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Entropic characterization of cyclic variability in internal combustion engines using chaotic time series analysis

C.E.A. Finney, K. Nguyen
University of Tennessee
Knoxville TN 37996-2210

C.S. Daw
Oak Ridge National Laboratory
Knoxville TN 37932-6472

We present a method based on a maximum-likelihood estimator of Kolmogorov entropy to quantify the degree of cycle-to-cycle variation in internal combustion engines. This estimation can be applied to evenly sampled, continuous time series, such as cyclinder pressure traces, from which time-embedded trajectories can be reconstructed. Because the estimator is applied to reconstructed trajectories, it can account for nonlinear structure and high dimensionality in the measured variable.

We suggest that cycle-to-cycle combustion differences can be highlighted by focusing on windows that define trajectory segments representing significant combustion events and ignoring the remaining portions of the cycle. This windowing scheme removes motoring effects or bias during the exhaust and intake strokes in which certain thermodynamic variables are constrained by the physical motion of the piston in the cylinder and not by combustion effects.

We demonstrate the applicability of this windowed entropy estimator to experimental time series of pressure measurements of a four-stroke, one-cyclinder industrial spark-ignition engine.


Finney CEA, Nguyen K, Daw CS (1994). Entropic characterization of cyclic variability in internal combustion engines using chaotic time series analysis. Proceedings of the 32nd Japanese Combustion Symposium (Sendai JAPAN; 1994 November 21-23): 400-402.
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