Approximating wind flows using computational fluid dynamics (CFD) methods can be time-consuming. Creating a tool for interactively designing prototypes while observing the wind flow change requires simpler models to simulate faster. Instead of running numerical approximations resulting in detailed calculations, data-driven methods in deep learning might be able to give similar results in a fraction of the time. This work rephrases the problem from computing 3D flow fields using CFD to a 2D image-to-image translation-based problem on the building footprints to predict the flow field at pedestrian height level. We investigate the use of generative adversarial networks (GAN), such as Pix2Pix [1] and CycleGAN [2] representing state-of-the-art for image-to-image translation task in various domains as well as U-Net autoencoder [3]. The models can learn the underlying distribution of a dataset in a data-driven manner, which we argue can help the model learn the underlying Reynolds-averaged Navier-Stokes (RANS) equations from CFD. We experiment on novel simulated datasets on various three-dimensional bluff-shaped buildings with and without height information. Moreover, we present an extensive qualitative and quantitative evaluation of the generated images for a selection of models and compare their performance with the simulations delivered by CFD. We then show that adding positional data to the input can produce more accurate results by proposing a general framework for injecting such information on the different architectures. Furthermore, we show that the models performances improve by applying attention mechanisms and spectral normalization to facilitate stable training.
翻译:使用计算流体动态( CFD) 方法估计风流流可能耗时。 在观察风流变化的同时为交互式设计原型创建一个工具, 需要更简单的模型来更快速地模拟。 与运行数字近似以详细计算的结果相比, 深层学习中的数据驱动方法也许能够在一小部分时间里产生类似的结果。 这项工作将问题从使用计算流体动态( CFD) 计算三维流流流场到基于2D图像到图像的翻译问题重新表述为建筑脚印来预测行人高度的流场。 我们调查了使用基因化对抗网络( GAN) 的情况, 例如 Pix2Pix [1] 和 CydleGAN [2] 需要更快速地模拟。 代表不同域的图像到图像转换的状态, 以及 U- Net 自动编码 [3] 。 模型可以以数据驱动的方式了解数据集的基本分布情况, 我们用Cnolderd- snavier- Stokes (RANS) 等方法从 CFDD 中学习基本的 Remagistrual 。 我们用新模拟的模拟模型来进行精确的模拟数据转换, 并用三维度的图像显示的图像的模型来比较。