FluidDyn: A Python Open-Source Framework for Research and Teaching in Fluid Dynamics by Simulations, Experiments and Data Processing

Journal of Open Research Software

Ashwin Vishnu Mohanan¹  Pierre Augier²  Cyrille Bonamy² 

[1] Linné Flow Centre, Department of Mechanics, KTH, Stockholm;University of Grenoble Alpes, CNRS, Grenoble INP, LEGI, Grenoble;

关键词: Fluid dynamics research with Python;    Numerical simulations;    Laboratory experiments;    Free and open-source software;    modular;    object-oriented;    collaborative;    efficient;    tested;    documented;   

DOI  :  10.5334/jors.237

来源: DOAJ

【 摘 要 】

FluidDyn is a project to foster open-science and open-source in the fluid dynamics community. It is thought of as a research project to channel open-source dynamics, methods and tools to do science. We propose a set of Python packages forming a framework to study fluid dynamics with different methods, in particular laboratory experiments (package fluidlab), simulations (packages fluidfft, fluidsim and fluidfoam) and data processing (package fluidimage). In the present article, we give an overview of the specialized packages of the project and then focus on the base package called fluiddyn, which contains common code used in the specialized packages. Packages fluidfft and fluidsim are described with greater detail in two companion papers [4, 5]. With the project FluidDyn, we demonstrate that specialized scientific code can be written with methods and good practices of the open-source community. The Mercurial repositories are available in Bitbucket (https://bitbucket.org/fluiddyn/). All codes are documented using Sphinx and Read the Docs, and tested with continuous integration run on Bitbucket Pipelines and Travis. To improve the reuse potential, the codes are as modular as possible, leveraging the simple object-oriented programming model of Python. All codes are also written to be highly efficient, using C++, Cython and Pythran to speedup the performance of critical functions.   Funding statement: This project has indirectly benefited from funding from the foundation Simone et Cino Del Duca de l’Institut de France, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 647018-WATU and Euhit consortium) and the Swedish Research Council (Vetenskapsrådet): 2013-5191. We have also been able to use supercomputers of CIMENT/GRICAD, CINES/GENCI (grant 2018-A0040107567) and the Swedish National Infrastructure for Computing (SNIC).

【 授权许可】

Unknown