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首页> 外文期刊>Building and Environment >Development of self-adaptive low-dimension ventilation models using OpenFOAM: Towards the application of AI based on CFD data
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Development of self-adaptive low-dimension ventilation models using OpenFOAM: Towards the application of AI based on CFD data

机译：使用OpenFoam开发自适应低尺寸通风模型：基于CFD数据的AI应用

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摘要

Numerous state-of-art CFD (Computational Fluid Dynamics) studies have shown their validity and feasibility in engineering applications but still lack prediction efficiency. It is of great potential to apply artificial intelligence (AI) on the basis of CFD considering their fast development. Thus, the data-dimension reduction of CFD can be very important for the efficiencies of database construction, training and storage. Our previously developed linear low-dimension ventilation model (LLVM) is able to convert high-resolution CFD data into low-dimension grid levels, facilitated the use of fast prediction for ventilation online control. However, limitation still exists considering the dilemma of prediction speed and accuracy, e.g., case of a larger building space. This is due to the neglect of volume contribution ratio from single mesh as well as correlations of cells information when using uniform low-dimension methods. Therefore, we proposed a self-adaptive non-uniform low-dimension model for the data conversion but using lower dimension size with acceptable accuracy. The open-source CFD platform OpenFOAM was used for the package development, called self-adaptive low-dimension tool (LDT), including two modules, i.e., Mon-uniform dividing' and 'self-update'. Error index was defined considering the contribution ratio of individual mesh volume. A series of cases were carried out for demonstration and evaluation. It is found that the proposed model is able to largely improve the data accuracy but with smaller dimension requirement compared to uniform dividing method (e.g., with comparable error index around 16.5% when using zone numbers of 80 for non-uniform and 210 for uniform). Moreover, the self-update module enables users to efficiently and automatically identify the optimal low-dimension zone numbers. This work can be of great importance for the application of CFD-AI techniques.
机译：许多最先进的CFD（计算流体动力学）研究表明了工程应用中的有效性和可行性，但仍然缺乏预测效率。考虑到他们的快速发展，在CFD的基础上施加人工智能（AI）是巨大的潜力。因此，CFD的数据维度降低对于数据库构建，培训和存储的效率非常重要。我们以前开发的线性低尺寸通风模型（LLVM）能够将高分辨率CFD数据转换为低维网格水平，促进了使用快速预测通风在线控制。然而，考虑到预测速度和精度的困境，例如较大的建筑空间的情况仍然存在限制。这是由于忽略了从单网格的体积贡献比以及使用均匀的低维方法时细胞信息的相关性。因此，我们提出了一种用于数据转换的自适应非均匀低维模型，但使用较低的尺寸尺寸具有可接受的精度。开源CFD平台OpenFoam用于包装开发，称为自适应低维工具（LDT），包括两个模块，即蒙大均匀分割'和“自更新”。考虑单个网格卷的贡献比定义错误索引。进行一系列案例进行示范和评估。发现该建议的模型能够在很大程度上提高数据精度，而是与均匀分割方法相比的尺寸要求较小（例如，在使用框架数为80的区域数为均匀的80×80时约为16.5％） 。此外，自更新模块使用户能够有效地和自动识别最佳的低维区域号。这项工作对于应用CFD-AI技术来说可能非常重要。

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