用于快速同化墙壁压力测量从高超音速流在形上的ML
Pierluigi Morra1, Charles Meneveau1, Tamer A Zaki2
1Department of Mechanical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA.
Scientific reports
|June 4, 2024
概括
本研究介绍了一种深度学习方法,用于在复杂的流体动力学模拟中更快地同化数据. 这种方法显著降低了用于预测超音速流量的计算成本.
科学领域:
- 计算流体动力学 计算流体动力学
- 机器学习应用程序 机器学习应用程序
- 科学计算是科学计算.
背景情况:
- 数据同化 (DA) 对于动态系统中的高可靠性预测至关重要.
- 对于过渡超音速流等复杂系统,DA的计算成本往往是过高的.
- 现有的变化技术面临着相当大的计算负担.
研究的目的:
- 为数据同化开发一个准确和有效的深度学习方法.
- 与传统方法相比,要实现实质性的计算加速.
- 为了解决复杂的流量模拟中反向问题的高成本.
主要方法:
- 利用深度运算网络 (DeepONet) 作为可压缩纳维埃-斯托克斯方程的元模型.
- 通过对监督学习数据集构建的最佳采样,最大限度地降低了昂贵的直接数值模拟.
- 采用无梯度技术来导航DeepONet的近似,以加速高维同化解决方案.
主要成果:
- 与变化技术相比,实现了大约3个数量级的计算加速.
- 证明成功地应用了风洞测量的数据同化.
- 在马赫6的过渡边界层流量上验证了该方法.
结论:
- 拟议的深度学习方法显著减轻了数据同化中的计算负担.
- DeepONet为复杂的流体动力学提供了一个准确,节和高效的元模型.
- 这种方法可以更快,更具成本效益地对过渡超音速流量进行高准确度预测.
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