【新文速递】2024年11月1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 1 篇，Mechanics of Materials 1 篇，Thin-Walled Structures 3 篇International Journal of Solids and StructuresFramework for electrochemical-mechanical behavior of all-solid-state batteries: From the reconstruction method to multi-physics and multi-scale modelingPingyuan Huang, Zhan-Sheng Guodoi:10.1016/j.ijsolstr.2024.113078全固态电池电化学力学行为框架:从重构方法到多物理场和多尺度建模All-solid-state batteries (ASSBs) are high-energy, high-power batteries. To enhance the understanding of the electrochemical-mechanical behavior in ASSBs across different scales, we developed a multi-physics and multi-scale modeling framework. This framework incorporates elastoplastic finite deformation and electrode microstructures of ASSBs, and the role of gradient plasticity in the governing equation for multiple physical fields was discussed. Utilizing X-ray computed tomography, we reconstructed the microstructure through a machine learning (ML)-informed image segmentation process. Our study clarifies the impact of electrode microstructures on concentration, stress, voltage, delamination and buckling from AM to electrode scale. Comparative analysis of the Feret diameter distribution of active materials (AMs) shows that ML-informed image segmentation outperforms two traditional segmentation methods. We observed that the asynchronous diffusion saturation of AMs, varying in shape and size, significantly influences the electrochemical-mechanical behavior of ASSBs, resulting in complicated debonding indices and J-integral distribution at the interface. The proposed upscaling homogenization procedure is demonstrated to be efficient for buckling analysis, with the shape mode closely matching existing experimental observations. These results shed light on the critical multi-physics and multi-scale coupling mechanisms in ASSBs.全固态电池(assb)是一种高能量、大功率的电池。为了更好地理解assb在不同尺度下的电化学力学行为，我们开发了一个多物理场和多尺度的建模框架。该框架考虑了assb的弹塑性有限变形和电极微观结构，并讨论了梯度塑性在多物理场控制方程中的作用。利用x射线计算机断层扫描，我们通过机器学习(ML)知情的图像分割过程重建了微观结构。我们的研究阐明了电极微观结构对从AM到电极尺度的浓度、应力、电压、分层和屈曲的影响。对活性物质(AMs)孔径分布的对比分析表明，基于ml的图像分割优于两种传统的分割方法。我们观察到，不同形状和尺寸的AMs的异步扩散饱和度显著影响assb的电化学-力学行为，导致界面上复杂的脱粘指数和j积分分布。所提出的上尺度均匀化方法对屈曲分析是有效的，其形状模态与已有的实验结果非常吻合。这些结果揭示了assb中关键的多物理场和多尺度耦合机制。Journal of the Mechanics and Physics of SolidsA bistable chain on elastic foundationYuval Roller, Yamit Geron, Sefi Givlidoi:10.1016/j.jmps.2024.105873弹性基础上的双稳链Arrays of bistable elements have been studied extensively in the last two decades due to their relevance to a wide range of physical phenomena and engineering applications, from rate-independent hysteresis to multi-stable metamaterials and soft robotics. Here, we study, theoretically and experimentally, an important extension of the bistable-chain model that has been largely overlooked, namely a discrete chain of bistable elements that is supported by a linear-elastic foundation. Focus is put on equilibrium configurations and their stability, from which the sequence of phase-transition events and the overall force-displacement relation are obtained. In addition, we study the influence of each of the bistable parameters and the stiffness of the elastic foundation on the overall behavior. Closed-form analytical expressions are derived by approximating the bistable behavior with a trilinear force-displacement relation. These are later validated numerically and experimentally. Our analysis shows that the sequence of phase transition may involve two fundamentally different scenarios, depending on the system parameters. The first scenario is characterized by the propagation of a single phase boundary associated with an ordered sequence of phase transitions, while the second involves the formation of multiple phase boundaries and a disordered sequence of transition events. Also, by identifying that the displacements of the chain are related through a linear recursive sequence, we show that, in some particular cases, the relevant expressions can be conveniently reduced to formulas associated with the celebrated Lucas or Fibonacci sequences, and the physical interpretation of these solutions is discussed.