A comparative study of machine learning models for predicting the state of reactive mixing
B. Ahmmed, M.K. Mudunuru, S. Karra, S.C. James, V.V. Vesselinov
Journal of Computational Physics2021DOI 10.1016/j.jcp.2021.110147
Summary
Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass growth. To investigate the evolution of mixing dynamics under different scenarios (e.g.,anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios.