Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements

TL;DR

This paper introduces a machine learning framework that reconstructs low-order flow fields and quantifies uncertainties from sparse pressure data, enhancing real-time aerodynamic predictions under gust conditions.

Contribution

It develops a probabilistic deep learning approach combining Monte Carlo dropout and learned noise models for uncertainty quantification in flow reconstruction.

Findings

Effective uncertainty modeling improves prediction reliability.

Dual uncertainty approach captures both model and measurement noise.

Framework demonstrates accuracy and efficiency in gust-encounter scenarios.

Abstract

This paper presents a novel machine-learning framework for reconstructing low-order gust-encounter flow field and lift coefficients from sparse, noisy surface pressure measurements. Our study thoroughly investigates the time-varying response of sensors to gust-airfoil interactions, uncovering valuable insights into optimal sensor placement. To address uncertainties in deep learning predictions, we implement probabilistic regression strategies to model both epistemic and aleatoric uncertainties. Epistemic uncertainty, reflecting the model's confidence in its predictions, is modeled using Monte Carlo dropout, as an approximation to the variational inference in the Bayesian framework, treating the neural network as a stochastic entity. On the other hand, aleatoric uncertainty, arising from noisy input measurements, is captured via learned statistical parameters, which propagates measurement noise through the network into the final predictions. Our results showcase the efficacy of this dual uncertainty quantification strategy in accurately predicting aerodynamic behavior under extreme conditions while maintaining computational efficiency, underscoring its potential to improve online sensor-based flow estimation in real-world applications.