Comparative Investigation of the High Pressure Autoignition of the Butanol Isomers

Investigation of the autoignition delay of the butanol isomers has been performed at elevated pressures of 15 bar and 30 bar and low to intermediate temperatures of 680-860 K. The reactivity of the stoichiometric isomers of butanol, in terms of inverse ignition delay, was ranked as n-butanol > sec-butanol ~ iso-butanol > tert-butanol at a compressed pressure of 15 bar but changed to n-butanol > tert-butanol > sec-butanol > iso-butanol at 30 bar. For the temperature and pressure conditions in this study, no NTC or two-stage ignition behavior were observed. However, for both of the compressed pressures studied in this work, tert-butanol exhibited unique pre-ignition heat release characteristics. As such, tert-butanol was further studied at two additional equivalence ratios ($\phi$ = 0.5 and 2.0) to help determine the cause of the heat release.Comment: 4 pages, 4 figures, presented at the 2011 Meeting of the Eastern States Sections of the Combustion Institut

Accelerating moderately stiff chemical kinetics in reactive-flow simulations using GPUs

The chemical kinetics ODEs arising from operator-split reactive-flow simulations were solved on GPUs using explicit integration algorithms. Nonstiff chemical kinetics of a hydrogen oxidation mechanism (9 species and 38 irreversible reactions) were computed using the explicit fifth-order Runge-Kutta-Cash-Karp method, and the GPU-accelerated version performed faster than single- and six-core CPU versions by factors of 126 and 25, respectively, for 524,288 ODEs. Moderately stiff kinetics, represented with mechanisms for hydrogen/carbon-monoxide (13 species and 54 irreversible reactions) and methane (53 species and 634 irreversible reactions) oxidation, were computed using the stabilized explicit second-order Runge-Kutta-Chebyshev (RKC) algorithm. The GPU-based RKC implementation demonstrated an increase in performance of nearly 59 and 10 times, for problem sizes consisting of 262,144 ODEs and larger, than the single- and six-core CPU-based RKC algorithms using the hydrogen/carbon-monoxide mechanism. With the methane mechanism, RKC-GPU performed more than 65 and 11 times faster, for problem sizes consisting of 131,072 ODEs and larger, than the single- and six-core RKC-CPU versions, and up to 57 times faster than the six-core CPU-based implicit VODE algorithm on 65,536 ODEs. In the presence of more severe stiffness, such as ethylene oxidation (111 species and 1566 irreversible reactions), RKC-GPU performed more than 17 times faster than RKC-CPU on six cores for 32,768 ODEs and larger, and at best 4.5 times faster than VODE on six CPU cores for 65,536 ODEs. With a larger time step size, RKC-GPU performed at best 2.5 times slower than six-core VODE for 8192 ODEs and larger. Therefore, the need for developing new strategies for integrating stiff chemistry on GPUs was discussed.Comment: 27 pages, LaTeX; corrected typos in Appendix equations A.10 and A.1

UConnRCMPy: Python-based data analysis for rapid compression machines

The ignition delay of a fuel/air mixture is an important quantity in designing combustion devices, and these data are also used to validate chemical kinetic models for combustion. One of the typical experimental devices used to measure the ignition delay is called a Rapid Compression Machine (RCM). This paper presents UConnRCMPy, an open-source Python package to process experimental data from the RCM at the University of Connecticut. Given an experimental measurement, UConnRCMPy computes the thermodynamic conditions in the reaction chamber of the RCM during an experiment along with the ignition delay. UConnRCMPy implements an extensible framework, so that alternative experimental data formats can be incorporated easily. In this way, UConnRCMPy improves the consistency of RCM data processing and enables the community to reproduce data analysis procedures.Comment: 8 pages, 3 figures, presented at the 10th US National Combustion Meetin