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Link statistics of dislocation network during strain hardening 应变硬化过程中位错网络的链路统计
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1016/j.jmps.2026.106533
Sh. Akhondzadeh , Hanfeng Zhai , Wurong Jian , Ryan B. Sills , Nicolas Bertin , Wei Cai
Dislocations are line defects in crystals that multiply and self-organize into a complex network during strain hardening. The length of dislocation links, connecting neighboring nodes within this network, contains crucial information about the evolving dislocation microstructure. By analyzing data from Discrete Dislocation Dynamics (DDD) simulations in face-centered cubic (fcc) Cu, we characterize the statistical distribution of link lengths of dislocation networks during strain hardening on individual slip systems. Our analysis reveals that link lengths on active slip systems follow a double-exponential distribution, while those on inactive slip systems conform to a single-exponential distribution. The distinctive long tail observed in the double-exponential distribution is attributed to the stress-induced bowing out of long links on active slip systems, a feature that disappears upon removal of the applied stress. We further demonstrate that both observed link length distributions can be explained by extending a one-dimensional Poisson process to include different growth functions. Specifically, the double-exponential distribution emerges when the growth rate for links exceeding a critical length becomes super-linear, which aligns with the physical phenomenon of long links bowing out under stress. This work advances our understanding of dislocation microstructure evolution during strain hardening and elucidates the underlying physical mechanisms governing its formation.
位错是晶体中的线缺陷,在应变硬化过程中会成倍增加并自组织成一个复杂的网络。连接该网络中相邻节点的位错链的长度包含了有关位错微观结构演变的关键信息。通过分析面心立方(fcc) Cu的离散位错动力学(DDD)模拟数据,我们描述了单个滑移系统应变硬化过程中位错网络链接长度的统计分布。分析表明,主动滑动系统的连杆长度服从双指数分布,而非主动滑动系统的连杆长度服从单指数分布。在双指数分布中观察到的独特的长尾归因于主动滑移系统中长连杆的应力诱导弯曲,这一特征在去除外加应力后消失。我们进一步证明,两个观测到的链路长度分布都可以通过扩展一维泊松过程来解释,以包含不同的生长函数。具体来说,当超过临界长度的连杆的增长率变为超线性时,出现双指数分布,这与长连杆在应力作用下弯曲的物理现象一致。这项工作促进了我们对应变硬化过程中位错微观结构演变的理解,并阐明了控制其形成的潜在物理机制。
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引用次数: 0
High-throughput dynamic experiments: The statistics of spall failure at ultra-high strain rates 高通量动态实验:超高应变速率下的片层破坏统计
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.jmps.2026.106523
Piyush Wanchoo , Rohit Berlia , Timothy P. Weihs , K.T. Ramesh
We describe a novel high-throughput experimental framework for dynamic loading based on an automated Laser-driven Microflyer Impact (LMI) platform. The framework is designed to rapidly explore the behavior of materials under dynamic loading, and we demonstrate this capability by performing an unprecedented number (173) of spall experiments to characterize the spall strength of a metal (polycrystalline pure copper). We examine pure polycrystalline copper with two different grain size distributions, one with a mean grain size well below 1.mum (referred to as the nanocrystalline copper) and one with a mean grain size  ∼ 8μm (referred to as the microcrystalline copper). The influence of microstructure and loading kinetics on the spall strength of metals remains poorly determined, largely due to the low throughput of conventional plate-impact experiments, motivating our study. We systematically vary the shock stress and (to a lesser degree) the tensile strain rate in our experiments, and examine the dependence of the spall strength on the prior shock stress, the tensile strain rate, and the grain size. The results provide clear evidence of a strong monotonic increase in spall strength with increasing tensile strain rate, and our microcrystalline copper results are largely consistent with the limited data available in the literature (often on material without a clearly defined grain size distribution). The influence of the prior shock stress on the spall strength is weaker than the strain rate effect, and appears (within our results) to depend also on the grain size distribution. Our results also demonstrate that our nanocrystalline copper has a significantly higher spall strength than its microcrystalline counterpart at these high strain rates. This is a reversal of the established trend that larger grain sizes can provide higher spall strengths when the grain size is sufficiently large, observed in some prior low-rate studies. Our extensive dataset provides validation data for microstructurally-aware spall models that predict an inverse grain-size dependence at high strain rates. This high-throughput LMI approach can also provide the datasets necessary for data-driven design of materials.
