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arXiv 2408.02604physics.flu-dyncs.LGmath.OC
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从测速数据中学习非牛顿流体的流变参数

Learning rheological parameters of non-Newtonian fluids from velocimetry data

Engineering Department, University of Cambridge(剑桥大学工程系);Materials Science and Engineering Department, University of Sheffield(谢菲尔德大学材料科学与工程系);School of Medicine and Population Health, Faculty of Health, University of Sheffield(谢菲尔德大学医学院与人口健康学院);Mechanical & Aerospace Engineering Department, University of Manchester(曼彻斯特大学机械与航空航天工程系)

Alexandros Kontogiannis, Richard Hodgkinson, Emily L. Manchester

AI总结:

本研究提出一种融合测速数据的贝叶斯逆N-S算法,可仅靠测速数据学习剪切稀化流体的Carreau参数及不确定性,经流动MRI实验验证,参数学习结果与流变仪测量高度吻合,且算法可扩展至其他可微黏度模型的非牛顿流体。

摘要:

我们求解了一个贝叶斯逆Navier-Stokes(N-S)问题,该问题融合测速数据以联合重建流场并学习未知的N-S参数。通过将Carreau剪切稀化黏度模型纳入N-S问题,我们设计了一种算法,仅需测速数据即可学习剪切稀化流体最可能的Carreau参数,并估计其不确定性。随后我们开展了流动MRI实验,以获取血液模拟流体流经理想化医疗装置(FDA喷嘴)的轴对称层流射流的测速数据。研究表明,该算法可通过学习最可能的Carreau参数成功重建流场,且学习得到的参数与流变仪测量结果高度吻合。该算法可适配任意代数有效黏度模型,只要模型可微;若将黏弹性模型纳入N-S问题,还可扩展至更复杂的非牛顿流体(如Oldroyd-B流体)。

英文摘要:

We solve a Bayesian inverse Navier-Stokes (N-S) problem that assimilates velocimetry data in order to jointly reconstruct the flow field and learn the unknown N-S parameters. By incorporating a Carreau shear-thinning viscosity model into the N-S problem, we devise an algorithm that learns the most likely Carreau parameters of a shear-thinning fluid, and estimates their uncertainties, from velocimetry data alone. We then conduct a flow-MRI experiment to obtain velocimetry data of an axisymmetric laminar jet through an idealised medical device (FDA nozzle) for a blood analogue fluid. We show that the algorithm can successfully reconstruct the flow field by learning the most likely Carreau parameters, and that the learned parameters are in very good agreement with rheometry measurements. The algorithm accepts any algebraic effective viscosity model, as long as the model is differentiable, and it can be extended to more complicated non-Newtonian fluids (e.g. Oldroyd-B fluid) if a viscoelastic model is incorporated into the N-S problem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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