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

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. •An efficient wavelet-based surrogate algorithm is proposed for multiscale coupling.•Generated surrogates replicate stochastic features from complex surface reactions.•Catalytic reactions modeled by KMC are demonstrated for upscaling using surrogates.•Computational overhead can be significantly reduced by the multiscale coupling.