【新文速递】2026年5月26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 1 篇
International Journal of Solids and Structures
Bulge mechanics of pre-tensioned multilayer 2D structures with interlayer and substrate slip
Xiangtian Shen, Yueguang Wei
doi:10.1016/j.ijsolstr.2026.114102
层间和衬底滑移的预张拉多层二维结构的膨胀力学
The bulge test is a cornerstone technique for characterizing the mechanical properties of two-dimensional (2D) materials, yet classical models fall short in describing multilayer systems by neglecting the coupled effects of interlayer slip, substrate slip, and pre-tension. Here, a comprehensive characterization study is presented, combining a nalytical modeling, molecular dynamics (MD) simulations, and experimental benchmarking. Explicit full-field theoretical solutions are derived for both rectangular strip (one-dimensional) and circular (axisymmetric) bulge configurations, incorporating linear elastic interfacial slips and pre-tension. These solutions explicitly decouple the contributions of membrane stretching, plate bending, and pre-tension, while elucidating the regulating roles of interfacial slips. The accuracy and robustness of the theoretical model are systematically validated against extensive MD simulations and literature experimental data on few-layer graphene. The framework developed herein provides a robust toolkit for the quantitative interpretation of bulge-test responses, enabling the identification of interfacial properties and pre-tension in complex 2D multilayer systems.
膨胀试验是表征二维（2D）材料力学性能的基础技术，但经典模型由于忽略了层间滑移、衬底滑移和预张力的耦合效应，在描述多层系统时存在不足。在这里，一个全面的表征研究提出，结合分析建模，分子动力学（MD）模拟，和实验基准。导出了包含线弹性界面滑移和预张力的矩形条形（一维）和圆形（轴对称）凸起结构的显式全场理论解。这些解决方案明确地解耦了膜拉伸、板弯曲和预张力的贡献，同时阐明了界面滑移的调节作用。理论模型的准确性和鲁棒性通过广泛的MD模拟和文献实验数据在几层石墨烯上进行了系统验证。本文开发的框架为膨胀试验响应的定量解释提供了一个强大的工具包，能够识别复杂的二维多层系统中的界面特性和预张力。
Journal of the Mechanics and Physics of Solids
A Multiscale Physics Guided Deep Learning Framework for Predicting Microscopic Evolution of Crystal Plasticity Considering Finite Deformations
Wei Liu, Huanbo Weng, Yinan Cui, Yinghua Liu
doi:10.1016/j.jmps.2026.106694
考虑有限变形的晶体塑性微观演化预测的多尺度物理引导深度学习框架
Understanding constitutive relationships is essential for the modeling, design, and practical application of advanced metals and alloys under extreme conditions. Currently, most constitutive models are phenomenological. While crystal plasticity finite element (CPFE) modeling can incorporate physical equations at the microscale, its high computational cost makes it difficult to apply in engineering problems. There is an urgent need to develop a multiscale neural network constitutive surrogate model to improve efficiency and address the issue of historical dependence. To address the limitations above, a multiscale autoregressive physics-guided neural network (MRPGNN) model is developed to obtain the evolution of the slip system rotation, resolved shear stress, back stress, isotropic hardening term, and state variables describing dislocation hardening through physics-based propagation via update equations, considering finite deformations. It is developed using initial state and macroscopic loading history as input, evolution of all physical quantities on the slip system with loading history serve as the outputs. The proposed MRPGNN model achieves two orders of magnitude higher efficiency than CPFE modeling while also effectively addressing cumulative errors. A film cooling hole (FCH) structural component is used to validate the robustness of the MRPGNN model and assess its potential for practical applications. Research shows that the MRPGNN model demonstrates high prediction accuracy and robustness. The MRPGNN model has the potential to be integrated with advanced technologies in the future, allowing for a deeper exploration of multiscale constitutive relationships by incorporating more complex deformation mechanisms (such as climb and diffusional creep and so on) through modifications to the update equations in the deep learning framework.
