特别地，研究小组推导了一种降阶三维阻力理论(3D-RFT)，该理论能够准确、快速地预测任意形状物体侵入颗粒介质时的阻力应力分布。借助颗粒介质的连续描述、一套全面的空间对称约束和有限的参考数据，研究人员开发了一种自一致且准确的3D-RFT。
研究人员在广泛的情况下验证了模型的能力，并表明它可以快速重新校准到不同的介质和入侵者表面类型。导致3D-RFT的前提是预期应用于其他具有强超局部化入侵行为的软材料。
据介绍，软质材料通常表现出复杂的行为，通过明显的固体和流体状态过渡。虽然针对这些材料存在越来越多的微尺度模拟方法，但通常需要封装宏观物理的降阶模型来预测外部物体如何与软介质相互作用。这种方法可以为从撞击和穿透问题到在自然地形上移动的各种情况提供直接的见解。这项工作提出了一个系统的计划，从基本基础上开发三维(3D)软材料降阶模型，使用连续介质对称性和流变学原理。
附：英文原文
Title: Mechanistic framework for reduced-order models in soft materials: Application to three-dimensional granular intrusion
Author: Agarwal, Shashank, Goldman, Daniel I., Kamrin, Ken
Issue&Volume: 2023-1-17
Abstract: Soft materials often display complex behaviors that transition through apparent solid- and fluid-like regimes. While a growing number of microscale simulation methods exist for these materials, reduced-order models that encapsulate the macroscale physics are often desired to predict how external bodies interact with soft media. Such an approach could provide direct insights in diverse situations from impact and penetration problems to locomotion over natural terrains. This work proposes a systematic program to develop three-dimensional (3D) reduced-order models for soft materials from a fundamental basis using continuum symmetries and rheological principles. In particular, we derive a reduced-order, 3D resistive force theory (3D-RFT), which is capable of accurately and quickly predicting the resistive stress distribution on arbitrary-shaped bodies intruding through granular media. Aided by a continuum description of the granular medium, a comprehensive set of spatial symmetry constraints, and a limited amount of reference data, we develop a self-consistent and accurate 3D-RFT. We verify the model capabilities in a wide range of cases and show that it can be quickly recalibrated to different media and intruder surface types. The premises leading to 3D-RFT anticipate application to other soft materials with strongly hyperlocalized intrusion behavior.
DOI: 10.1073/pnas.2214017120
Source: https://www.pnas.org/doi/10.1073/pnas.2214017120