【新文速递】2024年5月5日复合材料SCI期刊最新文章_ACT_Mechanical_Additive_System_Inspire_疲劳_断裂_复合材料_非线性_化学_燃料电池_电路_航空_航天_汽车_电子_农业_裂纹_BIM

【新文速递】2024年5月7日复合材料SCI期刊最新文章

今日更新：Composite Structures 2 篇，Composites Part A: Applied Science and Manufacturing 1 篇，Composites Part B: Engineering 1 篇，Composites Science and Technology 1 篇Composite StructuresEffects of long-term exposure of NSM CFRP-to-concrete bond to natural and accelerated aging environmentsAloys Dushimimana, José Sena-Cruz, Luís Correia, João Miguel Pereira, Susana Cabral-Fonseca, Ricardo Cruzdoi:10.1016/j.compstruct.2024.118174NSM cfrp -混凝土粘结剂长期暴露于自然和加速老化环境中的影响Carbon fiber reinforced polymer (CFRP) composites can be used to strengthen existing reinforced concrete (RC) structures. The CFRP laminate can be bonded to RC structure using epoxy adhesive via near-surface mounted (NSM) strengthening technique. However, existing literature generally lacks data about durability of NSM CFRP-to-concrete bond. In this study, strengthened concrete elements were exposed to laboratory-controlled environments (at approximately 20 °C/55 % RH, and water immersion at 20 °C) and natural field environments (to promote natural aging induced mainly by carbonation, high temperatures, freeze–thaw attack, and airborne chlorides) for up to four years. Durability tests were conducted yearly for the bond and its constituent materials. The highest bond strength degradations were nearly 12 % and 9 % for the specimens immersed in water and those exposed to freeze–thaw attack, respectively. Besides, environmental conversion factors of 0.88 and 0.93 were derived from a database of existing accelerated, and natural aging data from the present work, respectively.碳纤维增强聚合物(CFRP)复合材料可用于加固现有的钢筋混凝土(RC)结构。采用近表面安装(NSM)加固技术，采用环氧胶粘剂将CFRP复合材料粘接在RC结构上。然而，现有文献普遍缺乏关于NSM cfrp -混凝土粘结耐久性的数据。在这项研究中，加固混凝土元件暴露在实验室控制的环境(大约20 °C/55 % RH, 20 °C的水浸泡)和自然现场环境(促进主要由碳化、高温、冻融侵蚀和空气中的氯化物引起的自然老化)中长达四年。每年对粘结剂及其组成材料进行耐久性试验。浸水和冻融作用下的粘结强度下降幅度最大，分别接近12 %和9 %。环境转换因子分别为0.88和0.93，分别来自现有加速老化和自然老化数据库。High fidelity FEM based on deep learning for arbitrary composite material structureJiaxi Li, Weian Yao, Yu Lu, Jianqiang Chen, Yan Sun, Xiaofei Hudoi:10.1016/j.compstruct.2024.118176基于深度学习的任意复合材料结构高保真有限元分析Due to the outstanding performance, composite materials are widely used and analyzing their properties and designing them based on performance has become a crucial task in the field of many manufacturing industries. Composite materials possess complex multiscale structures, and traditional fine-scale finite element modeling and analysis may lead to severe computational resource challenges. To overcome this difficulty, breakthroughs in key technologies of multiscale accelerated analysis algorithms are required. In this study, an innovative approach based on artificial intelligence and multiscale finite element method is presented. This approach involves partitioning the entire composite material structure into coarse grids that resemble homogenous structures of similar size, providing results consistent to fine-grid finite element analysis. By utilizing CNN for image feature recognition and employing the CGAN adversarial method, coarse-grid equivalent stiffness matrices and multiscale shape functions from completely random microstructures of composite materials can be obtained. Consequently, this enables a rapid response process from microstructure to low-resolution grid to high-resolution physical field, with remarkably accurate physical field results. Moreover, compared to traditional fine-grid finite element methods, this approach significantly reduces memory usage and computation time. This method is applicable to composite materials with varying shaped inclusions, different component properties, and diverse geometric distributions, allowing these materials to perform high-fidelity finite element calculations on coarse grids and predict their mechanical behavior. Furthermore, this breakthrough opens avenues for accelerating the optimization design of composite materials with diverse mechanical functionalities, by employing a bottom-up approach.复合材料由于其优异的性能得到了广泛的应用，分析其性能并根据其性能进行设计已成为许多制造业领域的一项重要任务。复合材料具有复杂的多尺度结构，传统的精细尺度有限元建模和分析对计算资源构成了严峻的挑战。为了克服这一困难，需要突破多尺度加速分析算法的关键技术。本研究提出了一种基于人工智能和多尺度有限元方法的创新方法。这种方法包括将整个复合材料结构划分为粗网格，这些粗网格类似于大小相似的同质结构，从而提供与细网格有限元分析一致的结果。利用CNN进行图像特征识别，采用CGAN对抗方法，可以得到复合材料完全随机微观结构的粗网格等效刚度矩阵和多尺度形状函数。因此，这使得从微观结构到低分辨率网格再到高分辨率物理场的快速响应过程成为可能，并且具有非常精确的物理场结果。此外，与传统的细网格有限元方法相比，该方法显著减少了内存使用和计算时间。该方法适用于具有不同形状夹杂物、不同组分性能和不同几何分布的复合材料，允许这些材料在粗网格上进行高保真的有限元计算，并预测其力学行为。此外，通过采用自下而上的方法，这一突破为加速具有多种机械功能的复合材料的优化设计开辟了道路。