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- 2016
模糊控制技术在SIMPLER算法中的应用及求解性能分析
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Abstract:
为了提高SIMPLER算法在三维流动问题上的求解性能,引入模糊控制方法来自动调控速度亚松弛因子的大小.在数值计算过程中,将相邻两个迭代层次上的最大动量残差比值作为模糊控制输入量,速度亚松弛因子的变化量作为模糊控制输出量,基于最大动量残差的变化趋势可实现速度亚松弛因子的自动调控,从而达到加快收敛的目的.最后,通过3个经典的流动问题验证了模糊控制方法的优越性.研究表明:当初始亚松弛因子为最不利值时,模糊控制方法的收敛速度约是固定松弛因子方法的5～30倍;当初始亚松弛因子为最佳值时,模糊控制方法迭代次数与固定松弛因子方法迭代次数之比为0.7～2.0,收敛速度相差不大;采用模糊控制方法后,SIMPLER算法在不同初始亚松弛因子下均能得到高速收敛的解,同时健壮性也显著提高.研究工作将为大幅提升SIMPLER算法在三维流动问题上的求解性能起到重要作用.
In order to enhance the solving performance of the SIMPLER algorithm for three？？dimensional fluid flow problems, a fuzzy control method was introduced to automatically adjust the value of the velocity under？？relaxation factor. The ratio of the maximum momentum residuals of two successive iteration levels is used as the input variable of the fuzzy control, and the variation of the velocity under？？relaxation factor is taken as the output variable of the fuzzy control. Based on the changing trend of the maximum momentum residual, the velocity under？？relaxation factor could be adjusted for accelerating the iteration convergence. Finally, the fuzzy control method was evaluated by solving three classic fluid flow problems. It could be concluded that when the initial under？？relaxation factor is set at its most unfavorable value, the convergence rate of the fuzzy control method is about 5？？30 times of the fixed relaxation factor method; however, when the initial under？？relaxation factor is at its optimum value, the ratio of the iteration number of the fuzzy control method to the fixed relaxation factor method is 0.7？？2.0 and there is a little difference for the convergence rates between the two methods. The SIMPLER algorithm using fuzzy control method could not only always get solutions with high convergence rate under different initial under？？relaxation factors, but also possess much better robustness. Therefore, this research is of great significance in improving the solving performance of the SIMPLER algorithm for three？？dimensional fluid flow problems
| [1] | ［2］PATANKAR S V. A calculation procedure for two？？dimensional elliptic situations ［J］. Numerical Heat Transfer, 1981, 4(4): 409？？425. |
| [2] | LIU Xiaomin, LI Shuo. Effects of bionic volute tongue bioinspired by leading edge of owl wing on aerodynamic performance and noise of multi？？blade centrifugal fan ［J］. Journal of Xi’an Jiaotong University, 2015, 49(1): 14？？20. |
| [3] | ［5］周晓斯, 王元, 李志强. 近床面风沙流的颗粒拟流体大涡模拟分析 ［J］. 西安交通大学学报, 2014, 48(1): 60？？66. |
| [4] | ［6］文键, 杨辉著, 杜冬冬, 等. 螺旋折流板换热器换热强化的数值研究 ［J］. 西安交通大学学报, 2014, 48(9): 43？？48. |
| [5] | WEN Jian, YANG Huizhu, DU Dongdong, et al. Numerical simulation for heat transfer enhancement of a heat exchanger with helical baffles ［J］. Journal of Xi’an Jiaotong University, 2014, 48(9): 43？？48. |
| [6] | ［12］DRAGOJLOVIC Z, KAMINSKI D A. A fuzzy logic algorithm for acceleration of convergence in solving turbulent flow and heat transfer problems ［J］. Numerical Heat Transfer: Part B, 2004, 46(4): 301？？327. |
