粘弹性流体流量的非线性参数模型
C M Oishi1, A A Kaptanoglu2, J Nathan Kutz3
1Departamento de Matemática e Computação, Faculdade de Ciências e Tecnologia, São Paulo State University, Presidente Prudente, Brazil.
Royal Society open science
|October 29, 2024
概括
本研究引入了使用SINDy算法的复杂粘弹性流体流的可解释的减少顺序模型 (ROM). 数据驱动的模型准确地预测流动力学,并推断到未见的条件,推进非牛顿流体力学.
科学领域:
- 流体力学 流体力学 流体力学
- 非牛顿流体动力学的流体动力学.
- 计算科学 计算科学
背景情况:
- 降低顺序模型 (ROM) 对牛顿流体具有计算效率,但对于复杂的非牛顿粘弹性流动没有充分利用.
- 粘性弹性流动由于合的粘性和弹性力而带来不稳定性和分叉等挑战,需要大量的计算资源.
- 现有的方法很难在各种流量参数中捕捉粘弹性流体的复杂动力学.
研究的目的:
- 开发可解释的减少顺序模型 (ROM) 用于粘弹性流体的流动,使用稀疏识别非线性动力学 (SINDy) 算法.
- 为了证明数据驱动的替代模型在预测短暂演变和重建空间流域方面的能力.
- 创建一个参数模型,以怀森伯格数捕捉动态变化,并推断到高怀森伯格数.
主要方法:
- 应用稀疏识别非线性动力学 (SINDy) 算法,从数据中识别治理方程.
- 使用奥尔德罗伊德-B流体模型开发数据驱动的替代模型,用于在四轮滚筒机中进行基准振荡粘弹性流动.
- 非线性模型的参数化,以捕捉动态作为森堡数的函数.
主要成果:
- 成功证明了基于SINDy的ROM在预测短暂流动演变和重建空间流动场的有效性.
- 开发了一种完全参数的非线性模型,能够用韦森伯格数捕捉动态变化.
- 展示了该模型能够推断并准确预测主导动态的能力,即使对于高维森伯格数,超出了训练数据制度.
结论:
- SINDy算法为复杂的粘弹性流提供了一个强大的工具,用于为复杂的粘弹性流创建可解释的ROM.
- 数据驱动的替代模型为分析非牛顿流体动力学提供了计算效率高的替代方案.
- 这种方法在将机器学习应用于粘弹性流量建模和分析方面取得了重大进展.
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