Beschreibung:
<jats:title>Abstract</jats:title>
<jats:p>Deep learning has been widely utilized to accurately estimate the flow state from the sparse sensor measurements. Yet there is still a lack of understanding of the actual dimension of this type of regression problem. In this study, we propose an Autoencoder (AE) based estimation method to tackle the control-oriented, sensor-based flow estimation problem. This method encodes the input information into a few-nodes bottleneck, then decodes the compressed information to the estimated the flow state. This network stands for the least order representation of the input-output relationship. We choose the fluidic pinball as the benchmark problem which contains multiple-input and multiple-output. The rotations of three cylinders in the flow generates rich flow physics. Consequently, strong non-linearity arises between input measurements and output flow state. For fair comparison the Deep Neural Network (DNN) is also employed to highlight the influence of the encoding-decoding process in AE. The results show that when the flow is periodic, the input vector can be effectively encoded into a five-dimensional subspace without a significant loss of estimation accuracy. However, the five-dimensional encoding will eliminate partial information from the input vector when the forced flow becomes chaotic, resulting in a lower estimation accuracy than DNN.</jats:p>