【新文速递】2024年7月12日固体力学SCI期刊最新文章_ACT_Mechanical_Fluent_System_Deform_振动_复合材料_非线性_焊接_理论_材料_试验

【新文速递】2024年7月14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 1 篇Journal of the Mechanics and Physics of SolidsInteractions among phase transition, heat transfer and austenite plasticity in cyclic compression of NiTi shape memory alloys: Effect of loading frequencyKuo Zhang, Mingpeng Li, Qingping Sun, Lingyun Zhang, Guoan Zhoudoi:10.1016/j.jmps.2024.105782加载频率对NiTi形状记忆合金循环压缩相变、传热和奥氏体塑性的影响Displacement-controlled cyclic compressive responses of polycrystalline superelastic NiTi shape memory alloys (SMAs) are investigated at a maximum strain εmax of 4.2% and over frequencies ranging from 0.0007 Hz to 50 Hz in stagnant air. Our focus was on understanding the interactions among phase transition (PT), heat transfer and plastic flow of austenite phase during cyclic operation. We monitored temperature oscillations along with stress-strain relations and observed a critical frequency fcriAY, below which the responses were primarily influenced by the frequency-dependent coupling between PT and heat transfer, and above which macroscopic plastic deformation of the austenite phase played an important role in the cycling process, interacting with PT and heat transfer. The interactions at high frequencies (f>fcriAY) led to reductions in temperature magnitude, transition strain, latent heat, and hysteresis heat in subsequent cycles, eventually stabilizing the responses without plastic deformation. Theoretical analysis considering the interactions among PT, heat transfer, and plastic deformation was conducted to interpret and quantify the experimental findings. We find that the initiation and saturation of macroscopic plastic deformation of SMAs due to heat accumulation acted as a negative feedback mechanism in the cyclic responses, preventing the materials from overheating and potential damage in applications.研究了多晶超弹性镍钛形状记忆合金（SMA）在最大应变εmax 为 4.2%、频率范围为 0.0007 Hz 至 50 Hz 的停滞空气中的位移控制循环压缩响应。我们的重点是了解循环运行期间奥氏体相变 (PT)、传热和塑性流动之间的相互作用。我们监测了温度振荡和应力应变关系，并观察到一个临界频率 fcriAY，低于该频率时，响应主要受 PT 和热传递之间随频率变化的耦合影响，高于该频率时，奥氏体相的宏观塑性变形在循环过程中发挥重要作用，并与 PT 和热传递相互作用。高频率（f>fcriAY）下的相互作用导致后续循环中温度幅度、过渡应变、潜热和滞后热的降低，最终稳定了无塑性变形的响应。为了解释和量化实验结果，我们进行了理论分析，考虑了 PT、传热和塑性变形之间的相互作用。我们发现，热量积累导致的 SMA 宏观塑性变形的启动和饱和在循环响应中起到了负反馈机制的作用，防止了材料在应用中过热和潜在的损坏。Mechanics of MaterialsPrediction of viscoelastic effective creep compliances in cracked cross-ply composite laminatesKeiji Ogi, Shuji Yoshikawa, Tomonaga Okabe, Sota Onoderadoi:10.1016/j.mechmat.2024.105085 裂纹交叉铺层复合材料层合板的粘弹性有效蠕变柔度预测Viscoelastic effective creep compliances were predicted for symmetric composite laminates with constant matrix crack density. Following previous analytical models, the governing equations were solved in the Laplace domain, and the resulting displacement field was inverse Laplace transformed to obtain the displacement field, damage variables, and strain response to a given stress in the time domain. For the displacement field, both a series solution and a non-series solution were obtained. The non-series solution was then used to calculate the strain response of a cross-ply laminate subjected to constant stress under constant matrix crack density via the numerical inverse Laplace transform. To validate the model, the creep strains predicted using the model were compared with a finite element analysis solution, experimental results, and a synergistic damage mechanics solution.