理解本构关系对于在极端条件下的高级金属和合金的建模、设计和实际应用至关重要。目前，大多数本构模型都是现象学的。晶体塑性有限元（CPFE）建模虽然可以在微观尺度上包含物理方程，但其计算成本高，难以应用于工程问题。迫切需要开发一种多尺度神经网络本构代理模型来提高效率并解决历史依赖问题。为了解决上述局限性，开发了一个多尺度自回归物理引导神经网络（MRPGNN）模型，通过更新方程，考虑有限变形，通过基于物理的传播，获得滑移系统旋转、分解剪切应力、背应力、各向同性硬化项和描述位错硬化的状态变量的演变。该模型以初始状态和宏观加载历史为输入，滑移系统上各物理量随加载历史的演化为输出。所提出的MRPGNN模型比CPFE模型的效率提高了两个数量级，同时也有效地解决了累积误差。利用膜冷却孔（FCH）结构部件验证了MRPGNN模型的鲁棒性，并评估了其实际应用潜力。研究表明，MRPGNN模型具有较高的预测精度和鲁棒性。MRPGNN模型在未来有可能与先进技术相结合，通过修改深度学习框架中的更新方程，通过合并更复杂的变形机制（如爬升和扩散蠕变等），允许对多尺度本构关系进行更深入的探索。
ChebyKAN-Enhanced Deep Energy Method for Phase Field Fracture: From Homogeneous to Heterogeneous Materials
Peng Zhang, Keke Tang, Baixiang Xu
doi:10.1016/j.jmps.2026.106693
相场断裂的chebykan增强深能法：从均匀材料到非均匀材料
Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving phase field fracture problems, with the Deep Energy Method (DEM)—an energy-driven branch of PINNs—being particularly suitable due to its alignment with the variational nature of fracture mechanics. DEM typically employs Multi-Layer Perceptrons (MLPs) as function approximators; however, MLPs rely on fixed activation functions, and successful crack path prediction demands meticulous selection tailored to each specific problem. To address the limitation of fixed activation functions in capturing problem-specific nonlinearities, a ChebyKAN-enhanced DEM is developed where learnable activation functions are parameterized by Chebyshev polynomial expansions through Kolmogorov-Arnold Networks (KANs), enabling the network to adaptively discover the optimal activation function form without manually specifying the activation function type. A diffuse interface formulation is introduced to handle heterogeneous materials such as matrix-inclusion composites by smoothly transitioning material properties across boundaries, demonstrating that neural network-based phase field methods can effectively address fracture in multi-phase systems without explicit domain partitioning. Numerical examples involving crack propagation, coalescence, and branching in both homogeneous and heterogeneous materials demonstrate excellent agreement with finite element solutions. To improve computational efficiency, transfer learning strategies are systematically investigated, including step-transfer learning that exploits temporal continuity between load increments and cross-task transfer learning that reuses trained networks across different configurations. Compared with the baseline without transfer learning, these strategies substantially reduce the training epochs required at each load step. Regarding computational efficiency, the total wall-clock time of the proposed framework and FEM falls within the same order of magnitude across all benchmark cases; relative to earlier DEM implementations that are typically about one order of magnitude slower than FEM, this gap has been substantially narrowed.
