直接数值模拟三维科尔摩戈罗夫流程用于流模型开发
Kinga Andrea Kovács1, Miklós Balogh2, Gergely Kristóf2
1Department of Fluid Mechanics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, Hungary. kingaandrea.kovacs@edu.bme.hu.
Scientific data
|February 26, 2026
概括
一个新的 Kolmogorov 流的 3D 直接数值模拟数据集被发布. 这些开放访问数据支持流模型研究,特别是用于流体力学中的机器学习应用.
科学领域:
- 流体力学 流体力学 流体力学
- 计算物理学的计算物理.
- 流的研究研究流.
背景情况:
- 流模型是流体力学的一个重大挑战.
- 开发精确的流模型,特别是用于机器学习,需要高质量的数据.
- 直接数值模拟 (DNS) 提供了详细的流量信息,但在计算上是昂贵的.
研究的目的:
- 为了呈现一个精选的数据集的3D直接数值模拟的科尔摩戈罗夫流.
- 在多种参数模式 (雷诺德数,激发模式) 中提供数据.
- 支持基于机器学习的流模型的开发.
主要方法:
- 进行了Kolmogorov流的三维直接数值模拟.
- 涵盖了广泛的雷诺兹数和激发模式.
- 包括原始速度字段和基于Python的插值代码来实现网格兼容性.
主要成果:
- 创建了一个全面的数据集,捕捉各种流动行为.
- 确保数据与分析工具 (如ParaView) 的兼容性.
- 为增强可用性提供元数据.
结论:
- 开放式访问数据集有助于在流建模方面的研究.
- 它对于在流体动力学中推进机器学习方法特别有价值.
- 本资源旨在加速理解和预测流的进展.
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