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

• URL: http://arxiv.org/abs/2008.03157v2
• Date: Fri, 5 Mar 2021 15:11:40 GMT
• Title: A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction
• Authors: Shady Ahmed, Suraj Pawar, Omer San, Adil Rasheed, Mandar Tabib
• Abstract summary: Long short-term memory (LSTM) nudging framework for enhancement of reduced order models (ROMs) of fluid flows utilized noisy measurements for air traffic improvements. We build on the fact that in realistic application, there are uncertainties in initial and boundary conditions, model parameters, as well as measurements. In the presented LSTM nudging (LSTM-N) approach, we fuse forecasts from a combination of imperfect GROM and uncertain state estimates, with sparseian sensor measurements to provide more reliable predictions in a dynamical data assimilation framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements. Toward emerging applications of digital twins in aviation, the proposed approach allows for constructing a realtime predictive tool for wake-vortex transport and decay systems. We build on the fact that in realistic application, there are uncertainties in initial and boundary conditions, model parameters, as well as measurements. Moreover, conventional nonlinear ROMs based on Galerkin projection (GROMs) suffer from imperfection and solution instabilities, especially for advection-dominated flows with slow decay in the Kolmogorov width. In the presented LSTM nudging (LSTM-N) approach, we fuse forecasts from a combination of imperfect GROM and uncertain state estimates, with sparse Eulerian sensor measurements to provide more reliable predictions in a dynamical data assimilation framework. We illustrate our concept by solving a two-dimensional vorticity transport equation. We investigate the effects of measurements noise and state estimate uncertainty on the performance of the LSTM-N behavior. We also demonstrate that it can sufficiently handle different levels of temporal and spatial measurement sparsity, and offer a huge potential in developing next-generation digital twin technologies.

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