Fusing CFD and measurement data using transfer learning

TL;DR

This paper introduces a neural network-based transfer learning approach to fuse CFD simulation data and measurement data, improving accuracy and physical realism in aerodynamic analysis.

Contribution

It presents a novel non-linear data fusion method using neural networks and transfer learning, outperforming traditional linear POD-based methods.

Findings

Significant improvements over POD-based methods.

More physically accurate solutions near nonlinearities.

Provides solutions at arbitrary flow conditions.

Abstract

Aerodynamic analysis during aircraft design usually involves methods of varying accuracy and spatial resolution, which all have their advantages and disadvantages. It is therefore desirable to create data-driven models which effectively combine these advantages. Such data fusion methods for distributed quantities mainly rely on proper orthogonal decomposition as of now, which is a linear method. In this paper, we introduce a non-linear method based on neural networks combining simulation and measurement data via transfer learning. The network training accounts for the heterogeneity of the data, as simulation data usually features a high spatial resolution, while measurement data is sparse but more accurate. In a first step, the neural network is trained on simulation data to learn spatial features of the distributed quantities. The second step involves transfer learning on the measurement data to correct for systematic errors between simulation and measurement by only re-training a small subset of the entire neural network model. This approach is applied to a multilayer perceptron architecture and shows significant improvements over the established method based on proper orthogonal decomposition by producing more physical solutions near nonlinearities. In addition, the neural network provides solutions at arbitrary flow conditions, thus making the model useful for flight mechanical design, structural sizing, and certification. As the proposed training strategy is very general, it can also be applied to more complex neural network architectures in the future.