我们描述了一种基于自动激光驱动微飞片冲击(LMI)平台的动态加载实验框架。该框架旨在快速探索材料在动态载荷下的行为,我们通过执行前所未有的数量(173)的碎片实验来表征金属(多晶纯铜)的碎片强度来证明这种能力。我们研究了纯多晶铜,具有两种不同的晶粒尺寸分布,其中一种平均晶粒尺寸远低于1。Mum(称为纳米晶铜)和平均晶粒尺寸为 ~ 8μm(称为微晶铜)的铜。微观结构和加载动力学对金属的剥落强度的影响仍然很少确定,很大程度上是由于传统的板冲击实验的低通量,这激发了我们的研究。在我们的实验中,我们系统地改变了冲击应力和(在较小程度上)拉伸应变速率,并检查了剥落强度对先前冲击应力、拉伸应变速率和晶粒尺寸的依赖关系。结果提供了清晰的证据,表明随着拉伸应变速率的增加,微晶铜的强度会有很强的单调增加,我们的结果与文献中有限的数据(通常是在没有明确定义晶粒尺寸分布的材料上)基本一致。先前冲击应力对剥落强度的影响弱于应变率效应,并且(在我们的结果中)似乎也取决于晶粒尺寸分布。我们的研究结果还表明,在这些高应变速率下,我们的纳米晶铜具有明显高于微晶铜的片状强度。这是一个既定趋势的逆转,当晶粒尺寸足够大时,更大的晶粒尺寸可以提供更高的小块强度,在一些先前的低率研究中观察到。我们广泛的数据集为微观结构感知的碎片模型提供了验证数据,该模型预测了高应变速率下的逆粒度依赖性。这种高通量LMI方法还可以为数据驱动的材料设计提供必要的数据集。
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引用次数: 0
Modular hybrid machine learning and physics-based potentials for scalable modeling of Van der Waals heterostructures 范德华异质结构可扩展建模的模块化混合机器学习和基于物理的潜力
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-02-01 DOI: 10.1016/j.jmps.2026.106540
Hekai Bu , Wenwu Jiang , Penghua Ying , Ting Liang , Zheyong Fan , Wengen Ouyang
Accurately modeling the structural reconstruction and thermodynamic behavior of van der Waals (vdW) heterostructures remains a significant challenge due to the limitations of conventional force fields in capturing their complex mechanical, thermal, electronic, and tribological properties. To address these limitations, we develop a hybrid framework that combines single-layer machine-learned potential (sMLP) with physics-based anisotropic interlayer potential (ILP), effectively decoupling intralayer and interlayer interactions. This sMLP+ILP approach modularizes the modeling of vdW heterostructures like assembling LEGOs, reducing the required training configurations by at least an order of magnitude compared to the pure MLP approach, while retaining predictive accuracy and computational efficiency. We validate our framework by accurately reproducing the mechanical and thermal transport properties of graphite and bulk hexagonal boron nitride (h-BN), and by resolving intricate Moiré patterns in graphene/h-BN bilayer and graphene/graphene/h-BN trilayer heterostructures, achieving excellent agreement with experimental observations. Leveraging the developed sMLP+ILP approach, we reveal the stacking order-dependent formation of Moiré superlattice in trilayer graphene/h-BN/MoS2 heterostructures, demonstrating its ability to accurately model large-scale vdW systems comprising hundreds of thousands of atoms with near ab initio precision. Moreover, applications to nanotribology of h-BN nanoribbons demonstrate the framework’s unique ability to capture the critical influence of edge chemistry on sliding dynamics, specifically revealing that hydrogen passivation stabilizes zigzag edges against out-of-plane buckling, thereby inducing a pronounced enhancement of stick–slip friction. These findings demonstrate that the hybrid sMLP+ILP framework remarkably outperforms existing pure machine-learned or empirical potentials, offering a scalable and transferable solution for accurately and extensively modeling complex vdW materials across diverse applications, including sliding ferroelectricity, thermal management, resistive switching, and superlubric nanodevices.
