【新文速递】2024年12月23日固体力学SCI期刊最新文章_ACT_Mechanical_Additive_System_Marc_断裂_复合材料_化学_建筑_焊接_裂纹_理论_化机_材料_分子动力学_创新方法_控制_工厂

【新文速递】2024年12月27日固体力学SCI期刊最新文章

今日更新：International Journal of Solids and Structures 1 篇，Journal of the Mechanics and Physics of Solids 2 篇，International Journal of Plasticity 1 篇，Thin-Walled Structures 2 篇International Journal of Solids and StructuresPhase transition resistance induced by locally resonant metastructuresPeng-Cheng Qi, Yi-Ze Wangdoi:10.1016/j.ijsolstr.2024.113209局部共振元结构引起的相变电阻Based on the piecewise linear relation between the force and elongation of springs, the phase transition and its generating waves in mechanical metastructures are studied. With the Wiener-Hopf method, the governing equation of the transition wave is derived. External force compensations for defect springs are considered to describe the phase transition. Besides the condition that whether the phase transition can be generated, localized phase transition is discussed. Furthermore, finite element simulation and experiment are performed to show the dynamic phase transition. It can be concluded that the locally resonant metastructures can enhance the resistance of phase transition. This research is expected to be helpful to design new kinds of elastic wave metastructures and metamaterials to improve phase transition strength.基于弹簧的力与伸长率的分段线性关系，研究了力学元组织中的相变及其产生波。利用Wiener-Hopf方法，推导了过渡波的控制方程。考虑了缺陷弹簧的外力补偿来描述相变。除了能不能产生相变的条件外，还讨论了局域相变。在此基础上，进行了有限元模拟和实验研究。结果表明，局部共振的元结构可以增强相变的阻力。本研究对设计新型弹性波超结构和提高相变强度的超材料具有一定的指导意义。Journal of the Mechanics and Physics of SolidsMultiscale analysis method for profiled composite structures considering the forming processChen Liu, Jingran Ge, Shuwei Zhao, Qi Zhang, Xiaodong Liu, Jun Liangdoi:10.1016/j.jmps.2024.106014 考虑成形过程的复合材料异型结构多尺度分析方法The forming process often results in a highly heterogeneous mesoscale structure within composite structures, leading to enormous changes in mechanical properties. This complexity poses a significant challenge for accurately evaluating their mechanical behavior. In this paper, a concurrent multiscale analysis method considering the forming process is proposed to accurately analyze the mechanical behavior of profiled composite structures. The change in the internal mesoscale structure of the profiled composite structures is studied by simulating the preforming process of the composite woven fabric. A feature reduction scheme is proposed to reduce the multiscale model of profiled composite structures based on shear angle γ (selected as feature of the U-shape composite structure) and each feature region is coupled with a corresponding mesoscale model. Subsequently, a concurrent multiscale simulation method, based on self-consistent clustering analysis, is developed to model the mechanical behavior of profiled composite structures. The proposed method for simulation of profiled composite structures is validated against experimental data from literature covering various shear deformations. Finally, the progressive failure analysis of the U-shape composite structure (an example) is implemented to reveal its failure mechanism at both macroscale and mesoscale scales. The proposed multiscale analysis method can be applied to the structural design and the optimization of composite forming process.成形过程往往导致复合材料结构内部形成高度不均匀的中尺度结构，从而导致力学性能的巨大变化。这种复杂性对准确评估其机械行为提出了重大挑战。本文提出了一种考虑成形过程的并行多尺度分析方法，以精确分析复合材料异型结构的力学行为。通过模拟复合机织物的预成形过程，研究了异形复合材料结构内部中尺度结构的变化。提出了一种基于剪切角γ（选择u型复合材料结构的特征）的特征约简方案，并将每个特征区域与相应的中尺度模型耦合。在此基础上，提出了一种基于自洽聚类分析的并行多尺度模拟方法来模拟复合材料异型结构的力学行为。本文提出的模拟复合材料异型结构的方法与文献中涵盖各种剪切变形的实验数据进行了验证。最后，以u型复合材料结构为例进行了递进破坏分析，揭示了其宏观和中尺度的破坏机制。