Collaborative Research: A New Nonlinear Modal Updating Framework for Soft, Hydrated Materials

协作研究：用于软水合材料的新型非线性模态更新框架

• 批准号: 1728186
• 负责人: Mehmet Kurt
• 金额: $ 23.82万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1728186&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Nonlinear; Modal

The mechanical properties of soft, hydrated materials have long been of interest to the scientific community. Using soft materials in mechanical designs is becoming increasingly prominent due to their obvious advantages such as flexibility in design and intentional exploitation of nonlinearity. Especially, the high-rate response of soft materials has received attention due to their many applications in robotics, materials and the biomedical sciences. Most soft and hydrated materials (e.g., biomaterials) exhibit complex mechanical behavior that is challenging to quantify due to measurement uncertainties, mechanical anisotropy and inhomogeneity. In this project, a new nonlinear dynamics-based system identification and model updating methodology will be formulated to characterize and model soft, hydrated materials. The findings of this research have the potential to drastically enhance the accuracy, cost-efficiency and accessibility of broadband soft material characterization, and, as such, it can be transformative in diverse interdisciplinary areas, such as soft robotic design, mechanical indentation measurements and soft tissue feedback during surgery. The resulting model updating approach for soft materials will be transformative in predictive engineering designs since it will enable the better utilization and integration of soft materials in diverse applications. This approach can be used for both exploiting the nonlinearities in soft mechanical designs, as well as for their health monitoring. This project will also provide training and mentoring opportunities for a diverse group of K12, undergraduate and graduate students, with a special emphasis on underrepresented groups. Interactive demonstrations of the developed methodology are planned to be displayed in local science festivals to engage the interest of the public in this scientific issue.The main objective of this project is to introduce a new nonlinear dynamics-based system identification and model updating methodology to characterize soft, hydrated materials. It is based on direct analysis of measured response time series, and construction of appropriately defined transitions in appropriately defined frequency-energy plots (FEPs) of a soft-tissue tester and sample system. The dynamics of an underlying conservative system (i.e., the corresponding system with no dissipative effects) modeling the tester is then correlated with the measured response by computing nonlinear normal modes (NNMs). In the conservative system model, soft tissues are modeled as highly flexible elements with stiffness and damping nonlinearities. Then, the reconciliation of the measured and simulated responses in the FEPs is utilized to estimate the broadband dissipative properties of the soft tissues. The experimental validation will be done by testing soft materials such as tendons, hydrated PDMS and brain tissue. The physics-based nonlinear approach in this study for model updating is unprecedented since it is based exclusively on direct time series analysis, and the framework is sufficiently general to be applicable to other engineering applications, such as the reconciliation of nonlinear finite element models with experimental measurements, and the accurate model reduction of mechanical and aerospace components. Moreover, this research will drastically increase our understanding of complicated dynamical transitions and modal interactions in systems with nonlinear viscoelastic properties. It will also enable predictive engineering design of such systems and will provide new insights into the broadband response of soft materials by developing and applying a uniquely new nonlinear-dynamics based model updating framework.

长期以来，科学界一直感兴趣的软材料的机械性能。在机械设计中使用软材料由于其明显的优势，例如在设计方面的灵活性和有意利用非线性，因此变得越来越突出。尤其是，由于它们在机器人技术，材料和生物医学科学中的许多应用，软材料的高速响应引起了人们的关注。大多数软水材料（例如，生物材料）表现出复杂的机械行为，这对于由于测量不确定性，机械各向异性和不均匀性而造成的具有挑战性。在该项目中，将制定一种新的基于非线性动力学的系统识别和模型更新方法，以表征和建模软性水合材料。这项研究的发现有可能大大提高宽带软材料表征的准确性，成本效益和可及性，因此，在多种跨学科区域（例如软机器人设计，机械凹痕测量值和手术过程中的软组织反馈）中，它可能在不同的跨学科领域进行变化。所得的软材料的模型更新方法将在预测工程设计中具有变革性，因为它将在不同应用中更好地利用和整合软材料。这种方法可以用于利用软机械设计中的非线性以及其健康监测。该项目还将为多样化的K12，本科和研究生提供培训和指导机会，并特别强调代表性不足的群体。计划在本地科学节上展示开发方法的互动演示，以吸引公众在这个科学问题上的兴趣。该项目的主要目标是引入一种新的基于非线性动态的系统识别和模型更新方法，以表征软性水合材料。它基于对测得的响应时间序列的直接分析，并基于适当定义的频率能量图（FEP）的适当定义过渡的构建。然后，通过计算非线性正常模式（NNM）将基础保守系统的动力学（即没有耗散效应的相应系统）与测量响应相关。在保守的系统模型中，软组织被建模为具有刚度和阻尼非线性的高度柔韧元素。然后，利用FEP中测量和模拟响应的对帐来估计软组织的宽带耗散特性。实验验证将通过测试软材料（例如肌腱，水合PDM和脑组织）来完成。本研究中基于物理的非线性方法用于模型更新是前所未有的，因为它仅基于直接时间序列分析，并且该框架足够通用，可以适用于其他工程应用程序，例如与实验测量的非线性有限元模型的和解以及机械和航空空间组合的准确模型。此外，这项研究将大大提高我们对具有非线性粘弹性特性系统中复杂的动态过渡和模态相互作用的理解。它还将实现此类系统的预测工程设计，并通过开发和应用基于独特的基于非线性的模型更新框架来为软材料的宽带响应提供新的见解。

项目成果

A Nonlinear Reduced-Order Model of the Corpus Callosum Under Planar Coronal Excitation

平面冠状激励下胼胝体的非线性降阶模型

• DOI: 10.1115/1.4046503
• 发表时间: 2020
• 期刊: Journal of Biomechanical Engineering
• 作者: Mojahed, Alireza;Abderezaei, Javid;Kurt, Mehmet;Bergman, Lawrence A.;Vakakis, Alexander F.
• 通讯作者: Vakakis, Alexander F.

Nonlinear Dynamical Behavior of the Deep White Matter during Head Impact

• DOI: 10.1103/physrevapplied.12.014058
• 发表时间: 2019-07-30
• 期刊: PHYSICAL REVIEW APPLIED
• 影响因子: 4.6
• 作者: Abderezaei, Javid;Zhao, Wei;Kurt, Mehmet
• 通讯作者: Kurt, Mehmet

Amplified Flow Imaging (aFlow): A Novel MRI-Based Tool to Unravel the Coupled Dynamics Between the Human Brain and Cerebrovasculature

• DOI: 10.1109/tmi.2020.3012932
• 发表时间: 2020-12-01
• 期刊: IEEE TRANSACTIONS ON MEDICAL IMAGING
• 影响因子: 10.6
• 作者: Abderezaei, Javid;Martinez, John;Kurt, Mehmet
• 通讯作者: Kurt, Mehmet