由于常规力场在捕获其复杂的机械、热、电子和摩擦学性能方面的局限性,对范德华异质结构的结构重建和热力学行为进行准确建模仍然是一个重大挑战。为了解决这些限制,我们开发了一个混合框架,将单层机器学习势(sMLP)与基于物理的各向异性层间势(ILP)相结合,有效地解耦了层内和层间的相互作用。这种sMLP+ILP方法模块化了vdW异质结构的建模,就像组装乐高积木一样,与纯MLP方法相比,至少减少了所需的训练配置,同时保持了预测的准确性和计算效率。我们通过精确再现石墨和体六方氮化硼(h-BN)的机械和热输运性质,并通过解析石墨烯/h-BN双层和石墨烯/石墨烯/h-BN三层异质结构中复杂的摩尔条纹来验证我们的框架,与实验观察结果非常吻合。利用开发的sMLP+ILP方法,我们揭示了在三层石墨烯/h-BN/MoS2异质结构中moir超晶格的堆叠顺序依赖形成,证明了它能够以接近从头计算的精度精确模拟包含数十万原子的大规模vdW系统。此外,h-BN纳米带在纳米摩擦学上的应用表明,该框架具有独特的能力,可以捕捉边缘化学对滑动动力学的关键影响,特别是揭示了氢钝化可以稳定锯齿形边缘,防止面外屈曲,从而显著增强粘滑摩擦。这些发现表明,混合sMLP+ILP框架显著优于现有的纯机器学习或经验势能,为精确和广泛地建模复杂的vdW材料提供了可扩展和可转移的解决方案,适用于各种应用,包括滑动铁电性、热管理、电阻开关和超润滑纳米器件。
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引用次数: 0
Spontaneous oscillations in eukaryotic cilia and photo-responsive rods 真核纤毛和光反应杆的自发振荡
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-27 DOI: 10.1016/j.jmps.2026.106529
Daniele Agostinelli , Irene Anello , Reza Norouzikudiani , Antonio DeSimone
We present a comparative analysis of the chemo-mechanical mechanisms that drive spontaneous oscillations in two distinct active filamentous structures: photo-chemically deformable liquid crystal elastomer (LCE) rods and ATP-powered eukaryotic cilia. Using a unified framework of active planar rods, we develop simplified mathematical models for both systems. We reduce the governing partial differential equations to one-degree-of-freedom (1-DOF) nonlinear oscillators, each undergoing a supercritical Hopf bifurcation. For these reduced models, we obtain explicit analytical expressions for the onset and characteristics of self-sustained oscillations. Despite the common mathematical structure, the underlying physical mechanisms are fundamentally different. For LCEs, self-oscillation is an inertial phenomenon driven by an elastic-inertial feedback over the timescale of the photochemical reaction. In contrast, cilia live in the inertia-less regime, and the instability is driven by a negative effective damping (motive force) that arises from the mechanochemistry of molecular motors. The analytical predictions for critical activation thresholds, frequencies, and amplitudes agree with full nonlinear simulations, providing quantitative insight into the dynamics of these complex self-oscillating systems.
我们提出的化学-机械机制的比较分析,驱动自发振荡在两个不同的活性丝状结构:光化学变形液晶弹性体(LCE)棒和atp供电真核纤毛。利用活动平面杆的统一框架,我们建立了两个系统的简化数学模型。我们将控制偏微分方程化为一自由度(1-DOF)非线性振子,每个振子都经历一个超临界Hopf分岔。对于这些简化模型,我们得到了自持续振荡的开始和特征的显式解析表达式。尽管有共同的数学结构,潜在的物理机制是根本不同的。对于LCEs,自振荡是一种由光化学反应时间尺度上的弹性惯性反馈驱动的惯性现象。相比之下,纤毛处于无惯性状态,这种不稳定性是由分子马达的机械化学作用产生的负有效阻尼(动力)驱动的。关键激活阈值、频率和振幅的分析预测与完整的非线性模拟一致,为这些复杂的自振荡系统的动力学提供了定量的见解。
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引用次数: 0
Buckling and post-buckling of strain-softening and strain-stiffening columns 应变软化和应变加劲柱的屈曲和后屈曲
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1016/j.jmps.2026.106520
Shivam Agarwal , Yuzhen Chen , Lihua Jin
Axially compressed columns made of a neo-Hookean material show continuous buckling, snap-through buckling and snap-back buckling as the width-to-length ratio increases. Interestingly, some architected columns with microstructures in literature exhibit snap-through buckling at considerably lower width-to-length ratios. We attempt to find a general reasoning for this unconventional behavior, as the homogenized effect of the void-based microstructures is equivalent to strain-softening behavior of a continuum. We hypothesize that the strain-softening and strain-stiffening effects significantly affect the buckling and post-buckling of axially compressed columns. We use simple phenomenological models, such