Symbolic Analysis of the Cycle-to-Cycle Variability of a Gasoline–Hydrogen Fueled Spark Engine Model

Israel Reyes-Ramírez, Santiago D. Martínez-Boggio, Pedro L. Curto-Risso, Alejandro Medina, Antonio Calvo Hernández and Lev Guzmán-Vargas
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Israel Reyes-Ramírez: Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas (UPIITA)—Instituto Politécnico Nacional, Mexico City 07340, Mexico
Santiago D. Martínez-Boggio: Instituto de Ingeniería Mecánica y Producción Industrial, Universidad de la República, Montevideo 11300, Uruguay
Pedro L. Curto-Risso: Instituto de Ingeniería Mecánica y Producción Industrial, Universidad de la República, Montevideo 11300, Uruguay
Alejandro Medina: Departamento de Física Aplicada, Facultad de Ciencias, Universidad de Salamanca, Salamanca 37008, Spain
Antonio Calvo Hernández: Departamento de Física Aplicada, Facultad de Ciencias, Universidad de Salamanca, Salamanca 37008, Spain
Lev Guzmán-Vargas: Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas (UPIITA)—Instituto Politécnico Nacional, Mexico City 07340, Mexico

Energies, 2018, vol. 11, issue 4, 1-19

Abstract: An study of temporal organization of the cycle-to-cycle variability (CCV) in spark ignition engines fueled with gasoline–hydrogen blends is presented. First, long time series are generated by means of a quasi-dimensional model incorporating the key chemical and physical components, leading to variability in the time evolution of energetic functions. The alterations in the combustion process, for instance the composition of reactants, may lead to quantitative changes in the time evolution of the main engine variables. It has been observed that the presence of hydrogen in the fuel mixture leads to an increased laminar flame speed, with a corresponding decrease in CCV dispersion. Here, the effects of different hydrogen concentrations in the fuel are considered. First, it is observed that return maps of heat release sequences exhibit different patterns for different hydrogen concentrations and fuel–air ratios. Second, a symbolic analysis is used to characterize time series. The symbolic method is based on the probability of occurrence of consecutive states (a word) in a symbolic sequence histogram (SSH). Modified Shannon entropy is computed in order to determine the adequate word length. Results reveal the presence of non-random patterns in the sequences and soft transitions between states. Moreover, the general behavior of CCV simulations results and three types of synthetic noises: white, log-normal, and a noisy logistic map, are compared. This analysis reveals that the non-random features observed in heat release sequences are quite different from synthetic noises.

Keywords: spark-ignition engines; quasi-dimensional simulations; cycle-to-cycle variability; symbolic analysis; information theory; gasoline–hydrogen blends (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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