双稳态元件阵列在过去的二十年中得到了广泛的研究，因为它们与广泛的物理现象和工程应用相关，从速率无关的滞后到多稳态超材料和软机器人。在这里，我们从理论上和实验上研究了双稳链模型的一个重要扩展，这在很大程度上被忽视了，即由线弹性基础支撑的双稳单元的离散链。重点研究了平衡构型及其稳定性，由此得到了相变事件序列和整体力-位移关系。此外，我们还研究了每个双稳参数和弹性基础的刚度对整体性能的影响。用三线性力-位移关系逼近双稳特性，推导出闭型解析表达式。这些稍后被数值和实验验证。我们的分析表明，根据系统参数的不同，相变的顺序可能涉及两种根本不同的情况。第一种情况的特点是与有序相变序列相关的单相边界的传播，而第二种情况涉及多个相边界的形成和无序的相变事件序列。此外，通过识别链的位移通过线性递归序列相关，我们表明，在某些特定情况下，相关表达式可以方便地简化为与著名的卢卡斯或斐波那契序列相关的公式，并讨论了这些解的物理解释。Mechanics of MaterialsAccelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled Super learner machine-learning algorithmsAtwakyire Moses, Ying Gui, Buzhuo Chen, Marembo Micheal, Ding Chendoi:10.1016/j.mechmat.2024.105168基于规模化超级学习机器学习算法的镁合金力学性能加速智能预测与分析The use of machine learning algorithms in magnesium (Mg) alloys has evolved a scientific innovation for lightweight. The dataset was compiled by collecting data from the experiment and utilizing machine learning (ML) models to predict the mechanical properties of 348 Mg alloys. The proportion between the predicted and experimental results produced by different ML models demands more advanced regression methods to obtain better results. Utilizing Mg alloy descriptors as input variables and mechanical properties as output variables, four different ML models were employed namely (i.e.) Random Forest (RF), Extra Tree (ET), Gradient Boost (GB), and Extreme Gradient Boost (XGBoost) to resolve this difficult problem. Each single algorithm aimed to predict the mechanical properties of Mg alloy i.e. Ultimate Tensile Strength (UTS), Yield Strength (YS), and Elongation (EL). Subsequently, the data-driven intelligent prediction modeling technique called scaled Super Learner (SL) was employed to integrate the single models into the stacked model approach to enhance prediction accuracy. The results obtained using scaled Super Learner demonstrated enhanced prediction accuracy for UTS, YS, and EL. The findings further demonstrate enhanced prediction ability by outperforming other approaches as demonstrated by lower Root Mean Squared Error (RMSE) and higher R-Squared (R2) compared to previous studies. The reason for choosing Scaled Super Learner is because of its robustness and resistance to overfitting. Scaled Super Learner is also widely known for its better scalability, simplicity, and ability to handle noisy. The scaled Super Learner is an optimal approach for predicting the properties of Mg alloys. The proposed scaled Super learner serves as a tool for predicting Mg alloy properties.在镁合金中使用机器学习算法已经发展成为轻量化的科学创新。通过收集实验数据并利用机器学习(ML)模型预测348 Mg合金的力学性能，编制了数据集。不同ML模型产生的预测结果与实验结果之间的比例要求采用更先进的回归方法来获得更好的结果。利用镁合金描述符作为输入变量，机械性能作为输出变量，采用随机森林(Random Forest, RF)、额外树(Extra Tree, ET)、梯度增强(Gradient Boost, GB)和极限梯度增强(Extreme Gradient Boost, XGBoost)四种不同的ML模型来解决这个难题。每个算法旨在预测镁合金的力学性能，即极限抗拉强度(UTS)，屈服强度(YS)和伸长率(EL)。随后，采用数据驱动的智能预测建模技术——尺度超级学习者(scale Super Learner, SL)，将单一模型整合到堆叠模型方法中，提高预测精度。使用缩放的超级学习器获得的结果表明，UTS、YS和EL的预测精度有所提高。与之前的研究相比，研究结果进一步证明了通过更低的均方根误差(RMSE)和更高的R-Squared (R2)来优于其他方法，从而增强了预测能力。选择规模超级学习器的原因是它的鲁棒性和抗过拟合性。规模化超级学习者也因其更好的可扩展性、简单性和处理噪声的能力而广为人知。尺度超级学习器是预测镁合金性能的最佳方法。所提出的规模化超级学习器可作为预测镁合金性能的工具。