SenseDynamics

Predicting aerodynamics forces from sensor data

Started
May 1, 2020
Status
Completed
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Abstract

To advance state-of-the-art Machine Learning capabilities when creating precise surrogate models of transient nonlinear physical phenomena related to aerodynamics with known (quantified probability density function describing it) and traceable uncertainty. Create the capability to reconstruct such complex physical transient phenomenon with a minimum amount of discrete real sensorial input of finite and calibrated precision.

People

Collaborators

SDSC Team:
Christian Donner
Natasa Tagasovska
Guillaume Obozinski

PI | Partners:

EPFL, Geodetic Engineering Lab:

  • Dr. Iordan Doytchinov
  • Dr. Jan Skaloud

More info

EPFL, Unsteady Flow Diagnostics Lab:

  • Prof. Karen Mulleners
  • Dr. Guoshenge He
  • Dr. Daniel Fernex

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EPFL, Chair of Computational Mathematics and Simulation Science:

  • Prof. Jan Hesthaven
  • Dr. Qian Wang
  • Dr. Junming Duan

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Federal Institute of Metrology:

  • Dr. Henri Baumann

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description

Motivation

Aerodynamic navigation requires real-time capabilities to reliably infer and predict forces that act on the vehicle, based on sensor measurements. While physical simulations are complex and cannot provide fast predictions, surrogate Machine Learning models should be investigated to emulate the physics accurately.

Proposed Approach / Solution

We apply probabilistic time-series models to perform “now-casting” and “forecasting” the dynamics of a pitching airfoil subject to constant incoming wind. These models should detect model accurately the forces that act on the wing, and detect when there is danger of stall, i.e., flow disruption. Here we investigated several models, like linear regression, feed-forward neural networks, and hidden Markov models (HMM), that predict the lift force (force pushing the wing upwards) based on as few pressure sensors as possible placed on the wing. We found, that with a minimum of 4 sensors, one can predict accurately the lift force. In addition, the HMM model could even give acceptable predictions, if one of these sensors fails for some reason, without retraining the model.

Impact

As a proof-concept we showed, that probabilistic models can accurately predict the forces that act on the wing, and that they can extrapolate to unseen conditions. This is an essential step towards machine-learning-based autonomous navigation of flight vehicles.

Figure 1: An illustration of a stall development over an airfoil, as recorded in the experimental data. In the first row, each panel A – E represents one of five different snapshots of the airflow.  Every state is characterized by a significantly different behavior from the others (some exhibiting more turbulence as C and D, compared to A and B) which makes the prediction task challenging. Namely, as depicted in the second row, the machine learning model should be able to accurately predict a volatile output (compare grey and dashed red line), along with a confidence envelope around it (red band) which will faithfully capture the variability in the data.

Gallery

Annexe

Additional resources

Bibliography

  1. Deparday, J., Mulleners, K. (2019). Modeling the interplay between the shear layer and leading edge suction during dynamic stall Physics of Fluids 31(10), 107104. https://dx.doi.org/10.1063/1.5121312
  2. Mulleners, K., Raffel, M. (2013). Dynamic stall development Experiments in Fluids 54(2), 1469 1477. https://dx.doi.org/10.1007/s00348-013-1469-7
  3. Mulleners, K., Raffel, M. (2012). The onset of dynamic stall revisited Experiments in Fluids 52(3), 779 793. https://dx.doi.org/10.1007/s00348-011-1118-y

Publications

Donner, C.; Mishra, A.; Shimazaki, H. "A projected nonlinear state-space model for forecasting time series signals" International Journal of Forecasting 2025 View publication
Donner, C.; Tagasovska, N.; He, G.; Mulleners, K.; Shimazaki, H.; Obozinski, G. "Learning interpretable latent dynamics for a 2D airfoil system" RobustML workshop at ICLR 2021 2021 View publication

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