Sequential Regression of Finite-Time Lyapunov Exponents for Fast Identification of Fluid Transport Features

Computing finite-time Lyapunov exponents (FTLE) from experimental data is a computationally intensive task that is further complicated by measurement noise and disappearing particles. This work addresses these challenges by proposing regression as a means of computing the flow map Jacobian required in FTLE calculations. Further, it demonstrates that the new approach can be performed sequentially on particle snapshots typical of experimental data for improved performance. The theory supporting the new approach is discussed in relation to traditional methods using finite-differences and the method is applied to a range of flow configurations. Simulated data are used to study the influence of the method's parameters on outcomes and robustness to noise is examined with respect to traditional FTLE results.