Abstract A data-driven surrogate modelling methodology of CFD simulation data is proposed.It uses convolutional variational autoencoders (VAE) and multi-layer perceptron (MLP) neural networks in an integrated manner to predict temperature, velocity and species mass fraction profiles on a cell-by-cell basis. The VAE performs feature extraction and data compression, while significantly reducing the number of network interconnections and ensuring physically realistic predictions.The MLP maps the CFD boundary condition values to the encodings generated with the VAE encoder network. The approach is demonstrated via application to a 2D axisymmetric methane-fired turbulent jet diffusion flame. The integrated network model produced average normalized mean absolute errors (NMAE) of 2.41% for the temperature predictions, 0.99% for velocity and 0.925% for species mass fractions. It is, therefore, possible to predict 2D CFD data fields with reasonable accuracy and generalizability, although high NMAE values were observed for certain cells confined to a small region near the centreline of the burner. The methodology lays the foundation for application to larger industrial problems to visualize processes inside equipment, deploy virtual sensors, perform quick what-if analysis, explore the design space, link to optimization routines to effectively control equipment, detect anomalies, or to form part of lower-dimensional system simulations.

中文翻译：

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使用生成式深度学习利用甲烷燃烧器的模拟数据预测温度、流量和物种分布的应用

摘要 提出了一种数据驱动的 CFD 模拟数据代理建模方法。它以集成的方式使用卷积变分自编码器 (VAE) 和多层感知器 (MLP) 神经网络来预测细胞上的温度、速度和物种质量分数分布。逐细胞基础。VAE 执行特征提取和数据压缩，同时显着减少网络互连的数量并确保物理上真实的预测。MLP 将 CFD 边界条件值映射到使用 VAE 编码器网络生成的编码。该方法通过应用于二维轴对称甲烷燃烧湍流射流扩散火焰进行了演示。集成网络模型产生的平均归一化平均绝对误差 (NMAE) 为 2.41% 的温度预测、0.99% 的速度和 0。物种质量分数为 925%。因此，可以以合理的精度和普遍性来预测二维 CFD 数据字段，尽管在燃烧器中心线附近的小区域内观察到某些单元的高 NMAE 值。该方法为更大的工业问题的应用奠定了基础，以可视化设备内部的过程、部署虚拟传感器、执行快速假设分析、探索设计空间、链接到优化例程以有效控制设备、检测异常或形成低维系统模拟。