Scattering-Informed Microstructure Prediction during Lagrangian Evolution (SIMPLE)—a data-driven framework for modeling complex fluids in flow

IF 2.3 3区 工程技术 Q2 MECHANICS
Charles D. Young, Patrick T. Corona, Anukta Datta, Matthew E. Helgeson, Michael D. Graham
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引用次数: 2

Abstract

An overarching challenge in rheology is to develop constitutive models for complex fluids for which we lack accurate first principles theory. A further challenge is that most experiments probing dynamical structure and rheology do so only in very simple flow fields that are not characteristic of the complex deformation histories experienced by material in a processing application. A recently developed experimental methodology holds potential to overcome this challenge by incorporating a fluidic four-roll mill (FFoRM) into scanning small-angle X-ray scattering instrumentation (sSAXS) (Corona, P. T. et al. Sci. Rep. 8, 15559 (2018); Corona, P. T. et al. Phys. Rev. Mater 6, 045603 (2022)) to rapidly generate large data sets of scattering intensity for complex fluids along diverse Lagrangian flow histories. To exploit this uniquely rich FFoRM-sSAXS data, we propose a machine learning framework, Scattering-Informed Microstructure Prediction under Lagrangian Evolution (SIMPLE), which uses FFoRM-sSAXS data to learn an evolution equation for the scattering intensity and an associated tensorial differential constitutive equation for the stress. The framework incorporates material frame indifference and invariance to arbitrary rotations by data preprocessing. We use autoencoders to find an efficient reduced order model for the scattering intensity and neural network ordinary differential equations to predict the time evolution of the model coordinates. The framework is validated on a synthetic FFoRM-sSAXS data set for a dilute rigid rod suspension. The model accurately predicts microstructural evolution and rheology for flows that differ significantly from those used in training. SIMPLE is compatible with but does not require material-specific constraints or assumptions.

Abstract Image

拉格朗日演化过程中散射信息的微观结构预测(SIMPLE)是一种数据驱动的复杂流体流动建模框架
流变学面临的首要挑战是开发复杂流体的本构模型,而我们缺乏准确的第一性原理理论。进一步的挑战是,大多数探索动态结构和流变学的实验只在非常简单的流场中进行,而这些流场并不是材料在加工应用中经历的复杂变形历史的特征。最近开发的一种实验方法有可能克服这一挑战,该方法将流体四辊轧机(FFoRM)与扫描小角度x射线散射仪器(sSAXS)结合起来(Corona, p.t.等)。科学。众议员8,15559 (2018);科罗娜,p.t.等。理论物理。Rev. Mater, 6, 045603(2022)),以快速生成沿不同拉格朗日流动历史的复杂流体散射强度的大型数据集。为了利用这些独特丰富的form - ssaxs数据,我们提出了一个机器学习框架,即拉格朗日演化下的散射通知微观结构预测(SIMPLE),该框架使用form - ssaxs数据来学习散射强度的演化方程和相关的应力张量微分本构方程。该框架通过数据预处理实现了材料框架对任意旋转的不变性和不变性。我们使用自编码器找到一个有效的降阶散射强度模型,并使用神经网络常微分方程来预测模型坐标的时间演化。该框架在稀刚性杆悬架的合成form - ssaxs数据集上进行了验证。该模型准确地预测了与训练中使用的流动有很大不同的微观结构演变和流变学。SIMPLE兼容但不需要特定于材料的约束或假设。
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来源期刊
Rheologica Acta
Rheologica Acta 物理-力学
CiteScore
4.60
自引率
8.70%
发文量
55
审稿时长
3 months
期刊介绍: "Rheologica Acta is the official journal of The European Society of Rheology. The aim of the journal is to advance the science of rheology, by publishing high quality peer reviewed articles, invited reviews and peer reviewed short communications. The Scope of Rheologica Acta includes: - Advances in rheometrical and rheo-physical techniques, rheo-optics, microrheology - Rheology of soft matter systems, including polymer melts and solutions, colloidal dispersions, cement, ceramics, glasses, gels, emulsions, surfactant systems, liquid crystals, biomaterials and food. - Rheology of Solids, chemo-rheology - Electro and magnetorheology - Theory of rheology - Non-Newtonian fluid mechanics, complex fluids in microfluidic devices and flow instabilities - Interfacial rheology Rheologica Acta aims to publish papers which represent a substantial advance in the field, mere data reports or incremental work will not be considered. Priority will be given to papers that are methodological in nature and are beneficial to a wide range of material classes. It should also be noted that the list of topics given above is meant to be representative, not exhaustive. The editors welcome feedback on the journal and suggestions for reviews and comments."
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