Babanezhad Meisam, Behroyan Iman, Nakhjiri Ali Taghvaie, Rezakazemi Mashallah, Marjani Azam, Shirazian Saeed
Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam.
Faculty of Electrical-Electronic Engineering, Duy Tan University, 550000, Da Nang, Vietnam.
Sci Rep. 2021 Jan 14;11(1):1308. doi: 10.1038/s41598-020-80207-2.
Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of AlO/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.
计算流体动力学（CFD）模拟是一种减少实验及其相关成本的有用方法。尽管CFD可以预测流体流动的所有热流体参数，但使用这种方法无法确定这些参数之间的相互联系。人工智能（AI）算法的机器学习已经显示出智能记录工程数据的能力。然而，目前尚无研究深入探究CFD产生的变量之间的隐含联系。本研究试图将机械CFD与人工算法相结合。将遗传算法与模糊接口系统（GAFIS）相结合。针对入口温度（即305、310、315和320 K）模拟了加热管中AlO/水纳米流体的湍流强制对流。GAFIS将流体的节点坐标、入口温度和湍动能（TKE）作为输入进行学习。将流体温度作为输出进行学习。检查输入数量、种群大小和组件以获得最佳智能效果。最后，在最佳智能状态下，开发了一个公式来建立输出（即纳米流体温度）与输入（纳米流体节点坐标、入口温度和TKE）之间的关系。结果表明，当输入数量、种群大小和指数分别为5、30和3时，GAFIS智能达到最高水平。将湍动能作为第五个输入，回归值从0.95增加到0.98。这意味着通过考虑湍动能，GAFIS通过区分学习数据之间的更多差异达到了更高的智能水平。CFD和GAFIS预测的纳米流体温度值相同。
