一种可扩展的卷积神经网络方法来预测复杂环境中的流体流动.
Pratip Rana1, Timothy M Weigand2,3, Kevin R Pilkiewicz3
1Bennett Aerospace, Vicksburg, 39180, USA.
Scientific reports
|October 4, 2024
概括
卷积神经网络 (CNN) 可以预测流体流速场. 一种新的域分解方法提高了计算流体动力学 (CFD) 中复杂障碍配置的效率.
科学领域:
- 流体动力学 流体动力学
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 机器学习 机器学习
- 深度学习是一种深度学习.
背景情况:
- 精确预测流体流速场对于许多工程应用至关重要.
- 传统的计算流体动力学 (CFD) 方法在计算上可能很昂贵,特别是在复杂的几何和大领域.
- 深度学习模型,特别是卷积神经网络 (CNN),在加速这些预测方面表现有前途.
研究的目的:
- 评估CNN在预测阻碍物周围流体流动速度场方面的能力.
- 开发和评估一个域分解方法来处理比培训中使用的更大,更复杂的流域.
- 引入一个新的域分解指标,并分析错误传播.
主要方法:
- 在CNN模型中使用了封闭的剩余U-Net架构.
- 在CFD模拟中生成的速度场上训练网络.
- 通过将域分解为子域,开发了一种零碎,半连续的方法.
- 引入了域分解策略的局部定向向量场 (LOVE) 度量.
- 适用于速度场重建的平滑性和连续性约束.
主要成果:
- CNN模型准确地预测了稳定的流体流速场,用于看不见的入口速度和障碍物配置.
- 拟议的域分解方法提供了一个计算效率高的替代方案,可以在更大的数据集上重新训练模型.
- 爱度有效指导复杂领域的分解成弱交互的子集.
- 对越来越大的模拟域的错误传播进行了评估.
结论:
- CNN能够准确地预测流体流速场.
- 域分解策略显著提高了复杂流体流量问题的计算效率.
- 在流体动力学中,LOVE度量为优化域分解提供了一个强大的方法.
- 这种方法为在现实应用中高效准确的流体流量预测提供了一个有希望的途径.
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