as, the reduced polynomial model, to capture both the strain-softening and strain-stiffening behaviors, and the hyperfoam model, to capture extreme strain-softening as observed in typical elastomeric foams. Our finite element simulations on continuum columns produce post-buckling responses qualitatively similar to those architected or hierarchical columns, which verifies our hypothesis. Furthermore, we use asymptotic analysis to find the buckling and post-buckling responses for a wide range of width-to-length ratio and a varying degree of strain-softening/-stiffening. Results for the reduced polynomial model show that the strain-softening effect in slender columns or the strain-stiffening effect in wide columns can considerably reduce the post-buckling slope. Moreover, with the hyperfoam model, we discover a new regime of negative post-buckling slope, which leads to snap-through buckling in very slender columns. Our buckling experiments on the columns made of common open-cell and closed-cell foams validate our findings. At last, we present a discrete model, inspired by the Euler-Bernoulli beam theory, to explain the post-buckling behavior of strain-softening/-stiffening columns. Strain-softening/-stiffening can therefore be harnessed, apart from the width-to-length ratio, to tune the deformation of a column in various applications pertaining to construction, metamaterials, soft robotics, haptics, and packaging, to name a few.
由新型hookean材料制成的轴向压缩柱随着宽长比的增加,呈现出连续屈曲、卡通屈曲和卡回屈曲的现象。有趣的是,在文献中,一些具有微结构的结构柱在相当低的宽长比下表现出咔嗒通屈曲。我们试图找到这种非常规行为的一般原因,因为基于空洞的微观结构的均匀化效应相当于连续体的应变软化行为。我们假设应变软化和应变加劲效应显著影响轴压柱的屈曲和后屈曲。我们使用简单的现象学模型,例如,简化多项式模型,来捕捉应变软化和应变硬化行为,以及超泡沫模型,来捕捉典型弹性体泡沫中观察到的极端应变软化。我们对连续柱的有限元模拟产生的屈曲后响应在质量上与那些结构或分层柱相似,这验证了我们的假设。此外,我们使用渐近分析找到了宽长比范围内和不同程度的应变软化/刚度下的屈曲和后屈曲响应。简化多项式模型的结果表明,细长柱的应变软化效应或宽柱的应变加劲效应可以显著降低后屈曲斜率。此外,利用超泡沫模型,我们发现了一种新的负后屈曲斜率,这导致了非常细的柱的卡断屈曲。我们对普通开孔和闭孔泡沫柱进行了屈曲实验,验证了我们的发现。最后,我们提出了一个离散模型,受欧拉-伯努利梁理论的启发,来解释应变软化/加劲柱的后屈曲行为。因此,除了宽度与长度的比例外,应变软化/加强可以用于调整建筑、超材料、软机器人、触觉和包装等各种应用中的柱的变形。
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引用次数: 0
Nonlinear mechanics of arterial growth 动脉生长的非线性力学
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.jmps.2026.106525
Aditya Kumar , Arash Yavari
In this paper, we formulate a geometric theory of the mechanics of arterial growth. An artery is modeled as a finite-length thick shell that is made of an incompressible nonlinear anisotropic solid. An initial radially-symmetric distribution of finite radial and circumferential eigenstrains is also considered. Bulk growth is assumed to be isotropic. A novel framework is proposed to describe the time evolution of growth, governed by a competition between the elastic energy and a growth energy. The governing equations are derived through a two-potential approach and using the Lagrange-d’Alembert principle. An isotropic dissipation potential is considered, which is assumed to be convex in the rate of growth function. Several numerical examples are presented that demonstrate the effectiveness of the proposed model in predicting the evolution of arterial growth and the intricate interplay among eigenstrains, residual stresses, elastic energy, growth energy, and dissipation potential. A distinctive feature of the model is that the growth variable is not constrained by an explicit upper bound; instead, growth naturally approaches a steady-state value as a consequence of the intrinsic energetic competition. Several numerical examples illustrate the efficiency and robustness of the proposed framework in modeling arterial growth.
