2019 # Prediction of aerodynamic flow fields using convolutional neural networks

**Abstract:** An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen flow conditions and geometries given the pixelated shape of the object. In particular, we consider Reynolds Averaged Navier-Stokes (RANS) flow solutions over airfoil shapes. The CNN can automatically detect essential features with minimal human supervision and shown to effectively estimate the velocity and pressure field orders of magn…

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“…For this purpose, inputs (iii) and (iv) are provided to the CNN in the form of distance functions for objects and for monopoles. Such an approach has previously been used for CNNs to predict steady-state flow fields [ 3 , 7 ]. The distance functions are defined by i.e., for each cell with location ( i , j ) in a domain the minimal distances and to the boundary of an object and to a monopole M are determined.…”

confidence: 99%

“…For this purpose, inputs (iii) and (iv) are provided to the CNN in the form of distance functions for objects and for monopoles. Such an approach has previously been used for CNNs to predict steady-state flow fields [ 3 , 7 ]. The distance functions are defined by i.e., for each cell with location ( i , j ) in a domain the minimal distances and to the boundary of an object and to a monopole M are determined.…”

confidence: 99%

“…For this purpose, inputs (iii) and (iv) are provided to the CNN in the form of distance functions Φ o for objects and Φ m for monopoles. Such an approach has previously been used for CNNs to predict steady-state flow fields [3,7]. The distance functions are defined by…”

confidence: 99%

“…Despite the disadvantage of a large amount of data needed for training, ANNs appears in several works [4][5][6] in the field of loads and damage monitoring in the helicopter area. In the last few decades, these techniques have become widely used for force reconstruction [7,8] and aerodynamic flow interaction [9,10] problems. One of the main advantages of ANN is that no physical model is required behind the application of the methodology.…”

confidence: 99%