Monofractal and multifractal analysis of simulated heat release fluctuations in a spark ignition heat engine

Curto-Risso, P.L.; Medina, A.; Hernández, A. Calvo; Guzmán‐Vargas, L.; Angulo‐Brown, F.

doi:10.1016/j.physa.2010.08.024

Cited by 28 publications

(23 citation statements)

References 30 publications

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“…We shall quantitatively analyze this fact hereinafter. This behavior is qualitatively similar to those reported for real data [1] and agree with previous results [14], where we have studied the complexity of the heat release fluctuations by means of correlation dimension, monofractal and multifractal analyses. Our analyses have shown that over short scales, these fluctuations are characterized by the presence of short-term correlations, while for large scales, they resemble very irregular fluctuations with poor memory.…”

Section: Energetic Properties With Variabilitysupporting

confidence: 91%

“…The model also reproduces the main features of heat release cyclic variability when compared with experiments, as well as the evolution with the fuel ratio of the statistical parameters characterizing heat release time series and the corresponding return maps [16]. Furthermore, a fractal and a multifractal analysis of heat release was performed in [14].…”

Section: Simulated Model: Theoretical Backgroundmentioning

confidence: 88%

“…As recently shown, this deterministic scheme does not reproduce the experimental characteristics shown by heat release fluctuations. The incorporation of stochastic fluctuations on the characteristic length of unburned eddies during combustion, l t , is essential to reproduce the main features of heat release time series and their evolution with the fuel/air equivalence ratio [14,16]. All the results we present were obtained using isooctane as fuel.…”

Section: Simulated Model: Theoretical Backgroundmentioning

confidence: 99%

“…For analyzing this kind of fluctuation, many authors have reported extensive work relative to CV in spark-ignition engines by means of different statistical methods, such as return maps, recurrence maps, correlation coarse-grained entropy and sample entropy [1,[10][11][12][13]. Recently, Curto-Risso et al [14] and Sen et al [2,5] investigated the complex dynamics of cycle-to-cycle heat release variations in a spark-ignition engine by using fractal and multifractal methods.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, in thermodynamic optimization methods, these plots are widely used to get unified insights about the optimum performance regimes, widely acknowledged as those ranging between maximum power and maximum efficiency [18]. The structure of the paper is as follows: in Section 2, we give a brief background of the simulated model we use to evaluate the effects of the fluctuations [14,16]; Section 3 is devoted to presenting the results for the main statistical parameters characterizing the energetic properties; and in Section 4, we analyze the performance output functions and the power-efficiency plots. Finally, we summarize the main results and present conclusions of the work.…”

Section: Introductionmentioning

confidence: 99%

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Fluctuations in the Energetic Properties of a Spark-Ignition Engine Model with Variability

Curto-Risso

Medina

Hernández

et al. 2013

Entropy

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We study the energetic functions obtained in a simulated spark-ignited engine that incorporates cyclic variability through a quasi-dimensional combustion model. Our analyses are focused on the effects of the fuel-air equivalence ratio of the mixture simultaneously over the cycle-to-cycle fluctuations of heat release (Q R ) and the performance outputs, such as the power (P ) and the efficiency (η). We explore the fluctuant behavior for Q R , P and η related to random variations of the basic physical parameters in an entrainment or eddy-burning combustion model. P and η show triangle shaped first return maps, while Q R exhibits a structured map, especially at intermediated fuel-air ratios. Structure disappears to a considerable extent in the case of heat release and close-to-stoichiometry fuel-air ratios. By analyzing the fractal dimension to explore the presence of correlations at different scales, we find that whereas Q R displays short-range correlations for intermediate values of the fuel ratio, both P and η are characterized by a single scaling exponent, denoting irregular fluctuations. A novel noisy loop-shaped P vs. η plot for a large number of engine cycles Entropy 2013, 15 3278 is obtained. This plot, which evidences different levels of irreversibilities as the fuel ratio changes, becomes the observed loop P vs. η curve when fluctuations are disregarded, and thus, only the mean values for efficiency and power are considered.

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Section: Energetic Properties With Variabilitysupporting

confidence: 91%

Section: Simulated Model: Theoretical Backgroundmentioning

confidence: 88%

Section: Simulated Model: Theoretical Backgroundmentioning

confidence: 99%