Thin-Walled StructuresExperimental and numerical investigations on cold-formed titanium-clad bimetallic steel angle and channel section stub columnsYu Shi, Jie Wang, Xuhong Zhou, Xuanyi Xuedoi:10.1016/j.tws.2024.112477冷弯包钛双金属钢角钢和槽钢短柱的试验与数值研究Titanium-clad bimetallic steel (TCBS) exhibits outstanding corrosion resistance, and can be advantageously applied in corrosive environments. This study investigated an innovative application of TCBS in cold-formed stub columns. During the cold-forming process, TCBS demonstrated no discernible separation at the bonding interface of the Q235 substrate layer and TA1 cladding layer, indicating the maintenance of synergistic deformation between the cladding and substrate layers. The compressive behavior and ultimate resistance of cold-formed TCBS angle and channel section stub columns were investigated using both experimental and numerical methods. These tests included material tensile flat and corner coupon tests, initial local geometric imperfection measurements, and stub column tests. The stub column test comprised four cold-formed TCBS angle section stub columns and three cold-formed TCBS channel section stub columns covering the non-slender and slender cross-sections. The experimental results were utilized to validate the finite-element (FE) model, which was then used in a parametric study to obtain further numerical data at different cross-sections. A comparative analysis of the ultimate resistance from the tests and numerical analyses with the predicted results from design approaches in EN 1993-1-1, AISI S100, and the direct strength method (DSM) was revealed. The evaluation findings generally showed that the EN 1993-1-1 design method forecasts for cold-formed TCBS channel section stub columns were dangerous. The non-slender cross-section classification coefficient, which was inapplicable to cold-formed TCBS channel steel stub columns, was the primary cause of the above unsafe prediction. As for the design approaches in AISI S100 and DSM for cold-formed TCBS angle and channel section stub columns, as well as those in EN 1993-1-1 for cold-formed TCBS angle section stub columns, they were judged safe but conservative. The above prediction error was mainly caused by ignoring the enhancement in strength properties of the TCBS after the cold-forming process. The considerations mentioned above led to the proposal and demonstration of modifying recommendations that offered safe, accurate, and consistent design approaches to ultimate resistance for the cold-formed TCBS angle and channel section stub columns.钛包双金属钢(TCBS)具有优异的耐腐蚀性能，在腐蚀环境中具有良好的应用前景。本研究探讨了TCBS在冷弯短柱中的创新应用。在冷成形过程中，TCBS在Q235基材层与TA1熔覆层的结合界面处没有明显的分离，表明熔覆层与基材层之间保持了协同变形。采用实验和数值方法研究了冷弯TCBS角段和槽段短柱的抗压性能和极限抗力。这些测试包括材料拉伸平面和角部测试，初始局部几何缺陷测量和短柱测试。短柱试验包括4根冷弯TCBS角截面短柱和3根冷弯TCBS槽截面短柱，分别覆盖非细长截面和细长截面。实验结果用于验证有限元模型，然后将其用于参数化研究，以获得不同截面的进一步数值数据。对试验结果和数值分析结果与en1993 -1-1、AISI S100和直接强度法的预测结果进行了对比分析。评价结果普遍表明EN 1993-1-1设计方法对冷弯TCBS通道截面短柱的预测是危险的。不适用冷弯TCBS槽钢短柱的非细长截面分类系数是导致上述预测不安全的主要原因。对于AISI S100和DSM中关于冷弯TCBS角段和槽段短柱的设计方法，以及EN 1993-1-1中关于冷弯TCBS角段短柱的设计方法，认为安全但保守。上述预测误差主要是由于忽略了冷成型后TCBS强度性能的增强。基于上述考虑，我们提出并论证了修改建议，为冷成型TCBS角段和通道段短柱提供了安全、准确和一致的设计方法，以达到最终的耐受性。Uncertainty quantification for damage detection in 3D-printed auxetic structures using ultrasonic guided waves and a probabilistic neural networkHouyu Lu, Amin Farrokhabadi, Ali Mardanshahi, Ali Rauf, Reza Talemi, Konstantinos Gryllias, Dimitrios Chronopoulosdoi:10.1016/j.tws.2024.112466基于超声导波和概率神经网络的3d打印结构损伤检测的不确定性量化Auxetic structures hold significant potential for applications due to their outstanding properties. Ultrasonic waves and neural networks are the popular technologies used for structural health monitoring (SHM). To increase the reliability of the neural network output for SHM, comprehensive uncertainty quantification is needed for damage detection in unknown areas of auxetic structures. This paper presents the first comprehensive framework for