A novel linear transformation model for the analysis and optimisation of chemical kinetics

Abstract

In this study a novel model for the analysis and optimisation of numerical and experimental chemical kinetics is developed. Concentration time profiles of non-diffusive chemical kinetic processes and flame speed profiles of fuel oxidiser mixtures can be described by certain characteristic points, so that relations between the coordinates of these points and input parameters of chemical kinetic models become almost linear. This linear transformation model simplifies the analysis of chemical kinetic models, hence creating a robust global sensitivity analysis and allowing a quick optimisation and reduction of these models. Firstly, in this study the model is extensively validated by the optimisation of a syngas combustion model with a large data set of imitated ignition experiments. The optimisation with the linear transformation model is quick and accurate, revealing the potential to decrease numerical costs of the optimisation process by at least one order of magnitude, compared to established methods. Additionally, the optimisation on this data set demonstrates the capability of predicting reaction rate coefficients, more accurately than by currently known confidence intervals. In a first application, methane combustion models are optimised with a small experimental set, consisting of OH(A) and CH(A) concentration profiles from shock tube ignition experiments, species profiles from flow reactor experiments and laminar flame speeds. With the optimised models, especially the predictability for the flame speeds of mixtures of hydrogen, carbon monoxide and methane can be increased compared to established models. With the analysis of the optimised models, new information for the low pressure reaction coefficient of the fall-off reaction H+CH 3 (+M)<=>CH 4 (+M) is determined. In addition, the optimised combustion model is quickly and efficiently reduced to validate a new rapid reduction scheme for chemical kinetic models.

An optimised chemical kinetic model for the combustion of fuel mixtures of syngas and natural gas

“…The linear transformation model (linTM) -including its optimisation method -is described in detail in prior work [15] and is only reviewed briefly here. The linTM mainly consists of two basic concepts.…”

“…Thereby, the computational costs of this approximation increases exponentially with the number of optimised model parameters, drastically limiting the efficiency of research in this field of optimisation. To overcome this limitation, the novel linear transformation model (linTM) can be applied [15]. With the linTM the relations between model parameters and output parameters of the solution space are linearised, at the same time keeping a high accuracy of this linear approximation.…”

Future Fuels—Analyses of the Future Prospects of Renewable Synthetic Fuels

“…Since the RMG method included all possible isomers of the different species-for which many are not impacting the combustion characteristics-a reduction of the model was performed. This was done by applying the rapid reduction method of the linear transformation model (linTM) [65]. As a result, the model was reduced to 133 species and 1200 reactions [63], allowing computationally more efficient subsequent numerical investigations.…”

“…In this study, the reduced model was optimized on the ignition delay times to increase the model prediction abilities. The optimization was performed on basis of the methods of the linTM [65]. For selection of the active reactions in the optimization, a global sensitivity analysis of the ignition delay times on basis of the linTM was performed.…”

Methods and tools for the characterisation of a generic jet fuel

“…For the reduction, unimportant species and reactions as well, are identified using the software package of Chemical Workbench® [33]. Another approach to reduce the size of a mechanism is the linear transformation model (linTM) [34] developed recently. Both applications allow the reduction of a mechanism, in particular the number of species, thus enabling its efficient use in CFD simulations.…”