Composites Part A: Applied Science and ManufacturingAn efficient finite element mesh generation methodology based on μCT images of multi-layer woven compositesXuanxin Tian, Heng Zhang, Zhaoliang Qu, Shigang Aidoi:10.1016/j.compositesa.2024.108255 基于多层编织复合材料μCT图像的高效有限元网格生成方法High-fidelity models are essential for accurate finite element (FE) simulations of composite material behavior. This paper proposes an efficient meshing methodology based on micro-Computed Tomography (μCT) images. U-Net convolutional neural network was used for image segmentation. Connected yarns were then separated using an improved procedure based on watershed algorithm and geometric transformations. The proposed Constrained Delaunay-Advancing Front Technique (CD-AFT) surface reconstruction algorithm extracts point cloud of yarns from segmented images and outputs high-quality and smooth orientable manifold watertight triangulated surface. Intersecting meshes of yarns are separated through node position detection and Laplacian moving. Experimental results show that proposed methodology is capable of accomplishing mesh generation for different mesh sizes. Compared with commercial software, it has obvious advantages in mesh quality and size control. Since the proposed method operates independently of commercial software and manual operation, it facilitates the automated generation of numerous high-fidelity models from μCT images for FE simulations.高保真模型对于复合材料性能的精确有限元模拟至关重要。提出了一种基于微计算机断层扫描(μCT)图像的高效网格划分方法。采用U-Net卷积神经网络进行图像分割。然后使用基于分水岭算法和几何变换的改进方法分离连接的纱线。提出的约束delaunay推进前沿技术(CD-AFT)曲面重建算法从分割图像中提取纱线点云，输出高质量、光滑的可定向流形水密三角曲面。通过节点位置检测和拉普拉斯移动分离纱线的相交网格。实验结果表明，该方法能够实现不同网格尺寸的网格生成。与商业软件相比，它在网格质量和尺寸控制方面具有明显的优势。由于该方法独立于商业软件和人工操作，它有助于从μCT图像中自动生成大量高保真模型用于FE模拟。Composites Part B: EngineeringIntegrated Accelerated Testing Methodology for CFRP DurabilityYasushi Miyano, Masayuki Nakadadoi:10.1016/j.compositesb.2024.111527CFRP耐久性综合加速试验方法Integrated ATM, an integrated accelerated testing methodology for CFRP durability, is described herein. It is expressed as a single formula including several parameters representing the life of CFRP under an arbitrary environmental temperature and an arbitrary strain ratio R from R =0 to 1. Integrated ATM is based on Christensen's viscoelastic crack kinetics and conventional ATM. First, Integrated ATM is introduced based on the matrix resin viscoelasticity. Second, important parameters which affect CFRP life are found for the longitudinal tensile strength of a unidirectional CFRP as an Integrated ATM application. Finally, the parameter influences on CFRP life are assessed.本文描述了集成ATM，一种CFRP耐久性的集成加速测试方法。在任意环境温度下，在R =0 ~ 1的任意应变比R下，CFRP的寿命可表示为包含多个参数的单一公式。综合自动取款机是基于克里斯坦森粘弹性裂纹动力学和传统自动取款机。首先，介绍了基于基体树脂粘弹性的集成ATM。其次，找到了影响碳纤维增强材料寿命的重要参数，用于单向碳纤维增强材料的纵向拉伸强度作为集成ATM应用。最后，评估了各参数对碳纤维布寿命的影响。Composites Science and TechnologyThe strength prediction model of unidirectional fiber reinforced composites based on the renormalization group methodYixing Qian, Zhinan Li, Xin Zhou, Tong Xia, Yao Zhang, Zhenyu Yang, Dayong Hu, Zixing Ludoi:10.1016/j.compscitech.2024.110639 基于重整化群法的单向纤维增强复合材料强度预测模型When unidirectional fiber reinforced composites are subjected to longitudinal tensile loading and reach a critical failure state, they experience a sudden transition from local damage to catastrophic failure, commonly termed as an avalanche event. This paper integrates the self-organized criticality theory (SOC) concepts into the prediction of longitudinal tensile strength of composites and establishes a strength prediction model of composites based on the renormalization group method (RGM). The predictions of the RGM model are successfully validated against experimental results in the literatures, and it demonstrates relatively acceptable predictive accuracy compared to classical strength criteria. Compared to other state-of-the-art prediction models considering stochastic fiber strength distribution, the RGM model effectively provides strength statistics for fiber bundles of any size and describes the occurrence of composites avalanche failure induced by local stress concentration. This present model can be very conveniently implemented as a User Material Subroutine (UMAT) for finite simulations, facilitating practical prediction of the strength of composite structures.当单向纤维增强复合材料受到纵向拉伸载荷并达到临界破坏状态时，它们会经历从局部损伤到灾难性破坏的突然转变，通常被称为雪崩事件。将自组织临界理论(SOC)概念融入到复合材料纵向拉伸强度预测中，建立了基于重整化群法(RGM)的复合材料强度预测模型。RGM模型的预测结果与文献中的实验结果进行了对比验证，与经典强度准则相比，RGM模型的预测精度相对较好。与考虑随机纤维强度分布的其他最新预测模型相比，RGM模型有效地提供了任何尺寸纤维束的强度统计数据，并描述了局部应力集中引起的复合材料雪崩破坏的发生。该模型可以很方便地作为有限模拟的用户材料子程序(UMAT)实现，便于对复合材料结构的强度进行实际预测。来源：复合材料力学仿真Composites FEM