在本文中,我们提出了动脉生长力学的几何理论。动脉被建模为由不可压缩的非线性各向异性固体组成的有限长厚壳。还考虑了有限径向和周向特征应变的初始径向对称分布。假定体增长是各向同性的。提出了一种描述生长时间演化的新框架,该框架由弹性能和生长能之间的竞争支配。通过双势法和拉格朗日-达朗贝尔原理推导了控制方程。考虑了各向同性耗散势,假设其在生长速率函数中为凸。几个数值算例表明,该模型在预测动脉生长演化以及特征应变、残余应力、弹性能、生长能和耗散势之间复杂的相互作用方面是有效的。该模型的一个显著特征是,增长变量不受明确上界的约束;相反,作为内在的能量竞争的结果,增长自然接近于稳态值。几个数值例子说明了所提出的框架在模拟动脉生长方面的有效性和鲁棒性。
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引用次数: 0
Machine learning-energized framework for rapid and precise inverse design of programmable structures with multiple design variables 基于机器学习的多设计变量可编程结构快速精确反设计框架
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-18 DOI: 10.1016/j.jmps.2026.106524
Qingqing Chen, Chao Yuan, Tiejun Wang
Programmable structures enable autonomous deformation to achieve target three-dimensional (3D) shapes by triggering stimuli-responsive mismatch strain embedded in precursory configurations. However, the growing complexity of target 3D geometries makes it challenging to efficiently and accurately find optimal precursory design variables in a high-dimensional design space. Here, we propose a machine learning-energized framework for rapid and precise inverse design of programmable 3D structures. Firstly, a finite substructure algorithm is proposed to rapidly generate a large-scale database that accurately maps multiple design variables to programmable deformations. To this end, we decompose the full-scale structure into overlapping substructures and employ machine learning to augment the design variable-substructural deformation data pairs from limited finite element analyses. The deformed substructures are then sequentially stitched to reconstruct global deformation by optimal rotation and translation that minimize the Euclidean distance of overlapping regions. Compared to finite element analysis, the proposed finite substructure algorithm accelerates the forward prediction by four orders of magnitude. Based on the large-scale database, a well-trained neural network is obtained to inversely generate the coarse estimation of target design variables, which equips the gradient-free optimization with prior knowledge to approach the optimal result at an accelerated pace. Also, we establish a 3D printing and vacuum actuation platform to validate the inversely designed pneumatically programmable structures. Finally, we show a bio-inspired robotic arm capable of warping and grasping complex 3D objects to highlight the applicability of the proposed inverse design approach. This work provides a feasible paradigm for the inverse design of programmable structures, paving the way for potential applications in soft robotics and deployable devices.
可编程结构通过触发嵌入在前驱结构中的刺激响应不匹配应变,实现自主变形,以实现目标三维(3D)形状。然而,随着目标三维几何形状的日益复杂,如何在高维设计空间中高效、准确地找到最优的前兆设计变量成为一项挑战。在这里,我们提出了一个机器学习驱动的框架,用于可编程3D结构的快速精确逆设计。首先,提出了一种有限子结构算法,快速生成大规模数据库,将多个设计变量精确映射到可编程变形;为此,我们将全尺寸结构分解为重叠的子结构,并使用机器学习来增加来自有限有限元分析的设计变量-子结构变形数据对。然后将变形的子结构依次缝合,通过最小化重叠区域的欧几里得距离的最佳旋转和平移来重建全局变形。与有限元分析相比,本文提出的有限子结构算法将前向预测速度提高了4个数量级。在大规模数据库的基础上,得到训练良好的神经网络逆生成目标设计变量的粗估计,使无梯度优化具有先验知识,加快逼近最优结果。此外,我们还建立了3D打印和真空驱动平台,以验证反设计的气动可编程结构。最后,我们展示了一个能够弯曲和抓取复杂3D物体的仿生机械臂,以突出所提出的逆设计方法的适用性。这项工作为可编程结构的逆向设计提供了一个可行的范例,为软机器人和可部署设备的潜在应用铺平了道路。
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引用次数: 0
A physically based mechanical model for Mullins effect in thermoplastic polyurethanes 热塑性聚氨酯中Mullins效应的物理力学模型
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.jmps.2026.106532
Samuel C. Lamont, David J. Walters
Despite decades of research, connecting the chemical and physical structure of thermoplastic polyurethanes to their mechanical properties remains highly challenging. Of particular note are their large-deformation and rate-dependent behaviors, which vary greatly with molecular chemistry, including the type and relative content of soft and hard segments. In this work, we develop a physically motivated mechanical theory for predicting the behavior of thermoplastic polyurethanes. The theory incorporates a representation of microstructural evolution during mechanical deformation, which captures the signatures of stress softening over cyclic loading (commonly referred to as the Mullins effect). There are only eight physically motivated fitting parameters, including a direct dependence on the hard segment fraction. The model predicts that increasing the hard segment fraction leads to higher stiffness and greater energy dissipation, in quantitative agreement with published experimental data. We provide a comprehensive analysis of the model and validate its predictions across several independent datasets focused on mechanical characterization. Direct comparisons to experimental data demonstrate its predictive capability on the effect of loading rate, cyclic deformations, and applied tension or compression. Altogether, this work establishes a predictive framework that connects polymer chemistry and microstructure to emergent mechanical behaviors.
