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. 2021 Jan 15;11(1):1505. doi: 10.1038/s41598-021-81111-z.
Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.
本文采用计算流体力学 (CFD) 技术，对空气-水非平衡热条件下的鼓泡塔型反应器进行了机理建模和模拟。此外，自适应网络 (AN) 训练器与模糊推理系统 (FIS) 的组合作为人工智能方法调用 ANFIS，已经在 CFD 方法的优化中显示出了潜力。虽然基于粒子群优化 (PSO) 算法的人工智能方法模糊推理系统 (PSOFIS) 具有优化其他研究领域的良好背景，但在与 CFD 的合作方面尚无任何研究。PSOFIS 可以通过消除额外的 CFD 模拟来简化研究并减少所有困难。实际上，在获得最佳智能后，所有预测都可以由 PSOFIS 完成，而无需进行 CFD 建模所需的大量计算工作。本研究的第一个目标是开发用于 CFD 方法应用的 PSOFIS。第二个目标是比较 PSOFIS 和 ANFIS 以准确预测 CFD 结果。在本研究中，PSOFIS 用于学习 CFD 数据，以预测鼓泡塔内的水速度。研究了输入数量、种群大小和惯性权重的值，以获得最佳智能。一旦获得最佳智能，就无需在 CFD 域中细化网格。可以增加网格密度，并通过 PSOFIS 以更少的计算工作量更轻松地进行新的预测。为了进行强有力的验证，将 PSOFIS 在预测液体速度方面的结果与 ANFIS 的结果进行了比较。结果表明，对于相同的模糊集参数，PSOFIS 的预测比 ANFIS 更接近 CFD。PSOFIS 的回归数 (R)（0.98）略高于 ANFIS（0.97）。PSOFIS 具有在从 9477 增加到 774,468 的网格密度增量和独立于 CFD 建模的新节点的精确预测方面的强大潜力。
