Dataset of a parameterized U-bend flow for Deep Learning Applications
This provides a domain-specific resource for researchers in fluid dynamics and machine learning to test and develop methods for design optimization problems.
The authors introduced a dataset of 10,000 parameterized U-bend flow simulations to serve as a benchmark for design optimization, enabling the use of supervised, semi-supervised, and unsupervised deep learning approaches.
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.