尽管经过数十年的研究,将热塑性聚氨酯的化学和物理结构与其机械性能联系起来仍然是极具挑战性的。特别值得注意的是它们的大变形和速率依赖行为,这些行为随分子化学变化很大,包括软段和硬段的类型和相对含量。在这项工作中,我们开发了一个物理动机的力学理论来预测热塑性聚氨酯的行为。该理论结合了机械变形过程中微观结构演变的表征,它捕捉了循环加载过程中应力软化的特征(通常被称为穆林斯效应)。只有八个物理动机拟合参数,包括直接依赖于硬段分数。该模型预测,增加硬段分数会导致更高的刚度和更大的能量耗散,与已发表的实验数据在定量上一致。我们对该模型进行了全面的分析,并通过几个独立的数据集验证了其预测,这些数据集中在机械特性上。与实验数据的直接比较证明了其对加载速率、循环变形和施加的张力或压缩的影响的预测能力。总之,这项工作建立了一个预测框架,将聚合物化学和微观结构与紧急机械行为联系起来。
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引用次数: 0
Unveiling the evolving fracture toughness: The R-curve phenomenon in double network hydrogels 揭示断裂韧性的演化:双网状水凝胶中的r曲线现象
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-27 DOI: 10.1016/j.jmps.2026.106531
Wenjing Lu , Shuai Xu , Chong Wang , Zishun Liu
Double network (DN) hydrogels exhibit remarkable mechanical properties, most notably their high fracture toughness, which expands their potential applications across various fields. To guarantee the reliability of DN gels in practical uses, an in-depth comprehension of their fracture characteristics is crucial. Previous studies on the fracture behavior of DN gels mainly concentrated on the crack initiation process, often treating fracture toughness as a constant characteristic of the material. In contrast, our experimental research unveils the evolving fracture toughness as a fracture resistance curve (R-curve) in DN gels, indicating that fracture toughness increases with crack propagation until reaching a plateau of steady-state, rather than remaining constant. This phenomenon is attributed to mechanisms of crack tip softening and stress de-concentration resulting from the fracture of the brittle PAMPS network in DN gels. We have identified three critical parameters, i.e., the initiation fracture toughness (Ginit), steady state toughness (Gss), steady state crack extension length (Lss), to quantify this behavior. Additionally, we explore the effect of pre-damage of PAMPS network on the R-curve and develop a theoretical model linking the degree of pre-damage (h) to Gss. Our model effectively predicts the R-curve of DN gels under various pre-damage conditions. Furthermore, by considering different PAMPS network formulations, we establish a scaling law linking (Gss−Ginit) with Lss, thus creating a unified framework for understanding the internal physical properties of diverse DN gels. These insights also differentiate the toughening mechanisms in DN gels: energy dissipation from the PAMPS network affects (Gss−Ginit), while the Ginit reflects the intrinsic fracture properties governed by the elastic PAAm network. We anticipate that our experimental results and theoretical models will enhance the understanding of fracture in soft tough materials.
