适应网络的模糊推理系统（ANFIS）作为AI工具。通过将CFD结果作为基准来检查ANFIS的智能情况。论文结果表明，通过在模型中使用高斯二阶隶属函数（gauss2mf）作为隶属函数，并将x、y和z坐标、纳米颗粒体积分数和温度作为输入，可以满足ANFIS的智能要求。在此条件下，ANFIS预测的压力与CFD预测的压力相同。ANFIS的人工智能可以找出纳米流体压力与纳米颗粒分数和温度之间的关系。CFD模拟花费的时间（90 - 110分钟）比ANFIS学习和预测的总时间（369秒）多得多。CFD建模是在工作站计算机上完成的，而ANFIS方法是在普通台式机上运行的。
