用于预测流时间信号中的极端事件的条件POD
David Martín1, Joan Grau1, Lluís Jofre2
1Department of Fluid Mechanics, Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, 08019, Spain.
Scientific reports
|August 13, 2025
概括
这项研究引入了一种新的数据驱动方法,用于早期预测流中罕见的极端事件. 该方法使用能量模式来识别即将发生的事件,甚至在有限的数据上也超过现有方法.
科学领域:
- 流体动力学 流体动力学
- 流研究 流研究
- 数据驱动科学数据驱动科学
背景情况:
- 流中的极端事件很少见,但具有影响力,这给预测带来了挑战.
- 传统的数据密集型方法与这些事件的间歇性和不频繁性作斗争.
- 准确的预测对于各种工程应用中的系统可靠性和性能至关重要.
研究的目的:
- 开发一种新的数据驱动方法,用于在时间信号中预测极端事件的早期阶段.
- 为了在没有广泛的训练数据的情况下能够在飞行中检测罕见事件.
- 通过标记可能不准确的预测,提高流预测的可靠性.
主要方法:
- 从极端事件之前的段落中识别出最有活力的仅限时间的正正对角分解 (POD) 模式.
- 使用支向量机 (SVM) 来根据与已识别的POD模式相似性对传入信号进行分类.
- 应用条件POD方法来预测墙壁边界剪切流中的极端消散事件.
主要成果:
- 该方法在预测各种雷诺兹数和墙壁距离的极端散射事件方面表现强.
- 实现与汉克尔-DMD方法相匹配或超过预测交付时间,即使训练数据有限.
- 成功标记传入的极端事件,允许潜在的丢弃或缩短来自其他方法的不可靠预测.
结论:
- 条件POD方法提供了一个可行的数据驱动的解决方案,用于早期预测流中罕见的极端事件.
- 这种方法对于数据可用性有限的事件特别有效.
- 通过为极端事件提供及时警报,提高了流预测系统的可靠性.
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