  The calculation of heat transfer in fluid flow in simple flat channels is a
relatively easy task for various simulations methods. However, once the channel
geometry becomes more complex, numerical simulations become a bottleneck in
optimizing wall geometries. We present a combination of accurate numerical
simulations of arbitrary, non-flat channels and machine learning models
predicting drag coefficient and Stanton number. We show that convolutional
neural networks can accurately predict the target properties at a fraction of
the time of numerical simulations. We use the CNN models in a virtual
high-throughput screening approach to explore a large number of possible,
randomly generated wall architectures. We find that S-shaped channel geometries
are Pareto-optimal, a result which seems intuitive, but was not obvious before
analysing the data. The general approach is not only applicable to simple flow
setups as presented here, but can be extended to more complex tasks, such as
multiphase or even reactive unit operations in chemical engineering.
