Wavelet-based surrogate time series for multiscale simulation of heterogeneous catalysis

被引:30
|
作者
Gur, Sourav [1 ]
Danielson, Thomas [2 ]
Xiong, Qingang [2 ]
Hin, Celine [3 ]
Pannala, Sreekanth [4 ]
Frantziskonis, George [1 ]
Savara, Aditya [2 ]
Daw, C. Stuart [2 ]
机构
[1] Univ Arizona, Dept Civil Engn & Engn Mech, Tucson, AZ 85721 USA
[2] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA
[3] Virginia Polytech Inst & State Univ, 635 Prices Ford Rd, Blacksburg, VA 24060 USA
[4] SABIC, Sugar Land, TX 77478 USA
关键词
Wavelet based transformation; Random surrogates; Kinetic Monte Carlo; Temporal upscaling; Multiscale modeling of catalysis; STOCHASTIC SIMULATION; DIFFUSION PROBLEMS;
D O I
10.1016/j.ces.2016.01.037
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
We propose a wavelet-based scheme that encodes the essential dynamics of discrete microscale surface reactions in a form that can be coupled with continuum macroscale flow simulations with high computational efficiency. This makes it possible to simulate the dynamic behavior of reactor-scale heterogeneous catalysis without requiring detailed concurrent simulations at both the surface and continuum scales using different models. Our scheme is based on the application of wavelet-based surrogate time series that encodes the essential temporal and/or spatial fine-scale dynamics at the catalyst surface. The encoded dynamics are then used to generate statistically equivalent, randomized surrogate time series, which can be linked to the continuum scale simulation. We illustrate an application of this approach using two different kinetic Monte Carlo simulations with different characteristic behaviors typical for heterogeneous chemical reactions. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:165 / 175
页数:11
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