health diagnosis and uncertainty quantification based on ultrasonic guided waves in two 3D-printed star hourglass honeycomb auxetic structures. The proposed framework integrates in-plane compression with simultaneous ultrasonic testing to receive ultrasonic signals across various deformation states. Additionally, fully elastic and elasto-plastic finite element simulations are conducted to analyze wave energy variations and mechanical responses in auxetic structure. Critical damage deformation is identified based on observed deformation patterns and variations in signal energy. The Hilbert transform is used to extract two damage-sensitive features, namely envelope and phase. These features serve as input data for the Flipout probabilistic convolutional neural network (FPCNN) model, which integrates pseudo-independent weight perturbations and a Gaussian probabilistic layer within the visual geometry group 13 architecture to predict structural deformations and associated uncertainties. The UQ framework effectively separates and quantifies the predictive variance of the FPCNN model into aleatoric and epistemic uncertainty. The framework’s effectiveness is demonstrated through the comprehensive approach, combining compression and ultrasonic tests, finite element simulation, and the FPCNN technique.增塑型结构由于其优异的性能而具有巨大的应用潜力。超声和神经网络是结构健康监测的常用技术。为了提高神经网络输出的可靠性，需要对结构未知区域的损伤检测进行不确定度的综合量化。本文首次提出了基于超声导波的二维3d打印星形沙漏蜂窝结构健康诊断和不确定度量化的综合框架。提出的框架集成了平面内压缩和同时超声检测，以接收不同变形状态的超声信号。此外，还进行了全弹性和弹塑性有限元模拟，分析了消声结构的波能变化和力学响应。根据观测到的变形模式和信号能量的变化来识别临界损伤变形。希尔伯特变换用于提取两个损伤敏感特征，即包络和相位。这些特征作为Flipout概率卷积神经网络(FPCNN)模型的输入数据，该模型集成了伪独立权重扰动和视觉几何组13架构中的高斯概率层，以预测结构变形和相关不确定性。UQ框架有效地将FPCNN模型的预测方差分离并量化为任意不确定性和认知不确定性。通过压缩和超声试验、有限元模拟和FPCNN技术相结合的综合方法，验证了该框架的有效性。Thin-walled FRP-concrete-steel tubular tower with a top mass block subjected to lateral impact loading: Experimental study and FE analysisShuhong LIN, Bing ZHANG, Sumei ZHANG, Xuetao LYU, Xizhe FUdoi:10.1016/j.tws.2024.112475带顶块的薄壁frp -混凝土钢管塔横向冲击荷载试验研究与有限元分析Thin-walled hollow steel tube towers (HSTs), frequently utilized as a wind turbine tower, are facing challenges such as corrosion and local buckling in their service life. To enhance the anti-corrosion capacity and local buckling resistance, an FRP tube and an annular concrete layer can be employed to protect the HST, thus forming a novel member: thin-walled FRP-concrete-steel tubular towers (TW-FCSTs). Using a horizontal vehicle impact system, this study investigated the impact behavior of five large-scale TW-FCSTs, each with a top mass block to mimic the rotor and nacelle of wind turbine. All specimens were designed with a large diameter (300 mm) and a large void ratio (0.73 or 0.82). The experimental results revealed that: (1) Under lateral impact loading, TW-FCSTs displayed a global flexural failure mode, accompanied by localized concavity damage; (2) The inertial effect was enlarged by the top mass block, affecting the dynamic response of TW-FCSTs; (3) the increase of steel thickness led to higher energy dissipation but lower local deformation; (4) the increase of void ratio resulted in larger local deformation but smaller lateral global displacement. Finally, based on LS-DYNA, FE models were utilized to simulate the dynamic responses of TW-FCSTs with a top mass block.薄壁空心钢管塔架作为风力发电机组常用的塔架，在使用寿命中面临着腐蚀和局部屈曲等问题。为了提高HST的抗腐蚀能力和局部抗屈曲能力，可以采用FRP管和环形混凝土层来保护HST，从而形成一种新型构件:薄壁FRP-混凝土-钢管塔(TW-FCSTs)。利用水平车辆碰撞系统，研究了5个大型tw - fcst的碰撞行为，每个tw - fcst都有一个顶部质量块来模拟风力发电机的转子和机舱。所有试件均采用大直径(300 mm)和大孔隙比(0.73或0.82)设计。试验结果表明:(1)在横向冲击荷载作用下，TW-FCSTs呈现整体弯曲破坏模式，并伴有局部凹性损伤;(2)顶部质量块增大了惯性效应，影响了TW-FCSTs的动力响应;(3)钢材厚度的增加导致能量耗散增大，但局部变形减小;(4)孔隙比增大导致局部变形增大，整体侧向位移减小。最后，基于LS-DYNA，利用有限元模型模拟了带顶块的tw - fcst的动力响应。来源：复合材料力学仿真Composites FEM