Numerical investigation and theoretical description of flame propagation of unsaturated hydrofluorocarbon refrigerants

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For the optimal use of next generation refrigerants and the development of future environmentally friendly alternatives, a fundamental understanding of their combustion properties is urgently needed. One of the most important metrics used to classify fire safety of refrigerants is the laminar burning velocity (LBV), which plays a central role in the safety assessment of flammable substances. For slow-burning flames, such as hydrofluorocarbon (HFC) refrigerants, which may propagate at flame speeds of less than 10 cm/s, it is very challenging to accurately determine LBVs due to the effects of buoyancy and radiation. The elimination of the former effect can only be obtained under microgravity conditions, which are not widely accessible . Further, for the safety assessment of HFC refrigerants, there is a strong need for robust experimental methods and post-processing techniques that provide accurate laminar flame speed data under normal gravity conditions. However, numerical simulations can greatly support the development, verification, and uncertainty quantification of such robust methods. This project aims at developing accurate and robust methods to better understand, measure, and predict the combustion properties and safety-related parameters of existing and future HFC refrigerants. The analysis is facilitated by high-fidelity detailed numerical simulations of HFC flames that account for buoyancy and radiation effects. The first objective is the joint evaluation of LBV with the experimental subproject 2. Simulation and analysis will contribute to reduce and quantify potential uncertainties in the evaluation of flame speeds from measurements. Further, reduced kinetic mechanisms of different size and fidelity will be developed to support numerical simulations and asymptotic analysis. The second objective is the development of models describing the effects of Markstein numbers on LBV with a focus on how transport phenomena in the flame are affected by differential diffusion. An existing adjoint-based framework will be extended to higher-order sensitivities to explain the impact of Markstein numbers on thermo-chemistry and transport mechanisms. The third objective is the development of reduced-order models based on machine-learning (ML) combined with structural group analysis to predict LBV and Markstein numbers for existing and future HFC refrigerants. The training data will consist of experimental data from subproject 2 supplemented with data from simulations and will be continuously refined during the project based on the Design-of-Experiment (DoE) approach. The resulting ML-model will be useful for the model-based design and safety assessment of HFC refrigerants.