Abstract

This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics (CFD) simulations and deep learning models. The coupling approach is showcased by the development of a data-driven turbulence model. The new turbulence model is built using a deep learning neural network, whose mapping structure is based on a zero-equation turbulence model for built environment simulations, and is coupled with the CFD software OpenFOAM to create a hybrid framework. The neural network is a standard shallow multi-layer perceptron. The number of hidden layers and nodes per layer was optimized using Bayesan optimization algorithm. The framework is trained on an indoor environment case study, as well as tested on an indoor office simulation and an outdoor building array simulation. Results show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes (RANS) turbulence models, while maintaining a similar level of accuracy. The model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow scenarios. Despite some challenges, namely the mapping constraint, the limited training dataset size and the source of generation of training data, the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.

中文翻译：

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利用深度学习开发基于零方程的湍流模型，用于建筑环境的 CFD 模拟

摘要

本研究旨在通过创建计算流体动力学 (CFD) 模拟和深度学习模型之间的耦合框架，提高建筑环境中热舒适度和空气质量预测的准确性和速度。数据驱动的湍流模型的开发展示了耦合方法。新的湍流模型是使用深度学习神经网络构建的，其映射结构基于用于建筑环境模拟的零方程湍流模型，并与CFD软件OpenFOAM相结合以创建混合框架。神经网络是标准的浅层多层感知器。使用贝叶斯优化算法优化隐藏层和每层节点的数量。该框架经过室内环境案例研究的训练，以及室内办公室模拟和室外建筑阵列模拟的测试。结果表明，基于深度学习的湍流模型比传统的两方程雷诺平均纳维斯托克斯 (RANS) 湍流模型更稳健、更快，同时保持相似的精度水平。该模型还优于标准代数零方程模型，因为它具有泛化到各种流动场景的卓越能力。尽管存在一些挑战，即映射约束、有限的训练数据集大小和训练数据的生成来源，但混合框架证明了耦合技术的可行性，并作为未来开发更可靠和更先进模型的起点。