双网(DN)水凝胶具有优异的力学性能,特别是其高断裂韧性,这扩大了其在各个领域的潜在应用。为了保证DN凝胶在实际应用中的可靠性,深入了解其断裂特性至关重要。以往对DN凝胶断裂行为的研究主要集中在裂纹萌生过程上,往往将断裂韧性作为材料的恒定特征。相比之下,我们的实验研究揭示了断裂韧性在DN凝胶中的演变为断裂阻力曲线(r曲线),表明断裂韧性随着裂纹的扩展而增加,直到达到稳态状态的平台,而不是保持不变。这一现象归因于DN凝胶中脆性PAMPS网络断裂导致的裂纹尖端软化和应力去集中机制。我们确定了三个关键参数,即起裂韧性(Ginit),稳态韧性(Gss),稳态裂纹扩展长度(Lss),以量化这种行为。此外,我们还探讨了PAMPS网络预损伤对r曲线的影响,并建立了预损伤程度(h)与Gss之间的理论模型。我们的模型有效地预测了DN凝胶在各种预损伤条件下的r曲线。此外,通过考虑不同的PAMPS网络配方,我们建立了连接(Gss−Ginit)与Lss的标度定律,从而为理解不同DN凝胶的内部物理性质创建了统一的框架。这些见解也区分了DN凝胶中的增韧机制:PAMPS网络的能量耗散影响(Gss - Ginit),而Ginit反映了弹性PAAm网络控制的固有断裂特性。我们期望我们的实验结果和理论模型能增进对软韧材料断裂的认识。
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引用次数: 0
Multiscale modeling of coupled thermo-hydro-mechanical- chemical behavior in hydrate-bearing sediment 含水沉积物热-水-力-化学耦合行为的多尺度模拟
IF 6 2区 工程技术 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Pub Date : 2026-04-01 Epub Date: 2026-01-09 DOI: 10.1016/j.jmps.2026.106512
Jidu Yu , Jidong Zhao , Weijian Liang
This study presents a hybrid continuum-discrete multiscale computational framework that integrates the material point method (MPM) and the discrete element method (DEM) to model fully coupled thermo-hydro-mechanical-chemical (THMC) behavior of hydrate-bearing sediments (HBS). Key innovation of the framework lies in its direct use of DEM to model microscale mechanisms, such as hydrate bond degradation, particle rearrangement, and pore evolution, thereby bypassing the need for conventional elastoplastic constitutive models to define effective stress. We show that a simple hydrate saturation-dependent contact model within the DEM can effectively reproduce characteristic shear and volumetric responses of HBS under various hydrate saturation and confining stresses. By embedding a DEM-based representative volume element (RVE) at each material point in the MPM grid, microscale mechanical behaviors are seamlessly homogenized to inform large-deformation macroscale multiphysics processes. Numerical simulations of biaxial compression and indenter penetration demonstrate the framework’s capability to capture critical phenomena, including shear band formation, shear-induced dilation, and the generation of negative excess pore pressure that drives localized hydrate dissociation. The results further reveal that while higher hydrate saturation enhances shear strength, it also promotes brittle failure and intensified dissociation. Conversely, increased confining stress suppresses volumetric dilation and stabilizes the sediment by mitigating the development of negative pore pressure. This multiscale approach provides a powerful new tool for elucidating complex THMC interactions in HBS, with important implications for assessing hydrate-related geohazards and optimizing gas extraction strategies.
本文提出了一种结合物质点法(MPM)和离散元法(DEM)的连续-离散多尺度混合计算框架,对含水沉积物(HBS)的热-水-机械-化学(THMC)完全耦合行为进行建模。该框架的关键创新在于直接使用DEM来模拟微观尺度机制,如水合物键降解、颗粒重排和孔隙演化,从而绕过了传统弹塑性本构模型来定义有效应力的需要。研究表明,在不同水合物饱和度和围应力条件下,简单的水合物饱和度依赖接触模型可以有效地再现HBS的剪切和体积特征响应。通过在MPM网格中的每个材料点嵌入基于dem的代表性体积单元(RVE),可以无缝地均匀化微尺度力学行为,从而为大变形的宏观多物理场过程提供信息。双轴压缩和压头穿透的数值模拟表明,该框架能够捕获关键现象,包括剪切带形成、剪切引起的膨胀以及产生负超孔隙压力,从而驱动局部水合物解离。结果进一步表明,较高的水合物饱和度在提高抗剪强度的同时,也促进了脆性破坏,加剧了解离。相反,围应力的增加抑制了体积膨胀,并通过减缓负孔隙压力的发展来稳定沉积物。这种多尺度方法为阐明HBS中复杂的THMC相互作用提供了强大的新工具,对评估水合物相关地质灾害和优化天然气开采策略具有重要意义。
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引用次数: 0
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Journal of The Mechanics and Physics of Solids
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