【新文速递】2024年2月13日固体力学SCI期刊最新文章
今日更新：Journal of the Mechanics and Physics of Solids 1 篇，Mechanics of Materials 1 篇，Thin-Walled Structures 2 篇
Journal of the Mechanics and Physics of Solids
Physics-infused deep neural network for solution of non-associative Drucker–Prager elastoplastic constitutive model
Arunabha M. Roy, Suman Guha, Veera Sundararaghavan, Raymundo Arróyave
doi:10.1016/j.jmps.2024.105570
用于求解非关联德鲁克-普拉格弹塑性结构模型的物理注入型深度神经网络
In the present work, a physics-informed deep learning-based constitutive modeling approach has been introduced, for the first time, to solve non-associative Drucker–Prager elastoplastic solid governed by a linear isotropic hardening rule. A purely data-driven surrogate modeling approach for representing complex and highly non-linear elastoplastic constitutive response prevents accurate predictions due to the absence of prior physical information. To mitigate this, we design an efficient physics-constrained training approach leveraging prior physics-driven optimization procedures. It has been achieved by formulating a highly physics-augmented multi-objective loss function that includes elastoplastic constitutive relations, Drucker–Prager yield criterion, non-associative flow rule, Kuhn–Tucker consistency conditions, and various boundary conditions. Utilizing multiple densely connected independent feed-forward deep neural networks fed with high-fidelity numerical solutions in a data-driven loss function, the model obtains the accurate elastoplastic solution by minimizing the proposed loss function. The strength and robustness of the approach have been demonstrated by accurately solving the benchmark problem where a plastically deformed isotropic shallow stratum has been subjected to compressive pressure under plane strain Drucker–Prager yield condition. To optimize the performance and trainability of the model, extensive experiments on network architecture and various degrees of data-driven estimate shed light on significant improvement in terms of the accuracy of the elastoplastic solution, particularly, that exhibits sharp, or very localized features. Moreover, we propose a transfer learning-based PINNs modeling approach that elucidates the possibility of predicting solutions for different sets of applied stress and material parameters. Requiring significantly less training data, the framework can simultaneously enhance the accuracy of the solution and adaptability of training by demonstrating rapid convergence in critical loss components. The current study highlights a systematic development of a novel physics-informed deep learning approach which is quite generic in nature, yet robust and highly physics-augmented for transferability of known knowledge for vastly accelerated convergence with improved accuracy of predicting an accurate description of non-associative elastoplastic solution in the regime of continuum mechanics.
在本研究中，首次引入了一种基于物理信息的深度学习构成建模方法，用于求解受线性各向同性硬化规则支配的非关联德鲁克-普拉格弹塑性固体。由于缺乏先验物理信息，采用纯数据驱动的代用建模方法来表示复杂且高度非线性的弹塑性构效响应无法获得准确预测。为了缓解这一问题，我们设计了一种高效的物理约束训练方法，利用先前的物理驱动优化程序。它是通过制定一个高度物理增强的多目标损失函数来实现的，该函数包括弹塑性构成关系、德鲁克-普拉格屈服准则、非关联流动规则、库恩-塔克一致性条件和各种边界条件。该模型利用多个密集连接的独立前馈深度神经网络，在数据驱动的损失函数中输入高保真数值解，通过最小化建议的损失函数获得精确的弹塑性解。在平面应变德鲁克-普拉格屈服条件下，塑性变形的各向同性浅地层受到压缩压力，通过精确求解这一基准问题，证明了该方法的强度和鲁棒性。为了优化模型的性能和可训练性，我们对网络架构和不同程度的数据驱动估算进行了广泛的实验，结果表明，弹塑性解决方案的 准确性有了显著提高，尤其是在呈现尖锐或局部特征的情况下。此外，我们还提出了一种基于迁移学习的 PINNs 建模方法，阐明了预测不同应用应力和材料参数集的解决方案的可能性。该框架对训练数据的要求大大降低，通过在关键损耗元件上的快速收敛，可同时提高解决方案的准 确性和训练的适应性。当前的研究突出强调了一种新颖的物理信息深度学习方法的系统开发，这种方法在本质上是通用的，但却非常稳健，并具有高度的物理增强功能，可实现已知知识的可转移性，从而大大加快收敛速度，提高预测连续介质力学体系中非关联弹塑性解决方案精确描述的准确性。
Mechanics of Materials
The coupled effect of aspect ratio and strut micro-deformation mode on the mechanical properties of lattice structures
