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

fair-flexi - 用于模拟和培训的值得信赖的 CFD 代码

• 批准号: 528525010
• 负责人: Professorin Dr.-Ing. Andrea D. Beck
• 金额: --
• 依托单位: Institut für Wasser- und Umweltsystemmodellierung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/528525010?language=en
• 关键词: fair; flexi; trustworthy; CFD; code

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.

在这个项目中，我们的目标是提高 CFD 框架 FLEXI 的可用性、用户友好性和影响力。 FLEXI 是一个完善的开源生态系统，处于发展社区并确保长期有用性的最佳位置。大量出版物证明了 FLEXI 的高品质。然而，我们已经确定需要在文档和培训方面对其进行发展，并制定促进社区发展的策略。我们计划通过一个我们称之为“公平灵活”的新颖框架来解决这些问题。其核心是基于这样的认识：扩展、使用和学习复杂的科学软件堆栈远远超出了确保模拟结果或源代码遵循公平原则的范围。虽然这当然是第一步，但它并不能解决以下问题：a) 学习者不仅要学习如何运行代码，还要学习如何操作完整的模拟堆栈，包括预处理和后处理步骤。这些往往包括许多很少以显式方式表达的隐性知识。 b) 有经验的用户在更高层次上遇到同样的问题：代码和完整框架的许多选项、模型和参数，再加上计算研究的迭代性质，使得很难跟踪哪些有效、哪些无效。即使对于非常有经验的研究人员来说，重现他们自己的结果也是令人惊讶的困难。 c) 代码开发人员经常发现自己与其他代码维护者发生冲突：他们的新功能可能会减慢执行速度，与代码的其他部分产生不必要的交互，甚至破坏代码。通常，一段好的代码可能与开发人员甚至不知道的功能不兼容。每个级别描述的问题都体现了这样一个事实：仿真环境是具有许多参数和非线性相互作用的系统。这导致模拟结果的可信度、有用性和可重复性存在残余模糊性。这不仅阻碍了科学进步，而且使学习和使用代码变得痛苦。我们提出的新颖框架 fair-flexi 基于创建完整模拟环境的理念，从预处理工具的第一步到显示模拟结果“FAIR”的彩色图。为此，我们将生成一个自动化框架，用于跟踪、提供 DOI、存储和发布 Dataverse 中特定模拟活动中采取的所有步骤。该数据集可以通过其他人的交互式反馈来增强，并将成为研究人员、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，并且可重复到最精细的水平。我们计划与研究生和研究合作者一起对这种方法进行现场测试。据我们所知，这是一种新颖的方法，开源 CFD 代码社区尚未遵循。