今日更新:International Journal of Solids and Structures 1 篇,Journal of the Mechanics and Physics of Solids 2 篇,Thin-Walled Structures 2 篇
Homogenization-based chemomechanical properties of dissipative heterogeneous composites under transient mass diffusion
Yiqi Mao, Cong Wang, Yikun Wu, Haosen Chen
doi:10.1016/j.ijsolstr.2023.112623
瞬态质量扩散下基于均质化的耗散非均质复合材料化学力学性能
The chemomechanical properties of heterogeneous composites under mass diffusion are of significance in modern advanced technology and engineering applications. A homogenization-based two-scale chemomechanical model is developed for heterogeneous composites undergoing chemical mass diffusion. A two-scale incremental variational formulation is established for heterogeneous composites consisting of multiconstituents featuring local dissimilar diffusion-deformation properties. The minimization problems for coupled chemomechanical behaviors are solved for both macrostructure and microstructure contexts, where the macroscopic material properties are extracted from the results of local boundary value problem on the nested representative volume elements (RVEs). Through a staggered finite element method (FEM) implementation procedure, the proposed homogenization-based two-scale solution algorithm is implemented in the FEM package ABAQUS (V6.14). The developed variational model and tangential algorithm is checked by solving chemomechanical properties of particles enforced composite, where several numerical examples are conducted applying two-scale solution algorithm and validated by full-scale simulations. Parametric studies are carried out on the size effects of RVEs, with respect to the ‘inertia effect’ associated with ‘moment of mass concentration’, and the coupling mechanisms are discussed for mechanical and chemical solutions. To the end, the inelastic dissipations are solved on subscale BVPs and their effects on the mechanical deformation and chemical mass diffusion are checked. The contributions of this work are mainly two-folds. One is the theoretical advance for self-consistent homogenization modeling of the coupled multi-physics of heterogeneous composites, and a rigorous FE2 solution procedure. The other is providing numerical reference for evaluation of approximation algorithm as well as advanced data-driven method, which is needed for high-efficient material design.
非均相复合材料在质量扩散作用下的化学力学性能在现代先进技术和工程应用中具有重要意义。建立了基于均质化的非均质复合材料化学质量扩散双尺度化学力学模型。建立了具有局部不同扩散变形特性的多组分非均相复合材料的双尺度增量变分公式。从嵌套代表性体积元的局部边值问题中提取材料的宏观性能,解决了宏观和微观环境下耦合化学力学行为的最小化问题。通过交错有限元法(FEM)实现程序,在有限元软件包ABAQUS (V6.14)中实现了基于均质化的双尺度求解算法。通过求解颗粒增强复合材料的化学力学性能,验证了所建立的变分模型和切向算法的正确性。对rve的尺寸效应进行了参数化研究,涉及与“质量浓度矩”相关的“惯性效应”,并讨论了机械和化学解决方案的耦合机制。最后,对亚尺度BVPs的非弹性耗散进行了求解,并对其对力学变形和化学质量扩散的影响进行了校核。这项工作的贡献主要有两方面。一是提出了非均质复合材料耦合多物理场自洽均质建模的理论进展,并建立了严格的FE2求解程序。二是为高效材料设计所需的逼近算法和先进的数据驱动方法的评估提供数值参考。
Multi-scale spallation model for single-crystal ductile metals incorporating microscopic mechanism of void nucleation
Haonan Sui, Wenbin Liu, Yin Zhang, Huiling Duan
doi:10.1016/j.jmps.2023.105520
含孔洞成核微观机制的单晶延性金属多尺度散裂模型
Spall strength characterizes the tensile performance of materials under high strain rates. However, theoretical prediction of the spall strength has long been a big challenge since it involves information on the damage evolution at multiple length scales. In this work, a multi-scale model is proposed for the spallation of single-crystal ductile metals, which includes both a microscopic model of damage nucleation and an effective correlation between the microscopic physical mechanisms and the macroscopic mechanical behaviors. An energy criterion to predict the threshold stress for damage nucleation is firstly proposed based on a near-realistic three-dimensional configuration of dislocation emission. The statistical characteristics of the damage nucleation threshold are analyzed, based on which a multi-scale spall model is proposed to couple the physical essence at the micro-scale to the macro-scale, revealing the influences of temperature, vacancy concentration, and strain rate on spall strength. An approximate form of the multi-scale model is proposed by dimensional analysis and reasonable approximation, which is consistent with the empirical model summarized from experimental results, and further deduced to a simple scaling law that predicts the transition in the strain rate-sensitivity of spall strength.
