连续和不连续的可压缩流在一个收-分离的通道解决的物理信息的神经网络没有外源数据
Hong Liang1, Zilong Song1, Chong Zhao2
1Department of Physics, Hangzhou Dianzi University, Hangzhou, 310018, China.
Scientific reports
|February 15, 2024
概括
基于物理学的神经网络 (PINNs) 准确地解决了可压缩的流量问题,包括冲击波,而不需要外部数据. 这一进步使各种流量状态和反向问题的准确预测成为可能.
科学领域:
- 计算流体动力学的流体动力学.
- 物理学中的人工智能
- 部分微分方程 部分微分方程
背景情况:
- 收-分离喷嘴中的压缩流由欧勒方程控制.
- 对于依赖压力比的稳定状态,存在分析解决方案.
- 经典的数值方法与冲击不连续性作斗争,原来的PINNs无法准确预测这些流量.
研究的目的:
- 为了适应物理信息的神经网络 (PINNs) 准确地解决可压缩流量问题.
- 解决原始PINN在处理冲击波和不连续溶液方面的局限性.
- 为了证明修改后的PINNs在解决前向和反向流量问题的能力.
主要方法:
- 使用物理信息的神经网络 (PINNs) 来建模可压缩流动力学.
- 调整损失函数权重以确保物理准确性并防止微不足道的解决方案.
- 采用没有外源数据的通用设置来训练神经网络.
主要成果:
- 对于不同的压力比率,PINNs成功地预测了各种各样的流量分支 (亚音速,超音速,混合).
- 该方法准确地捕获不连续的溶液,包括正常的冲击波.
- PINN在解决逆问题方面表现出有效性,例如确定特定热量比率.
结论:
- 修改后的PINN为解决复杂的可压缩流量问题提供了强大而有效的数据处理方法.
- 这项研究突出了PINNs在流体动力学中的潜力,特别是在涉及不连续性的问题上.
- 开发的方法为稳定和不稳定的流量状态提供了准确的解决方案,而不需要实验数据.
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