fair-flexi - A trustworthy CFD code for simulation and training

在这个项目中，我们的目标是增加CFD框架Flexi的可用性、用户友好性和影响。Flexi是一个成熟的开源生态系统，处于发展其社区并确保长期有用的最佳位置。许多出版物证明了Flexi的高质量。然而，我们已确定有需要在文件和培训方面加强这方面的工作，并制订策略以促进社会的发展。我们计划通过我们称之为公平灵活的新框架解决这些方面的问题。其核心是基于这样一种认识，即扩展、使用和学习复杂的科学软件堆栈远远不止确保模拟结果或源代码遵循公平的原则。虽然这当然是第一步，但它并没有解决以下问题：a)学习者不仅要学习如何运行代码，而且要学习如何操作完整的模拟堆栈，包括前处理和后处理步骤。这些信息往往包含大量隐含的知识，而这些知识很少以显性的方式表达出来。B)有经验的用户在更高的层面上努力解决同样的问题：代码和完整框架的许多选项、模型和参数，再加上计算研究经常迭代的性质，使得跟踪哪些有效，哪些无效非常困难。即使是非常有经验的研究人员，也很难重现他们自己的结果。C)代码开发人员经常发现自己与其他代码维护人员发生冲突：他们的新功能可能会减慢执行速度，与代码的其他部分发生不必要的交互，甚至破坏代码。通常，一段好的代码可能与开发人员甚至可能意识不到的功能不兼容。在每个级别上描述的问题都体现了这样一个事实，即仿真环境是具有许多参数和非线性相互作用的系统。这导致了模拟结果的可信性、有用性和重现性方面的残余模糊性。这不仅阻碍了科学进步，还使学习和使用代码变得痛苦。我们提出的新框架FIRE-FLEXI是基于建立完整的仿真环境的思想，从预处理工具的第一步到显示仿真结果的彩色图形。为此，我们将生成一个自动化框架，该框架跟踪、提供DOI、在Dataverse中存储和发布在特定模拟战役中采取的所有步骤。该数据集可以通过其他人的互动反馈进行扩充，并将成为研究人员和开发人员以及学习者的宝贵工具。这使得模拟结果真正值得信赖，并可重现到最细微的级别。我们计划与研究生和研究合作者一起实地测试这种方法。就我们所知，这是一种新的方法，在开放源码的CFD代码社区中还没有遵循。

In this project, we aim at increasing the usability, user-friendliness and impact of the CFD framework FLEXI. FLEXI is a well-established open source ecosystem and is positioned optimally to grow its community and ensure long-lived usefulness. The high quality of FLEXI is witnessed by a number of publications. However, we have identified the need to grow it in terms of the documentation and training aspects and to develop strategies to foster the community. We plan on addressing these aspects through a novel framework which we call fair-flexi. At its core, it is based on the recognition that extending, using and also learning a complex scientific software stack goes far beyond making sure that the simulation results or the source code follow the FAIR principles. While that is of course a first step, it does not solve the following issues: a) Learners are confronted not just with learning how to run the code, but how to operate the full simulation stack, including pre- and postprocessing steps. These tend to include a lot of implicit knowledge that is seldom expressed in an explicit way. b) Experienced users struggle with the same issue at a higher level: The many options, models and parameters of the code and full framework together with often iterative nature of computational research make it very hard to track what worked and what didn’t. Even for very experienced researchers it is surprisingly hard to reproduce their own results. c) Code developers often find themselves in conflict with other code maintainers: Their new feature might slow down the execution, have unwanted interactions with other parts of the code or even break it. Often, a good piece of code might be incompatible with features the developer might not even be aware of. The problems described at each of these levels are an expression of the fact that simulation environments are systems with many parameters and non-linear interactions. This induces a residual ambiguity in the trustworthiness, the usefulness and reproducability of the simulation results. This hampers not just scientific advancement, but also makes learning and using the code painful. Our proposed novel framework fair-flexi is based on the idea of making the full simulation environment, from the first step in a preprocessing tool to a color figure showing the simulation results ’FAIR’. For this, we will generate an automated framework that tracks, provides a DOI, stores and publishes all the steps taken in a specific simulation campaign in a Dataverse. The dataset can augmented by interactive feedback from others and will serve as an invaluable tool to researchers and developers as well as learners. This makes the simulation results truly trustworthy and reproducible down to the most granular level. We plan on field-testing this approach with graduate students and research collaborators. To the best of our knowledge, this is a novel approach and has not been followed in the community of open source CFD codes.