热润滑脂中应力积累和松弛的数据驱动流变特性

IF 3 2区 工程技术 Q2 MECHANICS
Nagrani, Pranay P., Kulkarni, Ritwik V., Kelkar, Parth U., Corder, Ria D., Erk, Kendra A., Marconnet, Amy M., Christov, Ivan C.
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引用次数: 0

摘要

热润滑脂通常用作热界面材料,是一种复杂的糊状混合物,由基础聚合物组成,其中分散了致密的金属(或陶瓷)填充颗粒,以改善材料的传热性能。它们具有复杂的流变特性,影响热界面材料在其使用寿命期间的性能。我们对热润滑脂进行了流变学实验,观察了应力松弛和应力积累机制。这种复杂的流体状材料的随时间变化的流变行为不能被通常用于描述这些材料的稳定剪切减薄模型所捕获。我们发现触敏-弹粘塑性(TEVP)和非线性-弹粘塑性(NEVP)本构模型分别表征了观察到的应力松弛和累积机制。具体来说,我们在基于物理信息神经网络(pinn)的数据驱动方法中使用这些模型。在实验中,用pin - ns来解决从动态响应中确定流变模型参数的逆问题。这些训练数据是通过使用剪切流变仪在不同(恒定)剪切速率下的启动流量实验产生的。我们通过将预测的剪切应力演化与训练数据集中未使用的剪切速率下的实验进行比较,验证了“学习”模型。我们通过求解一个数值正演问题来进一步验证所学习的TEVP模型,以确定输入阶跃应变剖面的剪切应力演化。同时,通过与材料流动曲线的稳定Herschel-Bulkley拟合进行比较,进一步验证了NEVP模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven rheological characterization of stress buildup and relaxation in thermal greases
Thermal greases, often used as thermal interface materials, are complex paste-like mixtures composed of a base polymer in which dense metallic (or ceramic) filler particles are dispersed to improve the heat transfer properties of the material. They have complex rheological properties that impact the performance of the thermal interface material over its lifetime. We perform rheological experiments on thermal greases and observe both stress relaxation and stress buildup regimes. This time-dependent rheological behavior of such complex fluid-like materials is not captured by steady shear-thinning models often used to describe these materials. We find that thixo-elasto-visco-plastic (TEVP) and nonlinear-elasto-visco-plastic (NEVP) constitutive models characterize the observed stress relaxation and buildup regimes, respectively. Specifically, we use the models within a data-driven approach based on physics-informed neural networks (PINNs). PINNs are used to solve the inverse problem of determining the rheological model parameters from the dynamic response in experiments. These training data are generated by startup flow experiments at different (constant) shear rates using a shear rheometer. We validate the “learned” models by comparing their predicted shear stress evolution to experiments under shear rates not used in the training datasets. We further validate the learned TEVP model by solving a forward problem numerically to determine the shear stress evolution for an input step-strain profile. Meanwhile, the NEVP model is further validated by comparison to a steady Herschel–Bulkley fit of the material’s flow curve.
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来源期刊
Journal of Rheology
Journal of Rheology 物理-力学
CiteScore
6.60
自引率
12.10%
发文量
100
审稿时长
1 months
期刊介绍: The Journal of Rheology, formerly the Transactions of The Society of Rheology, is published six times per year by The Society of Rheology, a member society of the American Institute of Physics, through AIP Publishing. It provides in-depth interdisciplinary coverage of theoretical and experimental issues drawn from industry and academia. The Journal of Rheology is published for professionals and students in chemistry, physics, engineering, material science, and mathematics.
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