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

Sourav Gur, Thomas Danielson, Qingang Xiong, Celine Hin, Sreekanth Pannala, George Frantziskonis, Aditya Savara, C. Stuart Daw

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)165-175
Number of pages11
JournalChemical Engineering Science
StatePublished - Apr 22 2016


  • Kinetic Monte Carlo
  • Multiscale modeling of catalysis
  • Random surrogates
  • Temporal upscaling
  • Wavelet based transformation

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering


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