Finite-rate chemistry modelling of non-conventional combustion regimes using a Partially-Stirred Reactor closure: combustion model formulation and implementation details

The present work focuses on the numerical simulation ofModerate or Intense Low oxygen Dilution combustion condition, using thePartially-Stirred Reactor model for turbulence-chemistry interactions.The Partially-Stirred Reactor model assumes that reactions are confinedin a specific region of the computational cell, whose mass fractiondepends both on the mixing and the chemical time scales. Therefore, theappropriate choice of mixing and chemical time scales becomes crucial toensure the accuracy of the numerical simulation prediction. Results showthat the most appropriate choice for mixing time scale in Moderate orIntense Low oxygen Dilution combustion regime is to use a dynamicevaluation, in which the ratio between the variance of mixture fractionand its dissipation rate is adopted, rather than global estimations basedon Kolmogorov or integral mixing scales. This is supported by thevalidation of the numerical results against experimental profiles oftemperature and species mass fractions, available from measurements onthe Adelaide Jet in Hot Co-flow burner. Different approaches for chemicaltime scale evaluation are also compared, using the species formationrates, the reaction rates and the eigenvalues of the formation rateJacobian matrix. Different co-flow oxygen dilution levels and Reynoldsnumbers are considered in the validation work, to evaluate theapplicability of Partially-Stirred Reactor approach over a wide range ofoperating conditions. Moreover, the influence of specifying uniform andnon-uniform boundary conditions for the chemical scalars is assessed. Thepresent work sheds light on the key mechanisms of turbulence-chemistryinteractions in advanced combustion regimes. At the same time, itprovides essential information to advance the predictive nature ofcomputational tools used by scientists and engineers, to support thedevelopment of new technologies.This work has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska- Curie grant agreement No 643134.