Mud is a suspension of fine-grained particles (sand, silt, and clay) in water. The interaction of clay minerals in mud gives rise to complex rheological behaviors, such as yield stress, thixotropy, and viscoelasticity. Here, we experimentally examine the flow behaviors of kaolinite clay suspensions, a model mud, using steady shear rheometry. The flow curves exhibit both yield stress and rheological hysteresis behaviors for various kaolinite volume fractions (${\varphi }_k$). Further understanding of these behaviors requires fitting to existing constitutive models, which is challenging due to numerous fitting parameters. To this end, we employ a Bayesian inference method, Markov chain Monte Carlo, to fit the experimental flow curves to a microstructural viscoelastic model. The method allows us to estimate the rheological properties of the clay suspensions, such as viscosity, yield stress, and relaxation time scales. The comparison of the inherent relaxation time scales suggests that kaolinite clay suspensions are strongly viscoelastic and weakly thixotropic at relatively low ${\varphi }_k$, while being almost inelastic and purely thixotropic at high ${\varphi }_k$. Overall, our results provide a framework for predictive model fitting to elucidate the rheological behaviors of natural materials and other structured fluids.

中文翻译：

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使用贝叶斯推理了解高岭土悬浮液的流变学

泥浆是细粒颗粒（沙子、淤泥和粘土）在水中的悬浮液。泥浆中粘土矿物的相互作用会产生复杂的流变行为，例如屈服应力、触变性和粘弹性。在这里，我们使用稳态剪切流变仪实验检查高岭土悬浮液（一种模型泥浆）的流动行为。流动曲线表现出各种高岭石体积分数的屈服应力和流变滞后行为（${\phi }_k$). 进一步了解这些行为需要拟合现有的本构模型，由于拟合参数众多，这具有挑战性。为此，我们采用贝叶斯推理方法，马尔可夫链蒙特卡罗，将实验流动曲线拟合到微观结构粘弹性模型。该方法使我们能够估计粘土悬浮液的流变特性，例如粘度、屈服应力和松弛时间尺度。固有弛豫时间尺度的比较表明，高岭石粘土悬浮液在相对较低的温度下具有强粘弹性和弱触变性${\phi }_k$, 而在高时几乎没有弹性和纯触变性${\phi }_k$. 总的来说，我们的结果为预测模型拟合提供了一个框架，以阐明天然材料和其他结构流体的流变行为。