对具有恒定基体裂纹密度的对称复合材料层合板的粘弹性有效蠕变柔度进行了预测。在之前的分析模型基础上，对控制方程进行拉普拉斯域求解，对得到的位移场进行拉普拉斯逆变换，得到给定应力下的位移场、损伤变量和应变响应。对于位移场，得到了级数解和非级数解。然后利用非级数解，通过数值拉普拉斯逆变换计算了恒应力作用下恒矩阵裂纹密度下交叉铺层板的应变响应。为了验证模型的有效性，将模型预测的蠕变应变与有限元分析解、实验结果和协同损伤力学解进行了比较。Thin-Walled StructuresDigital twin-based non-destructive testing method for ultimate load-carrying capacity predictionLei Huang, Ziyu Xu, Tianhe Gao, Xiongwei Liu, Qingjie Bi, Bo Wang, Kuo Tiandoi:10.1016/j.tws.2024.112223基于数字孪生的极限承载能力预测无损检测方法Load-carrying experiments are essential for validating the mechanical performance of structural designs. Unpredictable early structure collapses during experiments would lead to severe safety accidents, and thus the real-time prediction of the ultimate load-carrying capacity (ULC) becomes highly necessary. However, due to multi-source experiment deviations, ULC prediction methods based on finite element (FE) analysis struggle to meet the high-accuracy and real-time requirements. To address this issue, this paper proposes a digital twin-based non-destructive testing (DT-NDT) method for ULC prediction. The method employs a deep neural network (DNN) to construct an experiment digital twin (DT) model in the offline phase. In the online phase, the experiment DT model utilizes the current measured strains to predict the ULC at future time steps in real-time, thus achieving non-destructive testing (NDT). First, a series of virtual experiments considering multi-source experiment deviations are carried out by FE analysis to obtain the virtual experiment dataset. The dataset includes strains and collapse loads corresponding to different experiment deviations and is augmented using the data augmentation approach based on structural symmetry. Subsequently, DNN is trained by the virtual experiment dataset to establish the mapping of strain values to collapse load. Finally, the strains are measured in real-time using strain gauges and the strain values are inputted into the DNN, i.e., the experiment DT model, to predict the collapse load. In this paper, the principle and effectiveness of the proposed method are illustrated through an analytical example and two experiment examples. In the experiment examples, the average relative errors of the DT-NDT method are 0.9% for an open-hole plate and -1.45% for a grid-stiffened cylindrical shell. The results illustrate the high prediction accuracy and effectiveness of the DT-NDT method, demonstrating its immense potential in NDT.承载试验是验证结构设计力学性能的必要条件。在试验过程中，不可预测的早期结构倒塌将导致严重的安全事故，因此对极限承载能力(ULC)的实时预测变得非常必要。然而，由于多源实验偏差，基于有限元(FE)分析的ULC预测方法难以满足高精度和实时性的要求。为了解决这一问题，本文提出了一种基于数字孪生的ULC无损检测(DT-NDT)方法。该方法采用深度神经网络(DNN)在离线阶段构建实验数字孪生(DT)模型。在在线阶段，实验DT模型利用当前测量的应变实时预测未来时间步长的ULC，从而实现无损检测(NDT)。首先，通过有限元分析，考虑多源实验偏差，进行一系列虚拟实验，得到虚拟实验数据集;该数据集包含不同实验偏差对应的应变和倒塌荷载，并采用基于结构对称性的数据增强方法进行扩充。随后，利用虚拟实验数据集对DNN进行训练，建立应变值与崩溃载荷的映射关系。最后，利用应变片实时测量应变，并将应变值输入DNN即实验DT模型，预测倒塌荷载。本文通过一个分析算例和两个实验算例说明了该方法的原理和有效性。在实验算例中，DT-NDT方法对开孔板的平均相对误差为0.9%，对网格加筋圆柱壳的平均相对误差为-1.45%。结果表明，DT-NDT方法具有较高的预测精度和有效性，显示了其在无损检测领域的巨大潜力。来源：复合材料力学仿真Composites FEM

【新文速递】2024年7月12日固体力学SCI期刊最新文章

International Journal of Solids and Structures

Journal of the Mechanics and Physics of Solids

Mechanics of Materials

Thin-Walled Structures

【新文速递】2024年7月14日固体力学SCI期刊最新文章