A deep learning framework for hydrogen-fueled turbulent combustion simulation
A deep learning framework for hydrogen-fueled turbulent combustion simulation
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DOI：
10.1016/j.ijhydene.2020.04.286
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发表时间：
2020-07-10
影响因子：
7.2
通讯作者：
He, Guo Qiang
中科院分区：
文献类型：
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作者：
An, Jian;Wang, Hanyi;He, Guo Qiang
高分辨率计算流体/火焰动力学（CFD）的高成本阻碍了其在燃烧相关设计、研究和优化中的应用。在这项研究中，我们提出了一个新的框架，湍流燃烧模拟的基础上，深度学习方法。受U-Net架构和初始模块的启发，设计了一个优化的深度卷积神经网络（CNN），用于构建深度学习求解器的框架，名为CFDNN。然后，CFDNN的训练上的模拟结果的氢气燃烧在一个空腔与不同的入口速度。经过训练后，CFDNN不仅可以准确地预测训练集范围内的流场和燃烧场，而且对训练集外的预测也表现出外推能力。CFDNN求解器的结果在预测的空间分布和时间动态方面与传统的CFD结果具有很好的一致性。同时，两个数量级的加速度是通过使用CFDNN求解器相比，传统的CFD求解器。这种基于深度学习的求解器的成功开发为燃气轮机和超燃冲压发动机等燃烧系统的低成本、高精度仿真、快速原型设计、设计优化和实时控制开辟了新的可能性。(C)2020年氢能出版有限责任公司。由爱思唯尔有限公司出版。保留所有权利。
The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired by a U-Net architecture and inception module is designed for constructing the framework of the deep learning solver, named CFDNN. CFDNN is then trained on the simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from the CFDNN solver show excellent consistency with conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration is achieved by using the CFDNN solver compared to a conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.