Stylianos Kechagias, Kabelan J. Karunaseelan, Reece N. Oosterbeek, Jonathan R.T. Jeffers
doi:10.1016/j.mechmat.2024.104944
高宽比和支柱微变形模式对晶格结构力学特性的耦合效应
Lattice structures have been integrated into various industrial applications owing to their unique compressive properties. Mechanical characterisation is usually done by testing a small specimen which is assumed representative of the utilised lattice. A specimen's aspect ratio (height to diameter/width ratio) is known to affect compressive properties in various engineering materials, yet its influence in lattices has not been investigated thoroughly. In this study, titanium lattice specimens designed with different aspect ratios (ranging from 0.5 to 3.0) and four different topologies (displaying bend and stretch-dominated micro-deformation modes) were fabricated using powder bed fusion and tested in quasi-static compression. Their compressive properties and failure modes were evaluated using acquired stress-strain curves and digital image correlation (DIC) analysis. Reducing the aspect ratio in the bend-dominated lattices increased the measured stiffness of the specimens by up to 40%. Conversely, increasing the aspect ratio of the stretch dominated lattices increased the measured stiffness of the specimens by up to 30%. For both topology types, decreasing the aspect ratio increased the measured strength of the specimens, but the effect was less than that observed for stiffness. Different responses were attributed to gradient strain accumulation and different failure patterns (densification versus shear banding) that were observed depending on the combination of aspect ratio and topology. These findings are particularly important for better predicting the mechanical behaviour of lattice-based components that have aspect ratios outside the range of conventional test specimens.
晶格结构因其独特的抗压性能而被广泛应用于各种工业领域。机械特性分析通常是通过测试一个小试样来完成的，假定该试样能代表所使用的晶格。众所周知，试样的长宽比（高度与直径/宽度之比）会影响各种工程材料的抗压性能，但其对晶格的影响尚未得到深入研究。本研究利用粉末床熔融技术制作了不同长宽比（从 0.5 到 3.0）和四种不同拓扑结构（显示弯曲和拉伸为主的微变形模式）的钛晶格试样，并进行了准静态压缩测试。利用获取的应力-应变曲线和数字图像相关（DIC）分析评估了它们的压缩性能和失效模式。降低以弯曲为主的晶格的纵横比，可将试样的测量刚度提高 40%。相反，增大以拉伸为主的晶格的长宽比，可使试样的测量刚度增加多达 30%。对于这两种拓扑类型，减小纵横比会增加试样的测量强度，但其影响小于刚度的影响。不同的反应可归因于梯度应变累积以及不同的破坏模式（致密化与剪切带），这取决于纵横比和拓扑结构的组合。这些发现对于更好地预测长宽比超出常规试样范围的基于晶格的部件的机械性能尤为重要。
Thin-Walled Structures
Simulating the Hysteretic Behaviour of Thin-Walled H-Section Steel Members Using the Geometrically Exact Beam Theory
Jian Fan, Junjie Yan, Zhifeng Wu
doi:10.1016/j.tws.2024.111688
利用几何精确梁理论模拟薄壁 H 型钢构件的滞回行为
Based on Gonçalves's geometrically exact beam theory considering cross-section deformation, the warping and distortional deformation of thin-walled members are described in this paper by means of a combination of section deformation modes. By integrating with the J2 elastic‒plasticity theory for the steel, a numerical model is established for the hysteretic behaviour of thin-walled steel members. Four classes of H-section members are selected on account of the design codes, and the influence of section warping and distortion deformation modes on the calculation of the hysteretic behaviour of components with different section types is analysed. The crucial aspect lies in the bending hysteresis behaviour around the strong and weak axes of the cross-section for H-beam steel members subjected to typical thin-walled members with relatively large widths and thicknesses. Test results on the hysteretic behaviour of H-section steel members are compared with those calculated from the proposed model in terms of the hysteric behaviour of components, strength degradation, energy-dissipating capacity, etc. These findings validate the correctness and feasibility of the established non-linear analysis model for thin-walled members.
