基于对分子振动状态的粗粒度处理的双温度热化学不平衡模型
Jiaqi Lv1,2, Qizhen Hong1, Xiaoyong Wang1
1State Key Laboratory of High Temperature Gas Dynamics, <a href="https://ror.org/057jnjd73">Institute of Mechanics</a>, Chinese Academy of Sciences, Beijing 100190, China.
Physical review. E
|October 19, 2024
概括
一个新的粗粒度模型 (CGM) 显著提高了高温气体流的计算效率. 这种减少顺序模型的准确性与状态对状态 (StS) 模型相美,能够更快地模拟热化学不平衡现象.
科学领域:
- 计算流体动力学 计算流体动力学
- 化学动力学 化学动力学
- 航空航天工程是航空航天工程.
背景情况:
- 高保真状态对状态 (StS) 模型准确模拟高温热化学不平衡流动.
- StS模型的计算成本限制了它们的实际应用.
研究的目的:
- 为高温热化学不平衡流量开发一个计算效率高的减少顺序模型.
- 在两个温度 (2T) 框架内使用粗粒度方法实现与StS模型可比的准确性.
主要方法:
- 通过将分子振动能量水平结合起来,开发了一个多组粗粒度模型 (CGM).
- 提出了振动组的分析分布函数,结合了特雷诺式和线性术语.
- 引入了使用代数组分布函数的新两温度模型 (CG2T).
- 集成了一个预训练的神经网络来加快源项计算.
主要成果:
- 多组CGM,即使有两个组,也与后冲击氧气流的StS模型结果非常相匹配.
- 一组CGM (CGM-1G) 无法接近StS结果,因为无法捕获非博尔兹曼效应.
- 与CGM-1G相比,拟议的CG2T模型显示了明显提高的准确性.
- 神经网络集成实现了30倍的速度提升,而不损失精度.
结论:
- 在CG2T模型提供了一个计算效率高,准确的替代STS模型模拟高温不平衡流.
- 粗粒度的振动能量水平和结合分析分布函数是模型准确性的关键.
- 神经网络加速进一步提高了CG2T模型的实际适用性.
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