In this work, we apply a nonlinear chaos analysis to the two phase flow in polymer electrolyte fuel cell cathode air-delivery microchannels. The fuel cell voltage signal is analyzed using techniques designed to estimate invariants typical of deterministic systems with high sensitivity to initial conditions, such as chaotic two phase flow. Voltage data are taken under varying fuel cell operating conditions, and noise in the data is reduced using a nonlinear noise reduction algorithm. The chaotic strange attractor of the system is reconstructed in phase space using time-delay embedding. Correlation sums over the strange attractor are calculated to estimate the fractal correlation dimension of the system. Estimations of the Kolmogorov entropy provide an additional measure of the complexity of the strange attractor. The values of the chaotic invariants are compared across varying degrees of cathode flooding to discern how they change with two phase flow regimes and fuel cell operating conditions. Future work will involve leveraging the chaotic understanding of two phase flow with chaos control methods to increase the power stability.

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