An Experimental Kinetics Study of Isopropanol Pyrolysis and Oxidation behind Reflected Shock Waves
Isopropanol has potential as a future bio-derived fuel and is a promising substitute for ethanol in gasoline blends. Even so, little has been done in terms of high-temperature chemical kinetic speciation studies of this molecule. To this end, experiments were conducted in a shock tube using simultaneous CO and H2O laser absorption measurements. Water and CO formation during isopropanol pyrolysis was also examined at temperatures between 1127 and 2162 K at an average pressure of 1.42 atm. Species profiles were collected at temperatures between 1332 and 1728 K and at an average pressure of 1.26 atm for equivalence ratios of 0.5, 1.0, and 2.0 in highly diluted mixtures of 20% helium and 79.5% argon. Species profiles were also compared to four modern C3 alcohol mechanisms, including the impact of recent rate constant measurements. The Li et al. (2019) and Saggese et al. (2021) models both best predict CO and water production under pyrolysis conditions, while the AramcoMech 3.0 and Capriolo and Konnov models better predict the oxidation experimental profiles. Additionally, previous studies have collected ignition delay time (τign) data for isopropanol but are limited to low pressures in highly dilute mixtures. Therefore, real fuel–air experiments were conducted in a heated shock tube with isopropanol for stoichiometric and lean conditions at 10 and 25 atm between 942 and 1428 K. Comparisons to previous experimental results highlight the need for real fuel–air experiments and proper interpretation of shock-tube data. The AramcoMech 3.0 model over predicts τign values, while the Li et al. model severely under predicts τign. The models by Capriolo and Konnov and Saggese et al. show good agreement with experimental τign values. A sensitivity analysis using these two models highlights the underlying chemistry for isopropanol combustion at 25 atm. Additionally, modifying the Li et al. model with a recently measured reaction rate shows improvement in the model’s ability to predict CO and water profiles during dilute oxidation. Finally, a regression analysis was performed to quantify τign results from this study.