application of machine learning to bring efficiency to costly experiments case of flame-extinction

Combustion instability induced by acoustic waves can lead to various undesired consequences, including thermal stress on the combustion chamber, noise, flame blow-off, flashback, vibrations, and even explosions. this paper employs the design of experiments approach to systematically gather reliable experimental data for predicting flame extinction. the acoustic power required at the moment of extinction is a crucial metric in understanding this phenomenon. four key features - frequency, equivalence ratio, wall diameter ratio, and reynolds number - serve as inputs for a machine learning (ml) model. considering the substantial cost of pure reaction gases and the potential damage to the acoustic driver in high-pressure conditions, it is imperative to intelligently select extinction test points. machine learning methods are employed to determine optimal acoustic power levels for these values. the collected data is utilized to train a selected supervised ml model, specifically the support vector regression (svr), to accurately predict the acoustic power level required for flame extinction in both methane and propane fuels. evaluation using the r-squared metric demonstrates the model s accuracy and robust performance across diverse conditions.