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We put forth a long short-term memory (LSTM) nudging framework for theenhancement of reduced order models (ROMs) of fluid flows utilizing noisymeasurements for air traffic improvements. Toward emerging applications ofdigital twins in aviation, the proposed approach allows for constructing arealtime predictive tool for wake-vortex transport and decay systems. We buildon the fact that in realistic application, there are uncertainties in initialand boundary conditions, model parameters, as well as measurements. Moreover,conventional nonlinear ROMs based on Galerkin projection (GROMs) suffer fromimperfection and solution instabilities, especially for advection-dominatedflows with slow decay in the Kolmogorov width. In the presented LSTM nudging(LSTM-N) approach, we fuse forecasts from a combination of imperfect GROM anduncertain state estimates, with sparse Eulerian sensor measurements to providemore reliable predictions in a dynamical data assimilation framework. Weillustrate our concept by solving a two-dimensional vorticity transportequation. We investigate the effects of measurements noise and state estimateuncertainty on the performance of the LSTM-N behavior. We also demonstrate thatit can sufficiently handle different levels of temporal and spatial measurementsparsity, and offer a huge potential in developing next-generation digital twintechnologies.

A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction