《山东大学学报(理学版)》 ›› 2020, Vol. 55 ›› Issue (2): 109-117.doi: 10.6040/j.issn.1671-9352.0.2019.669
• • 上一篇
温晓1,刘琪2*,高振2,曾维新2,吕咸青1
WEN Xiao1, LIU Qi2*, GAO Zhen2, DON Wai-sun2, LYU Xian-qing1
摘要: 局部的非侵入式约化基模型用于模拟瑞利-泰勒不稳定性(Rayleigh-Taylor instability, RTI)随时间演化的过程,其中初始小扰动的振幅和时间可视为自由参数。约化基模型把解看作一组基函数的线性组合,其中,基函数由本征正交分解获得,人工神经网络用于建立参数与基函数系数之间的映射关系。由于RTI随着时间的增加,相应的结构越来越复杂,尤其是后期会产生小规模旋涡的卷曲结构,因此考虑将RTI分为早期发展(线性)和中后期(拟非线性和弱非线性体制)发展阶段,即分段考虑时间参数。将时间参数分为3、5、6段,局部的非侵入式约化基模型与全局的非侵入式约化基模型相比,在精度相似的情况下,计算时间最快可以提高4倍左右。
中图分类号:
[1] RAYLEIGH L. Investigation of the character of the equilibrium of an incompressible heavy fluid of variable density[J]. Proceedings of the London Mathematical Society, 1882, 14(1):170-177. [2] TAYLOR G. The instability of liquid surfaces when accelerated in a direction perpendicular to their planes(I)[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1950, 201(1065):192-196. [3] NITTMANN J, FALLE S A E G, GASKELL P H. The dynamical destruction of shocked gas clouds[J]. Monthly Notices of the Royal Astronomical Society, 1982, 201(4):833-847. [4] ISOBE H, MIYAGOSHI T, SHIBATA K, et al. Filamentary structure on the Sun from the magnetic Rayleigh-Taylor instability[J]. Nature, 2005, 434(7032):478-481. [5] YE W H, ZHANG W Y, CHEN G N, et al. Numerical simulations of the FCT method on Rayleigh-Taylor and Richtmyer-Meshkov instabilities[J]. Chinese Journal of Physics, 1998, 15(3):278-282. [6] LI X L, JIN B X, GLIMM J. Numerical study for the three dimensional Rayleigh-Taylor instability through the TVD/AC scheme and parallel computation[J]. Journal of Computational Physics, 1996, 126(2):343-355. [7] TRYGGVASON G, BUNNER B, ESMAEELI A, et al. A Front-Tracking method for the computations of multiphase flow[J]. Journal of Computational Physics, 2001, 169(2):708-759. [8] TARTAKOVSKY A M, MEAKIN P. A smoothed particle hydrodynamics model for miscible flow in three-dimensional fractures and the two-dimensional Rayleigh-Taylor instability[J]. Journal of Computational Physics, 2005, 207(2):610-624. [9] DUCHEMIN L, JOSSERAND C, CLAVIN P, et al. Asymptotic behavior of the Rayleigh-Taylor instability[J]. Physical Review Letters, 2005, 94(22): 224501. [10] COOK A W, DIMOTAKIS P E. Transition stages of Rayleigh-Taylor instability between miscible fluids[J]. Journal of Fluid Mechanics, 2001, 443:69-99. [11] ZHANG J, WANG L F, YE W H, et al. Weakly nonlinear incompressible Rayleigh-Taylor instability in spherical and planar geometries[J]. Physics of Plasmas, 2018, 25(2):022701. [12] ZHANG J, WANG L F, YE W H, et al. Weakly nonlinear incompressible Rayleigh-Taylor instability in spherical geometry[J]. Physics of Plasmas, 2017, 24(6):062703. [13] ZHANG J, WANG L F, YE W H, et al. Weakly nonlinear multi-mode Rayleigh-Taylor instability in two-dimensional spherical geometry[J]. Physics of Plasmas, 2018, 25(8):082713. [14] ZHAO K G, XUE C, WANG L F, et al. Two-dimensional thin shell model for the nonlinear Rayleigh-Taylor instability in spherical geometry[J]. Physics of Plasmas, 2019, 26(2):022710. [15] ITO K, RAVINDRAN S S. A reduced-order method for simulation and control of fluid flows[J]. Journal of Computational Physics, 1998, 143(2):403-425. [16] BUI T T, WILLCOX K, GHATTAS O. Model reduction for large-scale systems with high-dimensional parametric input space[J]. SIAM Journal on Scientific Computing, 2008, 30(6):3270-3288. [17] HESTHAVEN J S, UBBIALI S. Non-intrusive reduced order modeling of nonlinear problems using neural networks[J]. Journal of Computational Physics, 2018, 363:55-78. [18] AUDOUZE C, VUYST F D, NAIR P B. Reduced-order modeling of parameterized PDEs using time-space-parameter principal component analysis[J]. International Journal for Numerical Methods in Engineering, 2009, 80(8):1025-1057. [19] HESTHAVEN J S, ROZZA G, STAMM B. Certified reduced basis methods for parametrized partial differential equations[M]. Cham, Switzerland: Springer International Publishing, 2016. [20] BORGES R, CARMONA M, COSTA B, et al. An improved weighted essentially non-oscillatory scheme for hyperbolic conservation laws[J]. Journal of Computational Physics, 2008, 227(6):3191-3211. [21] BULJAK V. Inverse analyses with model reduction: proper orthogonal decomposition and radial basis functions for fast simulations[M]. Berlin: Springer, 2012: 85-139. [22] KOHONEN T. An introduction to neural computing[J]. Neural Networks, 1988, 1(1):3-16. [23] 刘冰,郭海霞. MATLAB神经网络超级学习手册[M]. 北京:人民邮电出版社,2014: 156-168. LIU Bing, GUO Haixia. Neural network super learning manual based on MATLAB[M]. Beijing: Posts and Telecom Press, 2014: 159-168. [24] CENATE C F, RANI B S, RAMADEVI R, et al. Optimization of the cascade feed forward back propagation network for defect classification in ultrasonic images[J]. Russian Journal of Nondestructive Testing, 2016, 52(10):557-568. [25] CASTRO M, COSTA B, DON W S. High order weighted essentially non-oscillatory WENO-Z schemes for hyperbolic conservation laws[J]. Journal of Computational Physics, 2011, 230(5):1766-1792. |
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