剥落强度表征材料在高应变率下的拉伸性能。然而,碎屑强度的理论预测一直是一个很大的挑战,因为它涉及到多长度尺度下的损伤演化信息。本文提出了单晶韧性金属裂裂的多尺度模型,该模型既包括损伤成核的微观模型,又包括微观物理机制与宏观力学行为之间的有效关联。基于接近真实的位错发射三维结构,首次提出了一种预测损伤成核阈值应力的能量准则。分析了损伤成核阈值的统计特征,在此基础上提出了将微观尺度的物理本质与宏观尺度相结合的多尺度剥落模型,揭示了温度、空位浓度和应变速率对剥落强度的影响。通过量纲分析和合理逼近,提出了多尺度模型的近似形式,该模型与实验结果总结的经验模型相一致,并进一步推导出一个简单的标度律,该标度律预测了破碎强度应变速率敏感性的转变。
Entropic pressure on fluctuating solid membranes
Rubayet Hassan, Maria Alejandra Garzon, Wei Gao, Fatemeh Ahmadpoor
doi:10.1016/j.jmps.2023.105523
波动固体膜上的熵压
Biological and crystalline membranes exhibit noticeable fluctuations at room temperature due to their low bending stiffness. These fluctuations have a significant impact on their overall mechanical behavior and interactions with external objects. When two membranes come into proximity, they mutually suppress each other’s fluctuations, leading to a repulsive force that plays a pivotal role in the mechanical behavior of these membranes. From the mechanics point of view, crystalline membranes are modeled as solid membranes with inherent shear resistance, whereas biological membranes are commonly described as fluidic entities without shear resistance. Under this premise, the entropic force between two fluctuating biological membranes is proposed to scale as p∝1/d3, where d is the intermembrane distance. Yet, there are numerous instances where these membranes display shear resistance and behave akin to solid membranes. In this paper, we develop a statistical mechanics model within nonlinear elasticity to study the entropic force acting on a confined, fluctuating solid membrane. We demonstrate that, due to the nonlinear elasticity of solid membranes, the entropic force scales differently compared to that of fluid membranes. Our predictions align well with the results obtained from molecular dynamics simulations involving graphene, a representative of a solid membrane, confined between two rigid walls.
生物膜和晶体膜由于弯曲刚度较低,在室温下会出现明显的波动。这些波动对它们的整体机械行为以及与外部物体的相互作用有重大影响。当两层膜靠近时,它们会相互抑制对方的波动,从而产生一种斥力,这种斥力在这些膜的机械行为中起着关键作用。从力学角度来看,晶体膜被模拟为具有固有剪切阻力的固体膜,而生物膜通常被描述为没有剪切阻力的流体实体。在这一前提下,两个波动的生物膜之间的熵力被认为与 p∝1/d3(其中 d 是膜间距离)成比例。然而,在许多情况下,这些膜会显示剪切阻力,表现得与固态膜类似。在本文中,我们在非线性弹性中建立了一个统计力学模型,以研究作用于受限波动固体膜上的熵力。我们证明,由于固体膜的非线性弹性,熵力的大小与流体膜不同。我们的预测与石墨烯分子动力学模拟的结果非常吻合,石墨烯是固态膜的代表,被限制在两个刚性壁之间。
Mechanical properties of additively manufactured lattice structures designed by deep learning
Nurullah YÜKSEL, Oğulcan EREN, Hüseyin Rıza BÖRKLÜ, Hüseyin Kürşad SEZER
doi:10.1016/j.tws.2023.111475
基于深度学习的增材制造晶格结构力学性能研究
Lattice structures, characterized by their repetitive lightweight cellular forms, enable more effective load distribution compared to solid bodies. Designing lattice structures with tailored mechanical properties remains challenging due to the numerous design variables and their complex relationship with mechanical performance. This paper presents a novel approach employing a deep learning-based Generative Adversarial Network (GAN) model to address this engineering challenge. With its potential for creativity and innovation, GAN provides design diversity that cannot be achieved with traditional design methods or other generative design models. Distinct from previous studies, the GAN training data set consists of lattice structures with improved mechanical properties obtained using parametric design and simulated annealing method. This data set enables the GAN model to create lattice structures with high strength-to-weight ratio. These lattice designs were fabricated using a commercial Material Jetting Additive Manufacturing (MJ-AM) machine, allowing for the production of complex structures. The mechanical performance of the 3D-printed unit cell samples was evaluated through Finite Element Analysis (FEA), compression, and impact testing. The results reveal that the lattice structures generated using the GAN model demonstrated improved mechanical strength (i.e. up to 108% and 150% improved strength and elongation performance, respectively). This study shows AI's potential to widen lattice structure design space and create tailored parts with improved mechanical properties. The research also paves the way for future exploration of deep learning techniques in revolutionizing the design and fabrication of parts with tailored mechanical properties.
