发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一个贝叶斯框架，在主动微流变学中建模壁面与粒子间流体动力学相互作用，显著提升参数推断精度，并支持联合识别壁距、检验线性模型及区分模量空间变化。
AI 中文摘要
主动微流变学通过力驱动探针的运动来推断流变性质。然而，在小样本中，探针通常靠近壁面或其他探针，由此产生的流体动力学相互作用会使推断参数产生偏差。我们提出一个贝叶斯框架，将这些相互作用纳入针对牛顿流体和线性黏弹性流体的简化解析模型中，并在来自牛顿流体和Oldroyd-B流体的有限元模拟的含噪合成数据上对其进行测试。考虑这些相互作用显著提高了推断参数的准确性。在壁面附近，斜向施力使得材料参数和壁面距离能够从单次实验中联合识别。在非线性黏弹性区域，对多个力水平进行后验预测检查揭示了线性模型的不足。最后，当探针尺寸与异质性长度尺度相当时，该框架能够区分模量的空间变化与测量噪声。
英文摘要
Active microrheology infers rheological properties from the motion of a force-driven probe. In small samples, however, the probe is often close to walls or other probes, and the resulting hydrodynamic interactions bias the inferred parameters. We present a Bayesian framework in which these interactions are built into simplified analytical models for Newtonian and linear viscoelastic fluids and test it on noisy synthetic data from finite-element simulations of Newtonian and Oldroyd-B fluids. Accounting for the interactions substantially improves the accuracy of the inferred parameters. Near a wall, oblique forcing allows the material parameters and the wall distance to be identified jointly from a single experiment. In the nonlinear viscoelastic regime, posterior predictive checks over multiple force levels reveal the inadequacy of the linear model. Finally, when the probe size is comparable to the heterogeneity length scale, the framework distinguishes spatial variations in the modulus from measurement noise.
