Screening gas‐phase chemical kinetic models: Collision limit compliance and ultrafast timescales

Abstract

Detailed gas‐phase chemical kinetic models are widely used in combustion research, and many new mechanisms for different fuels and reacting conditions are developed each year. Recent works have highlighted the need for error checking when preparing such models, but a useful community tool to perform such analysis is missing. In this work, we present a simple online tool to screen chemical kinetic mechanisms for bimolecular reactions exceeding collision limits. The tool is implemented on a user‐friendly website, cloudflame.kaust.edu.sa, and checks three different classes of bimolecular reactions; (ie, pressure independent, pressure‐dependent falloff, and pressure‐dependent PLOG). In addition, two other online modules are provided to check thermodynamic properties and transport parameters to help kinetic model developers determine the sources of errors for reactions that are not collision limit compliant. Furthermore, issues related to unphysically fast timescales can remain an issue even if all bimolecular reactions are within collision limits. Therefore, we also present a procedure to screen ultrafast reaction timescales using computational singular perturbation. For demonstration purposes only, three versions of the rigorously developed AramcoMech are screened for collision limit compliance and ultrafast timescales, and recommendations are made for improving the models. Larger models for biodiesel surrogates, tetrahydropyran, and gasoline surrogates are also analyzed for exemplary purposes. Numerical simulations with updated kinetic parameters are presented to show improvements in wall‐clock time when resolving ultrafast timescales.

The chemical dynamics of hydrogen/hydrogen peroxide blends diluted with steam at compression ignition relevant conditions

“…• the timescale participation index (TPI), which identifies the reactions mostly related to each mode's timescale [129][130][131][132]; thus, for each CSP mode, the TPIs of all reactions are calculated and the largest ones (in absolute value) are selected; when positive, the related reaction favors the explosive character of the mode's timescale, while when negative the associated reaction tends to dissipate the related timescale [133]. This tool will be mainly used to identify the reactions mostly related to the system's fast explosive timescale (τ e,f ), i.e., the system's characteristic timescale, which controls the ignition delay time [121,[134][135][136][137][138][139].…”

Identifying the mechanism of formation of chlorinated silane polymer by‐products during chemical vapor infiltration of SiC from CH3SiCl3/H2

“…Before modeling, we first verified the compliance of the UT2017 model in terms of collision limit by CLC_script 24 because some errors in thermodynamic data and reaction rates are sometimes historically present in some reaction models, 24,25 which occurs collision limit violations. It was found in UT2017 that three forward reactions and four reverse reactions exceeded the collision limit; therefore, these were modified.…”

Computational analysis of the effect of hydrogen peroxide addition on premixed laminar hydrogen/air flames

“…Interested in leading order accuracy, the CSP basis vectors a i , b i can be approximated by the right (α i ) and left (β i ), respectively, eigenvectors of the Jacobian J of g(z) [31,34,[48][49][50]. Apart from an amplitude, each CSP mode is described by a timescale which is defined as the inverse norm of the associated eigenvalue, i.e., τ i = |λ i | −1 , where [51][52][53]. A CSP mode is characterised as explosive if the associated eigenvalue is positive and dissipative otherwise [50].…”