  We focus on a convolutional neural network (CNN), which has recently been
utilized for fluid flow analyses, from the perspective on the influence of
various operations inside the CNN considering some canonical regressions with
fluid flow data. We consider two types of the CNN-based fluid flow analyses; 1.
CNN metamodeling and 2. CNN autoencoder. For the first type of CNN with the
additional scalar inputs, which is one of the common forms of CNN for fluid
flow analysis, we investigate the influence of input placements in the CNN
training pipeline. As an example, the estimation of force coefficients of
laminar flows over a flat plate and two side-by-side cylinders are considered.
We find that care should be taken for the placement of additional scalar inputs
depending on the problems and the flows users handle. We then investigate the
influence of various parameters and operations on CNN performance, with the
utilization of autoencoder (AE). A two-dimensional turbulence is considered for
the demonstration of AE. The results of AE highly rely on the decaying nature.
The influence of padding operation at a convolutional layer is also
investigated. The zero padding shows reasonable ability compared to other
methods which account for boundary conditions of numerical data. Moreover, the
effect of the dimensional reduction/extension methods inside CNN is also
examined. The CNN model is robust to the dimension reduction operations, while
being sensitive to the dimensional-extension methods. The findings through the
paper can help us toward the practical uses of CNN-based fluid flow analyses.
