Master Thesis
05-2017 - 12-2017 Energia / Materiali / Meccanica Development of a fractal predictive model for an ICE.
-> GT-Power model setup;
-> Calibration of some combustion coefficients on GT-Power, in order
to match with the experimental data:
- burned mass fraction curves, in-cylinder pressure and HRR have been analysed for the calibration;
-> Data analysis on Excel and MatLab:
- computation and analysis of the errors between the experimental layout and our model in terms of maximum pressure, crank angle at 50% of burned mass fraction, crank angle inteval between (10-90)% of burned mass fraction;
-> Collection of some proper variables to design the predictive model:
- statistical analysis on the calibrated variables and some physical quantities, such as pressure or temperature at spark advance, residuals mass fraction, engine speed, turbulence intensity, etc., in order to evaluate the best correlation coefficients between the model parameters and these physical quantities; in other words, understand which of those selected physical quantities are suitable for the development of the predictive model;
-> development of the predictive model on MatLab:
- evaluate the predictive combustion coefficients through the calibrated ones;
- analysis of the errors between the calibrated model and the predictive one;
-> adjustment of some parameters in order to reduce the errors with
respect to the experimental layout, on GT-Power;
-> final analysis of the results.