所提出的多尺度分析方法可应用于复合材料的结构设计和成形工艺优化。Consistent machine learning for topology optimization with microstructure-dependent neural network material modelsHarikrishnan Vijayakumaran, Jonathan B. Russ, Glaucio H. Paulino, Miguel A. Bessadoi:10.1016/j.jmps.2024.106015基于微结构相关神经网络材料模型的拓扑优化一致机器学习Additive manufacturing methods together with topology optimization have enabled the creation of multiscale structures with controlled spatially-varying material microstructure. However, topology optimization or inverse design of such structures in the presence of nonlinearities remains a challenge due to the expense of computational homogenization methods and the complexity of differentiably parameterizing the microstructural response. A solution to this challenge lies in machine learning techniques that offer efficient, differentiable mappings between the material response and its microstructural descriptors. This work presents a framework for designing multiscale heterogeneous structures with spatially varying microstructures by merging a homogenization-based topology optimization strategy with a consistent machine learning approach grounded in hyperelasticity theory. We leverage neural architectures that adhere to critical physical principles such as polyconvexity, objectivity, material symmetry, and thermodynamic consistency to supply the framework with a reliable constitutive model that is dependent on material microstructural descriptors. Our findings highlight the potential of integrating consistent machine learning models with density-based topology optimization for enhancing design optimization of heterogeneous hyperelastic structures under finite deformations.增材制造方法与拓扑优化相结合，可以创建具有可控空间变化材料微观结构的多尺度结构。然而，由于计算均匀化方法的费用和微结构响应微分参数化的复杂性，在非线性存在的情况下，这种结构的拓扑优化或反设计仍然是一个挑战。这一挑战的解决方案在于机器学习技术，该技术可以在材料响应与其微观结构描述符之间提供有效的、可微分的映射。这项工作提出了一个框架，通过将基于均质化的拓扑优化策略与基于超弹性理论的一致机器学习方法相结合，设计具有空间变化微观结构的多尺度异质结构。我们利用神经架构，坚持关键的物理原理，如多凸性、客观性、材料对称性和热力学一致性，为框架提供依赖于材料微观结构描述符的可靠本构模型。我们的研究结果强调了将一致机器学习模型与基于密度的拓扑优化相结合的潜力，以增强有限变形下非均质超弹性结构的设计优化。International Journal of PlasticityTwinning induced by asymmetric shear responseJie Huang, Mingyu Lei, Guochun Yang, Bin Wendoi:10.1016/j.ijplas.2024.104226不对称剪切反应诱导孪晶Twinning, a plastic deformation mode, is crucial in dictating material plasticity and significantly impacting their mechanical properties. In this work, we propose a new twinning mechanism based on the phenomenon of asymmetric shear response. By integrating transition state theory with this mechanism, we derive the twinning nucleation stress, and reveal the impact of temperature and strain rate on twin nucleation and growth processes. The model's efficacy is validated through a comparison of predicted results for face centered cubic (FCC), body centered cubic (BCC) and hexagonal close packed (HCP) crystals with experimental ones. This work provides a theoretical foundation for predicting the conditions under which twinning occurs, thereby guiding the design and fabrication of materials containing twin structures.孪生是一种塑性变形模式，对材料的塑性和力学性能有重要影响。在这项工作中，我们提出了一种新的基于不对称剪切响应现象的孪生机制。将过渡态理论与此机制相结合，导出了孪晶成核应力，揭示了温度和应变速率对孪晶成核和生长过程的影响。通过面心立方（FCC）、体心立方（BCC）和六方密排（HCP）晶体的预测结果与实验结果的比较，验证了该模型的有效性。这项工作为预测孪晶发生的条件提供了理论基础，从而指导含有孪晶结构的材料的设计和制造。