Oak Ridge National Laboratory
Knoxville TN 37932-6472
University of Tennessee
Knoxville TN 37996-2210
University of California, San Diego
La Jolla CA 92093-0402
We describe methods based on data symbolization for evaluating irreversibility in time series. Our methods test hypotheses about the degree of irreversibility in noisy measurements of complex processes. A symbolic approach is attractive because it is computationally efficient, robust to noise, and provides a clear basis for establishing statistical confidence limits. Speed and robustness are important for experimental applications where on-line diagnostics and control are desired. Our results are also relevant to the question of selecting alternative models for explaining observed data and for fitting model parametric values. We illustrate our points with output from numerical models and experimental data from combustion and multiphase-flow systems of engineering interest.
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