Abstract

The combination of Computational Fluid Dynamics (CFD) and Data Science is in the focus of many research projects (Brunton, 2019; Aulich, 2019). Especially the area of physics-informed machine learning aiming to replace CFD at least partially, hence, to save computational costs by inter- and extrapolating fluid fields, attracts much attention. But even before reaching these long-term goals, already today there are a lot of advantages in the wide field of Data Science, which are beneficial for day-to-day engineering tasks and in particular for CFD workstreams. Simulation setups are becoming more complex and memory demanding due to several trends towards more degrees of freedom, towards unsteady analyses, towards parameter space exploration and optimization. This results in a vast number of results and data, which needs to be postprocessed to enable engineers to digest and analyze the data and finally derive insights and gain deeper understanding. Although simulation and postprocessing, hence the data generation, can be automated, there is still manual effort needed to inspect the results to ensure the validity and to understand the underlying mechanisms. These tasks can be supported by methods and techniques from the field of data science. The presented work will show how data access and visualization can be simplified and improved with the help of interactive dashboards. Furthermore, it will be presented how image recognition can support engineers in detecting specific phenomena e.g., separation and how ML can be used to “save” the domain expert’s knowledge. Overall, this talk is an appeal to all engineers to invest some time for data management as this is the required base for all presented activities. Also, the presentation aims to raise awareness for methods and techniques, which can be applied easily and for free and can generate value by enabling faster knowledge transfer. References Brunton, S. L. (2019). Machine Learning for Fluid Mechanics. arXiv. Aulich, M. K. (2019). Surrogate estimations of complete flow fields of fan stage designs via deep neural networks. Proceedings of the ASME Turbo Expo.