经典研究的连贯结构只描绘了一幅围墙流的部分图片
Andrés Cremades1,2, Sergio Hoyas3, Ricardo Vinuesa4,5
1Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de Valéncia, Valencia, Spain. andrescb@kth.se.
Nature communications
|November 19, 2025
概括
了解流能量转移仍然是一个挑战. 本研究介绍了一种基于数据的方法,使用深度学习和SHAP值来客观地识别关键的流域,从而推进流分析.
科学领域:
- 流体动力学 流体动力学
- 流研究 流研究
- 计算科学 计算科学
背景情况:
- 经过140年的研究,在流中能量传输和消散的机制尚未完全被理解.
- 之前的研究集中在连贯结构上,但它们在流动发展中的确切作用仍然不确定.
研究的目的:
- 开发一个数据驱动的方法论,客观地识别流中的重要区域.
- 将这些数据识别区域与经典的连贯结构进行比较.
主要方法:
- 一个深度学习模型被训练来预测流的未来状态.
- 使用梯度SHAP (SHapley增量扩展) 算法来量化每个网格点的重要性.
- 使用SHAP值计算高重要性地区.
主要成果:
- 在SHAP分析中,在流中客观地确定了更为重要的区域.
- 这些确定的地区不同程度地与传统的连贯结构达成一致.
- 在SHAP识别的区域和任何单一类型的古典结构之间没有发现完全的相关性.
结论:
- 提出的基于SHAP的方法提供了一个客观的方法,用于精确确定动荡流中的关键区域.
- 这种方法为流体结构和能量动态之间的关系提供了新的视角.
- 这些发现表明,比传统的连贯结构更了解流组织的细微差别.
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