适应性物理信息的神经操作员用于粗粒度的非平衡流
Ivan Zanardi1, Simone Venturi1, Marco Panesi2
1Center for Hypersonics and Entry Systems Studies, Department of Aerospace Engineering, University of Illinois Urbana-Champaign, Urbana, 61801, IL, USA.
Scientific reports
|September 19, 2023
概括
本研究引入了一种机器学习 (ML) 框架,以加快非平衡反应流模拟. 层次深度学习模型准确地预测了超音速飞行应用的化学动力学.
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
- 机器学习 机器学习
背景情况:
- 模拟非平衡反应流是计算密集的.
- 精确的化学动力学建模对于高超音速飞行等应用至关重要.
研究的目的:
- 开发基于机器学习 (ML) 的替代模型,以提高非平衡反应流模拟中的计算效率.
- 确保ML模型遵守基本的物理原理.
主要方法:
- 一个层次和自适应的深度学习策略,结合了缩小维度和神经运算符.
- 基于物理学的神经操作员用软和硬约束块.
- 转移学习用于简化培训和适应性预测用于加速评估.
主要成果:
- 在0-D场景中对近30种物种的化学动力学进行准确预测,最大相对误差为4.5%.
- 在1D冲击模拟中实现了1-4.5%的准确性,比传统方法加快了10倍.
- 基于局部非平衡条件的演示适应性预测能力.
结论:
- 拟议的ML框架为反应性纳维埃-斯托克斯溶解器提供了高效,符合物理条件的替代品的基础.
- 能够在复杂的多维模拟中准确地描述非平衡现象.
- 为模拟超音速流动化学动力学提供了显著的加速.
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