物理信息神经网络（pinn）已经成为解决相场断裂问题的一种很有前途的方法，其中深能量法（DEM）是pinn的一个能量驱动分支，由于其与断裂力学的变分特性相一致，因此特别适用。DEM通常使用多层感知器（mlp）作为函数逼近器；然而，mlp依赖于固定的激活函数，成功的裂纹路径预测需要针对每个特定问题进行细致的选择。为了解决固定激活函数在捕获特定问题非线性方面的局限性，开发了一个chebykan增强的DEM，其中可学习的激活函数通过Kolmogorov-Arnold网络（KANs）通过Chebyshev多项式展开进行参数化，使网络能够自适应地发现最优激活函数形式，而无需手动指定激活函数类型。引入了一种扩散界面公式来处理非均相材料，如基体-夹杂复合材料，通过平滑地跨边界过渡材料的性能，证明基于神经网络的相场方法可以有效地处理多相系统中的断裂，而无需显式的域划分。在均质和非均质材料中涉及裂纹扩展、合并和分支的数值例子与有限元解非常吻合。为了提高计算效率，系统地研究了迁移学习策略，包括利用负载增量之间的时间连续性的步进迁移学习和跨不同配置重用训练网络的跨任务迁移学习。与没有迁移学习的基线相比，这些策略大大减少了每个负载步骤所需的训练次数。在计算效率方面，所提出的框架和FEM的总挂钟时间在所有基准情况下都处于同一数量级；相对于早期的DEM实现（通常比FEM慢一个数量级），这一差距已经大大缩小。
International Journal of Plasticity
Machine learning guided element substitution strategy for improving strength and heat resistance in cost-effective aluminum alloys
Ziyao Zhao, Kaikai Qiu, Haochen Xu, Lei Jiang, Zhihao Zhang, Jianxin Xie
doi:10.1016/j.ijplas.2026.104732
机器学习指导的元素替代策略，以提高经济高效的铝合金的强度和耐热性
Heat resistance in Al-Cu-Mg alloys is commonly achieved through Ag-promoted Ω phase strengthening, yet this approach is intrinsically limited by the high cost of Ag and the rapid coarsening of the Ω phase at service temperatures near 200 °C. To overcome these limitations, a machine learning–guided element substitution strategy was investigated. This approach integrates key alloy factor screening, Shapley Additive Explanations (SHAP) an alysis and multi-objective active learning to identify the alloy factors governing room-temperature yield strength (RTYS) and thermal-exposure yield strength (TEYS). Low-cost elements, including Si, Mn, Ti, and Zr, were identified to enable simultaneous enhancement of RTYS and TEYS. Guided by these insights, a low-cost, high-strength, heat-resistant alloy featuring core–shell θ′ phases (Al-5.18Cu-0.28Mg-0.32Mn-0.18Si-0.12Zr-0.08Ti, wt.%) was designed. After T6 treatment, the alloy achieves a RTYS of 432 ± 3 MPa and retains 87% of its yield strength after thermal exposure at 200 °C for 100 h. Compared with the commercial 2040 alloy, the present alloy exhibits increases of 6.6% and 29.4% in RTYS and TEYS, respectively, while raw-material cost is reduced by over 70%. These performance enhancements arise from a synergistic optimization of composition and microstructure. In particular, Si promotes the formation of thermally stable C/L interfacial phases at the θ′/α-Al interface, imparting higher stability than the conventional Mg-Ag core-shell Ω phases strengthening. The reduced Mg content raises the solution-treatment temperature window, enabling more complete Cu dissolution and increasing the number density of θ′ phases, whereas Al6Mn and Al3(Zr, Ti) dispersoids further enhance the thermal stability of the microstructure.
Al-Cu-Mg合金的耐热性通常是通过Ag促进Ω相强化来实现的，但这种方法本质上受到Ag的高成本和Ω相在200°C附近使用温度下快速粗化的限制。为了克服这些限制，研究了一种机器学习引导的元素替换策略。该方法集成了关键合金因素筛选、Shapley加性解释（SHAP）分析和多目标主动学习，以确定控制室温屈服强度（RTYS）和热暴露屈服强度（TEYS）的合金因素。低成本元素，包括Si， Mn， Ti和Zr，被确定为能够同时增强RTYS和TEYS。在这些见解的指导下，设计了一种低成本，高强度，具有核壳θ′相的耐热合金（Al-5.18Cu-0.28Mg-0.32Mn-0.18Si-0.12Zr-0.08Ti, wt.%）。经T6处理后，合金的RTYS为432±3 MPa，在200℃下热暴露100 h后，合金的屈服强度保持了87%。与2040合金相比，该合金的RTYS和TEYS分别提高了6.6%和29.4%，而原材料成本降低了70%以上。这些性能增强来自于成分和微观结构的协同优化。特别是在θ′/α-Al界面处，Si促进了热稳定的C/L界面相的形成，比传统的Mg-Ag核-壳Ω相强化具有更高的稳定性。Mg含量的降低提高了固溶处理温度窗，使Cu溶解更完全，θ′相的数量密度增加，而Al6Mn和Al3（Zr, Ti）弥散体进一步增强了显微组织的热稳定性。
Thin-Walled Structures
A linear isotropic thermoelastic shell model including Gauss curvature effects: A theoretical derivation and a nalytical solutions
