Dataset of a parameterized U-bend flow for Deep Learning Applications
Jens Decke Olaf Wünsch Bernhard Sick
This dataset contains 10,000 fluid flow and heat transfer simulations in
U-bend shapes. Each of them is described by 28 design parameters, which are
processed with the help of Computational Fluid Dynamics methods. The dataset
provides a comprehensive benchmark for investigating various problems and
methods from the field of design optimization. For these investigations
supervised, semi-supervised and unsupervised deep learning approaches can be
employed. One unique feature of this dataset is that each shape can be
represented by three distinct data types including design parameter and
objective combinations, five different resolutions of 2D images from the
geometry and the solution variables of the numerical simulation, as well as a
representation using the cell values of the numerical mesh. This third
representation enables considering the specific data structure of numerical
simulations for deep learning approaches. The source code and the container
used to generate the data are published as part of this work.