Thin-Walled StructuresImpact resistance and fire resistance of solid waste based interface self-assembled fiber reinforced composite structuresKe Yan, Shaobo Qi, Xingyu Shen, Mengqi Yuan, Hao Wu, Yunxian Yang, Yazhuo Qiandoi:10.1016/j.tws.2024.112870 固体废物基界面自组装纤维增强复合材料结构的抗冲击和防火性能Inspired by the resource utilization of solid waste, industrial sludge has been developed as a raw material NH2-MCM-41@Cu-AF. The preparation method of fiber self-assembly structure design was explored by combining macroscopic grafting behavior with the evolution of microscopic bridging structure. The basic mechanical properties, impact resistance, flame retardant, and thermal insulation properties were analyzed. By exploring and predicting the transition from single fiber composite materials to multi-protection, experimental results have shown that NH2-MCM-41@Cu-AF significantly enhances the protective performance. The composite fabric has increased the pulling force by 3.25 times, and the ballistic limit speed has been increased from 57 m/s to 94 m/s. The strong penetration resistance of composite materials has been demonstrated through multi-layer bulletproof performance. Composite fabrics have stronger flame retardancy and thermal insulation, forming a dense protective layer under high temperatures, ensuring structural integrity to the greatest extent possible. Machine learning prediction methods and threshold analysis processes have been established for the final implementation of NH2-MCM-41@Cu-AF. The development of new protective materials and their application in engineering practice provide theoretical support and an experimental basis.受固体废物资源化利用的启发，工业污泥被开发为原料NH2-MCM-41@Cu-AF。将宏观接枝行为与微观桥接结构演变相结合，探索纤维自组装结构设计的制备方法。分析了材料的基本力学性能、抗冲击性能、阻燃性能和保温性能。通过对单纤维复合材料向多防护过渡的探索和预测，实验结果表明NH2-MCM-41@Cu-AF显著提高了防护性能。复合织物的拉力提高了3.25倍，弹道极限速度从57 m/s提高到94 m/s。复合材料的多层防弹性能证明了复合材料的抗侵彻性能。复合织物具有更强的阻燃性和保温性，在高温下形成致密的保护层，最大限度地保证结构的完整性。机器学习预测方法和阈值分析流程已经建立，最终实现NH2-MCM-41@Cu-AF。新型防护材料的开发及其在工程实践中的应用为工程防护提供了理论支持和实验依据。Multi-objective optimization of composite stiffened panels for mass and buckling load using PNN-NSGA-III algorithm and TOPSIS methodTao Zhang, Zhao Wei, Liping Wang, Zhuo Xue, Suian Wang, Peiyan Wang, Bowen Qi, Zhufeng Yuedoi:10.1016/j.tws.2024.112878基于PNN-NSGA-III算法和TOPSIS方法的复合材料加筋板质量和屈曲载荷多目标优化A novel multi-objective optimization framework for composite stiffened panels is proposed in this study, leveraging a combination of the Parallel Neural Network (PNN), Non-dominated Sorting Genetic Algorithm-III (NSGA-III), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. This framework demonstrates high efficiency and accuracy in obtaining the optimal design for intricate optimization challenges. The PNN in this framework, leveraging data-driven methods, addresses the limitations of Classical Laminate Theory (CLT) in constructing optimization surrogate models, such as challenges in parameter range determination, lack of independence, and the necessity for secondary inverse problem solving. In contrast to NSGA-II, NSGA-III which uses reference points and correlation operators achieves more uniform and rich Pareto fronts under stacking sequence constraints. Additionally, to minimize the required effort and expert knowledge in selecting optimal design parameters, this framework incorporates the Entropy Weight Method (EWM) and TOPSIS method. EWM calculates the entropy of optimization objectives from all alternatives in the Pareto front, assigns weights accordingly, and employs TOPSIS to rank the closeness of each alternative to the ideal solution, thereby identifying the optimal design.基于并行神经网络（PNN）、非支配排序遗传算法- iii （NSGA-III）和TOPSIS方法，提出了一种新的复合材料加筋板多目标优化框架。该框架在复杂的优化问题中具有较高的效率和准确性。该框架中的PNN利用数据驱动方法，解决了经典层压板理论（CLT）在构建优化代理模型方面的局限性，例如参数范围确定方面的挑战、缺乏独立性以及二次逆问题求解的必要性。与NSGA-II相比，使用参考点和相关算子的NSGA-III在叠加序列约束下实现了更均匀、更丰富的Pareto front。此外，为了最大限度地减少选择最优设计参数所需的努力和专家知识，该框架结合了熵权法（EWM）和TOPSIS方法。EWM从Pareto front的所有备选方案中计算优化目标的熵，分配相应的权重，并利用TOPSIS对每个备选方案与理想解的接近程度进行排序，从而确定最优设计。来源：复合材料力学仿真Composites FEM