Eric Sinclair Fongho, Platon Dongmo Nizegha, Arno Roland Ngatcha Ndengna, Joseph Nkongho Anyi, Jean Chills Amba, Robert Nzengwa
doi:10.1016/j.tws.2026.115151
含高斯曲率效应的线性各向同性热弹性壳模型：理论推导和解析解
This paper presents a thermoelastic extension of the Nzengwa–Tagne (N–T) kinematic framework. The objective is to derive a reduced two-dimensional shell model that explicitly retains the contribution of the Gauss curvature through the third fundamental form, which is rare in most shell equations. The main contributions include the derivation of a linear thermoelastic shell model retaining the third fundamental form, the identification of Gauss-related stress resultants, and the formulation of an exact reduced two-dimensional model based on Hamilton’s principle and Navier’s approach, together with closed-form an alytical benchmarks for representative shell configurations. Several first-order shear deformation theories are revisited in this approach. In the present work, the Gauss-related contribution is introduced at the level of the strain–displacement relation and consistently propagated to the thermoelastic constitutive equations. The formulation assumes small displacements, prescribed temperature, and a reduction of the three-dimensional (3D) stress state to a two-dimensional (2D) plane stress framework. Material properties are assumed temperature-independent, which is consistent with a linear thermoelastic framework and appropriate for moderate thermal variations. This choice allows the derivation of closed-form an alytical solutions while preserving thermoelastic coupling. The strain field includes a quadratic term associated with the variation of the third fundamental form, leading to additional curvature–rotation coupling effects (Gauss-related moments) arising from the intrinsic geometry of the shell. It is shown that these contributions remain negligible in the classical thin-shell regime, but become significant for moderately thick and doubly curved shells. The proposed model therefore provides a consistent framework to isolate and quantify Gauss curvature effects within a reduced shell theory. An alytical solutions are derived for various shell configurations and boundary conditions. For dynamic problems, Navier-type solutions are obtained using Hamilton’s principle for simply supported shells. The results show good agreement with available solutions in the literature. Comparisons with LD2/LD4, CST and FSDT models are presented through displacement fields and stress resultants. The present formulation is intended as a theoretical benchmark model and remains fully compatible with finite element implementation. Extensions including transverse shear and thickness stretching effects are discussed as perspectives for thicker shell regimes.
本文提出了Nzengwa-Tagne （N-T）运动框架的热弹性扩展。目标是推导出一个简化的二维壳模型，通过第三种基本形式明确地保留高斯曲率的贡献，这在大多数壳方程中是罕见的。主要贡献包括推导了保留第三种基本形式的线性热弹性壳模型，确定了高斯相关应力结果，基于Hamilton原理和Navier方法的精确简化二维模型的公式，以及代表性壳构型的封闭形式分析基准。几种一阶剪切变形理论在这种方法中被重新审视。在本工作中，高斯相关贡献被引入到应变-位移关系水平，并一致地传播到热弹性本构方程中。该配方假定小位移，规定温度，并将三维（3D）应力状态减小到二维（2D）平面应力框架。假设材料性能与温度无关，这与线性热弹性框架一致，适合于适度的热变化。这种选择允许推导封闭形式的解析解，同时保持热弹性耦合。应变场包括与第三种基本形式的变化相关的二次项，导致壳的固有几何形状产生额外的曲率-旋转耦合效应（高斯相关矩）。结果表明，这些贡献在经典薄壳状态下仍然可以忽略不计，但在中等厚度和双弯曲壳状态下变得显著。因此，提出的模型提供了一个一致的框架来隔离和量化高斯曲率效应在一个简化壳理论。导出了各种壳体构型和边界条件下的解析解。对于动力问题，利用Hamilton原理得到了简支壳的navier型解。所得结果与文献中已有的解相吻合。通过位移场和应力结果与LD2/LD4、CST和FSDT模型进行了比较。本公式旨在作为一个理论基准模型，并保持与有限元实现完全兼容。包括横向剪切和厚度拉伸效应在内的扩展作为厚壳结构的观点进行了讨论。
