Predicting Onflow Parameters Using Transfer Learning for Domain and Task Adaptation

TL;DR

This paper presents a transfer learning approach using convolutional neural networks to predict onflow parameters like angle of attack and speed, enabling adaptation to new domains and tasks in aerodynamic data analysis.

Contribution

The study introduces a transfer learning methodology for CNNs that allows real-time adaptation to different data distributions and prediction tasks in aerodynamic parameter estimation.

Findings

Effective domain and task adaptation demonstrated

Transfer learning improves prediction accuracy in new domains

No significant improvement with noisy data

Abstract

Determining onflow parameters is crucial from the perspectives of wind tunnel testing and regular flight and wind turbine operations. These parameters have traditionally been predicted via direct measurements which might lead to challenges in case of sensor faults. Alternatively, a data-driven prediction model based on surface pressure data can be used to determine these parameters. It is essential that such predictors achieve close to real-time learning as dictated by practical applications such as monitoring wind tunnel operations or learning the variations in aerodynamic performance of aerospace and wind energy systems. To overcome the challenges caused by changes in the data distribution as well as in adapting to a new prediction task, we propose a transfer learning methodology to predict the onflow parameters, specifically angle of attack and onflow speed. It requires first training a convolutional neural network (ConvNet) model offline for the core prediction task, then freezing the weights of this model except the selected layers preceding the output node, and finally executing transfer learning by retraining these layers. A demonstration of this approach is provided using steady CFD analysis data for an airfoil for i) domain adaptation where transfer learning is performed with data from a target domain having different data distribution than the source domain and ii) task adaptation where the prediction task is changed. Further exploration on the influence of noisy data, performance on an extended domain, and trade studies varying sampling sizes and architectures are provided. Results successfully demonstrate the potential of the approach for adaptation to changing data distribution, domain extension, and task update while the application for noisy data is concluded to be not as effective.