论文标题

了解使用贝叶斯推断的高岭石粘土悬浮液的流变学

Understanding the rheology of kaolinite clay suspensions using Bayesian inference

论文作者

Ran, Ranjiangshang, Pradeep, Shravan, Acharige, Sébastien Kosgodagan, Blackwell, Brendan C., Kammer, Christoph, Jerolmack, Douglas J., Arratia, Paulo E.

论文摘要

泥是水中细粒颗粒(沙,淤泥和粘土)的悬浮液。泥浆中粘土矿物质的相互作用产生了复杂的流变行为,例如屈服应力,触变拷贝和粘弹性。在这里,我们使用稳定的剪切流变仪实验检查了高岭石粘土悬浮液,模型泥的流动行为。流动曲线既表现出各种高岭石体积分数($ ϕ_K $)的屈服应力和流变学滞后行为。对这些行为的进一步理解需要适合现有的本构模型,这由于许多拟合参数而具有挑战性。为此,我们采用了贝叶斯推理方法马尔可夫链蒙特卡洛(MCMC),以适合实验流曲线与微结构粘弹性模型。该方法使我们能够估算粘土悬浮液的流变特性,例如粘度,产生应力和放松时间尺度。固有松弛时间尺度的比较表明,高岭石粘土悬浮液具有强烈的粘弹性,在相对较低的$ ϕ_k $下具有弱触变,同时几乎是无弹性的,纯弹性,纯粹在高$ nix_k $的情况下。总体而言,我们的结果为预测模型提供了一个框架,以阐明天然材料和其他结构化流体的流变行为。

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 ($ϕ_k$). 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 (MCMC), 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 $ϕ_k$, while being almost inelastic and purely thixotropic at high $ϕ_k$. Overall, our results provide a framework for predictive model fitting to elucidate the rheological behaviors of natural materials and other structured fluids.

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