Data Science & Computational Intelligence Research Group, Universitas Medan Area, Medan, Indonesia.
Data Science & Computational Intelligence Research Group, Universitas Sumatera Utara, Medan, Indonesia.
Sci Rep. 2021 Oct 25;11(1):20973. doi: 10.1038/s41598-021-00279-6.
This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of AlO/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m, but there is no data for the wall heat flux of 95 w/m. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m). The intelligence of PSOFIS is checked by the coefficient of determination (R pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation.
本文主要关注人工智能在纳米流体流动数值建模中的应用和性能。在基液中悬浮金属纳米粒子显示出在增强基于基液的传热方面的潜力。已有许多数值研究致力于调查纳米流体的热和流体动力学特性。然而，在任何研究中都没有考虑通过人工智能 (AI) 算法来优化计算流体动力学 (CFD) 建模。从准确性的角度来看，CFD 是一种强大的技术。然而，它可能需要花费大量的时间和成本，特别是在大规模和复杂的问题中。人们期望人工智能算法的机器学习技术可以通过对 CFD 数据进行模式识别来改善这种 CFD 缺陷。一旦人工智能智能地找到 CFD 模式，就不需要进行 CFD 计算。在这项研究中，基于粒子群优化的模糊推理系统 (PSOFIS) 被用于预测加热管内 AlO/水湍流的速度分布。CFD 建模中的一个挑战问题是特定边界条件下的数据丢失。例如，CFD 数据可用于壁面热通量为 75、85、105 和 125 w/m，但不存在壁面热通量为 95 w/m 的数据。因此，PSOFIS 学习可用的 CFD 数据，并预测数据不可用的地方（即壁面热通量为 95 w/m）的速度分布。通过不同接受比 (AR) 和惯性权重阻尼比 (IWDR) 的决定系数 (R 模式) 检查 PSOFIS 的智能。分别为 0.7 和 0.99 时，获得最佳智能。在这种情况下，CFD 和 PSOFIS 预测的速度分布相匹配。作为 PSOFIS 的性能，对于 268 s 的学习时间，对 CFD 数据丢失的预测可以忽略不计（~1 s）。相比之下，每次模拟的 CFD 计算大约需要 600 s。