晶格结构的特点是其重复的轻质细胞形式,与实体相比,可以更有效地分配负载。由于众多的设计变量及其与力学性能的复杂关系,设计具有定制力学性能的晶格结构仍然具有挑战性。本文提出了一种采用基于深度学习的生成对抗网络(GAN)模型来解决这一工程挑战的新方法。GAN具有创造和创新的潜力,提供了传统设计方法或其他生成设计模型无法实现的设计多样性。与以往的研究不同,GAN训练数据集由通过参数化设计和模拟退火方法获得的具有改进力学性能的晶格结构组成。该数据集使GAN模型能够创建具有高强度重量比的晶格结构。这些晶格设计是使用商用材料喷射增材制造(MJ-AM)机器制造的,允许生产复杂的结构。通过有限元分析(FEA)、压缩和冲击测试来评估3d打印单元胞样品的力学性能。结果表明,使用GAN模型生成的晶格结构显示出更高的机械强度(即强度和延伸性能分别提高108%和150%)。这项研究表明,人工智能有潜力扩大晶格结构的设计空间,并创造出具有改进机械性能的定制零件。该研究还为未来探索深度学习技术在革命性地设计和制造具有定制机械性能的零件方面铺平了道路。
Stochastic analysis of thin beams and plates incorporating von-Kàrmàn nonlinear strains using meshless method
Aswathy M., Arun C.O.
doi:10.1016/j.tws.2023.111478
含von-Kàrmàn非线性应变的薄梁和薄板随机分析
The current paper proposes methods for stochastic meshless analysis of thin beams and plates, by considering von-Kàrmàn nonlinear strains. Here, Young’s modulus is assumed to have a spatial variation over the domain and is modelled as a homogeneous random field. Further, it is discretized utilizing moving least square based shape functions. Gaussian and lognormal properties are presumed to model the randomness related to Young’s modulus. Element-free Galerkin method is used as the meshless tool. The present study suggests employing perturbation method if the quantities of interest are limited to the first or second probabilistic moments of the responses. The nonlinear equations in the proposed perturbation formulations are solved using Newton–Raphson iterative scheme for finding out the deterministic part of the response, as well as its initial two derivatives with respect to random variables. Additionally, if there is an interest in higher probabilistic moments, probability density functions, cumulative distribution functions, and so forth, the study proposes a method using high-dimensional model representation (HDMR) in the meshless framework. In HDMR, the nonlinear connection between input variables and the output response is represented in terms of hierarchical correlated function expansions. These hierarchical functions are approximated over the response values evaluated using deterministic analysis on selected number and combinations of control points of random variables involved. Lagrange interpolation method is employed for the construction of response surfaces. Unlike Monte Carlo simulation (MCS) which needs numerous deterministic nonlinear analyses to be performed during each simulation, HDMR requires only a few deterministic nonlinear analyses to construct the component functions. Hence, simulations performed on the reduced order models of high-dimensional response can be used to estimate the probabilistic moments as well as density functions and cumulative distributions of response, with high computational efficiency compared to MCS. The probabilistic moments and distributions produced for a range of coefficient of variation of input random field up to 30%, for both the normal and lognormal input random fields are compared with those provided by crude MCS on the system of equations and are observed to be matching well.
本文提出了考虑von-Kàrmàn非线性应变的薄梁和薄板随机无网格分析方法。在这里,假定杨氏模量在整个域上具有空间变化,并将其建模为均匀随机场。进一步,利用基于移动最小二乘的形状函数对其进行离散化。假定高斯和对数正态性质来模拟与杨氏模量相关的随机性。采用无单元伽辽金法作为无网格工具。如果感兴趣的量仅限于响应的第一或第二概率矩,则本研究建议采用摄动法。采用Newton-Raphson迭代格式求解了所提出的扰动公式中的非线性方程,以找出响应的确定性部分及其对随机变量的初始导数。此外,如果对高概率矩、概率密度函数、累积分布函数等感兴趣,本研究提出了一种在无网格框架中使用高维模型表示(HDMR)的方法。在HDMR中,输入变量和输出响应之间的非线性联系是用层次相关函数展开来表示的。通过对所涉及的随机变量控制点的选定数量和组合的确定性分析,对这些层次函数的响应值进行近似。采用拉格朗日插值法构造响应面。与蒙特卡罗模拟(MCS)不同,每次模拟都需要进行大量的确定性非线性分析,HDMR只需要少量的确定性非线性分析来构建组件函数。因此,对高维响应的降阶模型进行模拟可以用来估计响应的概率矩、密度函数和累积分布,与MCS相比具有较高的计算效率。将正态输入随机场和对数正态输入随机场在变异系数30%范围内产生的概率矩和概率分布与方程系统上的粗MCS提供的概率矩和概率分布进行了比较,观察到两者匹配良好。