152 research outputs found
Semi-supervised machine learning model for Lagrangian flow state estimation
In recent years, many researchers have demonstrated the strength of
supervised machine learning for flow state estimation. Most of the studies
assume that the sensors are fixed and the high-resolution ground truth can be
prepared. However, the sensors are not always fixed and may be floating in
practical situations -- for example, in oceanography and river hydraulics,
sensors are generally floating. In addition, floating sensors make it more
difficult to collect the high-resolution ground truth. We here propose a
machine learning model for state estimation from such floating sensors without
requiring high-resolution ground-truth data for training. This model estimates
velocity fields only from floating sensor measurements and is trained with a
loss function using only sensor locations. We call this loss function as a
"semi-supervised" loss function, since the sensor measurements are utilized as
the ground truth but high-resolution data of the entire velocity fields are not
required. To demonstrate the performance of the proposed model, we consider
Stokes' second problem and two-dimensional decaying homogeneous isotropic
turbulence. Our results reveal that the proposed semi-supervised model can
estimate velocity fields with reasonable accuracy when the appropriate number
of sensors are spatially distributed to some extent in the domain. We also
discuss the dependence of the estimation accuracy on the number and
distribution of sensors.Comment: 10 pages, 10 figure
Flow control by a hybrid use of machine learning and control theory
Flow control has a great potential to contribute to the sustainable society
through mitigation of environmental burden. However, high dimensional and
nonlinear nature of fluid flows poses challenges in designing efficient control
laws. This paper aims to propose a hybrid method (i.e., machine learning and
control theory) for feedback control of fluid flows. We propose a partially
nonlinear linear-system extraction autoencoder (pn-LEAE), which consists of
convolutional neural networks-based autoencoder (CNN-AE) and a custom layer to
extract a low-dimensional latent dynamics. This pn-LEAE basically extracts a
linear dynamical system so that the modern control theory can easily be
applied, but at the same time, it is designed to capture a nonlinear
development of the latent dynamics. We demonstrate the effectiveness of the
linear system extracted by the pn-LEAE, as well as the designed control law's
effectiveness for a flow around a circular cylinder at the Reynolds number of
. This is the first attempt utilizing CNN-AE for
linearization of fluid flows involving transient development to design a
feedback control law.Comment: 13 pages, 12 figure
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