| [7] | ［13］JAIN A, KAMINSKI D A. Effect of fin number and position on thermal behavior of natural convection in enclosed cavity using fuzzy controller algorithm ［C］∥Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence. Piscataway, NJ, USA: IEEE, 2007: 516？？522. |
| [8] | ［16］LI Z Y, TAO W Q. A new stability？？guaranteed second？？order difference scheme ［J］. Numerical Heat Transfer: Part B, 2002, 42(4): 349？？365. |
| [9] | ［17］张乃尧, 阎平凡. 神经网络与模糊控制 ［M］. 北京: 清华大学出版社, 1998. |
| [10] | ［8］LIU X L, TAO W Q, ZHENG P, et al. Control of convergence in a computational fluid dynamic simulation using fuzzy logic ［J］. Science in China, 2002, 45(5): 495？？502. |
| [11] | ［9］RYOO J, KAMINSKI D A, DRAGOJLOVIC Z. A residual？？based fuzzy logic algorithm for control of convergence in a computational fluid dynamic simulation ［J］. ASME Journal of Heat Transfer, 1999, 121(4): 1076？？1078. |
| [12] | ［10］RYOO J, DRAGOJLOVIC Z, KAMINSKI D A. Control of convergence in a computational fluid dynamics simulation using ANFIS ［J］. IEEE Transactions on Fuzzy Systems, 2005, 13(1): 42？？47. |
| [13] | ［11］DRAGOJLOVIC Z, KAMINSKI D A, RYOO J, et al. Tuning of a fuzzy rule set for controlling convergence of a CFD solver in turbulent flow ［J］. International Journal of Heat and Mass Transfer, 2001, 44(20): 3811？？3822. |
| [14] | ［14］JAIN A, KAMINSKI D A. Using cognitive computing to extend the range of Grashof number in numerical simulation of natural convection ［J］. International Journal of Heat and Mass Transfer, 2009, 52(15): 3446？？3455. |
| [15] | ［1］PATANKAR S V, SPALDING D B. A calculation procedure for heat, mass and momentum transfer in three dimensional parabolic flows ［J］. International Journal of Heat and Mass Transfer, 1972, 15(10): 1787？？1806. |
| [16] | ［4］施东晓, 毕勤成, 周荣启. 磁性液体两相界面演变特性的数值研究 ［J］. 西安交通大学学报, 2014, 48(9): 123？？129. |
| [17] | SHI Dongxiao, BI Qincheng, ZHOU Rongqi. Numerical investigation on the evolution characteristics of two？？phase interface in ferrofluids ［J］. Journal of Xi’an Jiaotong University, 2014, 48(9): 123？？129. |
| [18] | ［15］陶文铨. 数值传热学 ［M］. 2版. 西安: 西安交通大学出版社, 2001. |
| [19] | ［3］刘小民, 李烁. 仿？^翼前缘蜗舌对多翼离心风机气动性能和噪声的影响 ［J］. 西安交通大学学报, 2015, 49(1): 14？？20. |
| [20] | ZHOU Xiaosi, WANG Yuan, LI Zhiqiang. Granular pseudo？？fluid large？？eddy simulation study of aeolian sand transport near a bed surface ［J］. Journal of Xi’an Jiaotong University, 2014, 48(1): 60？？66. |
| [21] | ［7］赵曙, 朱惠人, 郭涛, 等. 旋转带肋回转通道流动换热数值模拟 ［J］. 西安交通大学学报, 2014, 48(2): 125？？130. |
| [22] | ZHAO Shu, ZHU Huiren, GUO Tao, et al. Numerical predictions of flow and heat transfer for rotating internal cooling channels with rib turbulators ［J］. Journal of Xi’an Jiaotong University, 2014, 48(2): 125？？130. |
| [23] | ［18］TANG L Q, CHENG T, TSANG T T H. Transient solutions for three？？dimensional lid？？driven cavity flows by a least？？squares finite element method ［J］. International Journal for Numerical Methods in Fluids, 1995, 21(5): 413？？432. |
