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

TL;DR

This paper introduces an LSTM-based nudging framework to improve reduced order models for real-time wake-vortex transport and decay prediction, integrating noisy measurements and addressing uncertainties for digital twin applications.

Contribution

It presents a novel LSTM nudging approach that enhances ROM predictions by fusing imperfect models with uncertain measurements, suitable for real-time digital twin systems in aviation.

Findings

LSTM-N improves prediction accuracy under noisy conditions.

The approach handles sparse spatial and temporal measurements effectively.

It demonstrates potential for real-time digital twin applications in aviation.

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.