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以卷积神经网络为基础的燃烧模型的大模拟的推断性能:雷诺兹数,过器内核和过器大小的影响.
Geveen Arumapperuma1, Nicola Sorace1, Matthew Jansen1
1School of Engineering, The University of Edinburgh, Edinburgh, EH8 3JL Scotland, UK.
卷积神经网络 (CNN) 模型在高雷诺兹数训练时,在流性燃烧模拟中显示出良好的推断. 在低雷诺德数训练时,性能降低,但模型在过器大小和内核中很好地泛化.
科学领域:
- * 计算流体动力学
- * 燃烧模式的建模
- * 机器学习应用程序
背景情况:
- *卷积神经网络 (CNN) 越来越多地用于大型模拟 (LES) 中的子网格规模建模.
- *评估这些基于CNN的模型的推断性能对于它们在乱预混合燃烧中的实际应用至关重要.
研究的目的:
- * 调查CNN模型对流预混合燃烧的推断能力.
- * 分析训练雷诺兹数,过器大小和过器内核对模型性能的影响.
- * 评估CNN模型对未见模拟条件的概括性.
主要方法:
- * 在甲/空气和/空气喷气火焰的直接数值模拟 (DNS) 数据集上训练CNN模型.
- * 测试不同雷诺兹数,过器大小 (高斯和盒子内核) 和过器类型的模型性能.
- * 在样本以外的条件下评估外推性能.
主要成果:
- *CNN模型在接受足够高的雷诺兹数训练时表现出良好的外推性能.
- *当在低雷诺德数数据上训练的模型应用于更高的雷诺德数时,性能会显著下降.
- * 模型显示不同过器尺寸和内核类型,特别是较小尺寸的不同过器尺寸和内核类型,具有令人满意的插值和合理的外推值.
- * 培训数据的战略权重向更大的过器大小提高了概括性.
结论:
- *如果适当训练,基于CNN的燃烧模型可以在不同的过条件中有效地泛化.
- *高雷诺兹数训练是流性燃烧LES中强大的外推性能的关键.
- *训练数据的选择和权重对CNN模型的概括能力产生了重大影响.
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