本文以 Gonçalves 考虑截面变形的几何精确梁理论为基础，通过截面变形模式的组合来描述薄壁构件的翘曲和扭曲变形。通过与钢的 J2 弹塑性理论相结合，建立了薄壁钢构件滞后行为的数值模型。根据设计规范选择了四类 H 型截面构件，分析了截面翘曲和扭曲变形模式对不同截面类型构件滞回行为计算的影响。关键在于 H 型钢构件在横截面强轴和弱轴周围的弯曲滞后行为，这些构件都是典型的薄壁构件，宽度和厚度相对较大。从构件的滞后行为、强度退化、能量耗散能力等方面，将 H 型钢构件的滞后行为测试结果与拟议模型的计算结果进行了比较。这些结果验证了所建立的薄壁构件非线性分析模型的正确性和可行性。
Quasi-static indentation responses of FMLs/2.5D woven carbon-fiber honeycomb sandwich structures under different structural parameters
Ya-nan Zhang, Yu Tian, Xin-yang Liu, Hao Zhou, Jin Zhou, Yu-bing Hu
doi:10.1016/j.tws.2024.111702
不同结构参数下 FMLs/2.5D 编织碳纤维蜂窝夹层结构的准静态压痕响应
Here, a new type of sandwich structure made of fiber metal laminates (FMLs) as facesheet and 2.5D woven carbon-fiber (CFRP) honeycomb as core was developed. The FMLs/CFRP honeycomb sandwich structures were subjected to quasi-static indentation tests to investigate the mechanical response with the variation of panel structure, honeycomb cell element size, honeycomb core wall thickness, and honeycomb core height. In addition to experiments, a finite element method (FEM) model was also created in ABAQUS/Explicit to analyze the damage mechanisms and failure modes of honeycomb sandwich structures under quasi-static indentation. Both the experimental and FEM simulation results revealed that the honeycomb sandwich structure underwent two different failure modes under quasi-static loading: localized damage dominated by indentation and global damage due to overall bending deformation. The combination of panel strength and honeycomb core strength determined the failure mode of the sandwich structure. Damage analysis of the honeycomb core showed that it mainly underwent fiber compression damage and matrix compression damage under quasi-static indentation. This study provides guidance to optimize the design of honeycomb sandwich structures.
本文开发了一种新型夹层结构，该结构由金属纤维层压板（FMLs）作为面板，2.5D 碳纤维（CFRP）编织蜂窝作为芯材。对 FMLs/CFRP 蜂窝夹层结构进行了准静态压痕试验，以研究其机械响应随面板结构、蜂窝单元尺寸、蜂窝芯壁厚度和蜂窝芯高度的变化而变化。除实验外，还在 ABAQUS/Explicit 中创建了有限元法（FEM）模型，以分析蜂窝夹层结构在准静态压痕作用下的破坏机制和失效模式。实验和有限元模拟结果表明，蜂窝夹层结构在准静态加载下经历了两种不同的破坏模式：以压痕为主的局部破坏和整体弯曲变形导致的整体破坏。面板强度和蜂窝芯强度的组合决定了夹层结构的破坏模式。对蜂窝芯的破坏分析表明，在准静态压痕作用下，蜂窝芯主要发生纤维压缩破坏和基体压缩破坏。这项研究为优化蜂窝夹层结构设计提供了指导。
