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This study presents a novel phase-field model for ductile fracture by the introduction of both the plastic driving force and the degrading fracture toughness into crack phase-field computations based on the phenomenological justification for ductile fracture in elastoplastic materials. Assuming that the constitutive work density consists of elastic, pseudo-plastic and crack components, we derive the governing equations from local and global optimization problems within the continuum thermodynamics framework. In addition to the elastic strain energy, the plastic strain energy also works as a driving force to sustain damage evolution. Additionally, we introduce a degrading fracture toughness to reflect the evolution of micro-defects and their coalescences into each other that are caused by accumulated plastic deformation. Equipped with these ingredients, the proposed model realizes the reduction of both stiffness and fracture toughness to simulate the failure phenomena of elastoplastic materials. Several numerical examples are presented to demonstrate the capability of the proposed model in reproducing some typical ductile fracture behaviors. The findings and perspectives are subsequently summarized.

The present study proposes an MPM (material point method)–FEM (finite element method) hybrid analysis method for simulating granular mass–water interaction problems, in which the granular mass causes dynamic motion of the surrounding water. While the MPM is applied to the solid (soil) phase whose motion is suitably represented by Lagrangian description, the FEM is applied to the fluid (water) phase that is adapted for Eulerian description. Also, the phase-field approach is employed to capture the free surface. After the accuracy of the proposed method is tested by comparing the results to some analytical solutions of the consolidation theory, several numerical examples are presented to demonstrate its capability in simulating fluid motions induced by granular mass movements.

The present study proposes a topology optimization of composites considering elastoplastic deformation to maximize the energy absorption capacity of a structure under a prescribed material volume. The concept of a so-called multiphase material optimization , which is originally defined for a continuous damage model, is extended to elastoplastic composites with appropriate regularization for material properties in order to regularize material parameters between two constituents. In this study, we formulate the analytical sensitivity for topology optimization considering elastoplastic deformationand its path-dependency. For optimization applying a gradient-based method, the accuracy of sensitivities iscritical to obtain a reliable optimization result. The proposed analytical sensitivity method takes the derivative of the total stress which satisfies equilibrium equation instead of that of the incremental stress and does not need implicit sensitivity terms. It is verified that the proposed method can provide highly accurate sensitivity enough to obtain reliable optimization results by comparing with that evaluated from the finite difference approach.

Solid electrolytes encompass various types of nanodefects, including grain boundaries and nanovoids at the Li-metal/solid electrolyte interface, where lithium dendrite penetration has been extensively observed. Despite the importance of ion transport near grain boundaries with different anisotropy and the combinatorial effects with interfacial nanovoids, a comprehensive understanding of these phenomena has remains elusive. Here, we develop a chemo-electro-mechanical phase-field model to elucidate how Li penetrates Li7La3Zr2O12 in the co-presence of grain boundaries and interfacial nanovoids. The investigation unveils a grain-boundary-anisotropy-dependent behavior for Li-ion transport correlated with the presence of interfacial nanovoids. Notably, the Σ1 grain boundary exhibits faster Li dendrite growth, particularly in the co-presence of interfacial nanovoids. The model quantitatively reveals whether interfacial electronic properties dominate Li dendrite morphology and penetration, providing a strategy for designing stable Li/solid electrolyte interfaces. These findings help prioritize approaches for optimally tailoring nanodefects and exploiting synergetic effects at the interface to prevent dendrite formation.Grain boundary nanodefects exist in solid electrolytes but detailed factors affecting ion transport are still limited. Here, a chemo-electro-mechanical phase-field model shows how Li penetrates Li7La3Zr2O12 in the co-presence of grain boundaries and interfacial nanovoids

The present study proposes topology optimization of a micro-structure for composites considering the ma-cro-scopic structural response, applying a decoupling multi-scale analysis based on a homogenization approach. In this study, it is assumed that topology of macro-structure is unchanged and that topology of micro-structure is unique over the macro-structure. The stiffness of the macro-structure is maximized with a prescribed material volume of constituents under linear elastic regime. A gradient-based optimization strategy is applied and an analytical sensitivity approach based on numerical material tests is introduced. It was verified from a series of numerical examples that the proposed method has great potential for advanced material design.

Earthquakes that cause extensive damage occur frequently in Japan, the most recent being the Noto Peninsula earthquake on January 1, 2024. To facilitate such a recovery, we introduce a community-based participatory research program implemented through cooperation between universities and local communities after the 2011 Great East Japan Earthquake. In this project, the university and the town of Shichigahama, one of the affected areas, collaborated to hold annual workshops in the target area, which evolved into a climate monitoring survey. Even in Japan, where disaster prevention planning is widespread, various problems arise in the process of emergency response, recovery and reconstruction, and building back better when disasters occur. As is difficult for residents and local governments to solve these problems alone, it is helpful when experts participate in the response process. In this study, we interviewed town hall and university officials as representatives of local residents regarding this project and discussed their mutual concerns. The community-based participatory research framework developed in the Shichigahama project could be used in the recovery from the Noto Peninsula Earthquake as well as in future reconstruction and disaster management projects.

This paper presents a two-scale topology optimization framework for determining the optimal microstructure in porous material under transient heat conduction and transfer. The new optimization model, which can consider the surface area directly from microstructure topology as the size-dependent term, is introduced to enhance the heat transfer performance. In more detail, a homogenization method capable of considering the size-dependent microscopic heat transfer effect is adopted to express the microscopic material responses. A well-known material interpolation, referred to as the SIMP approach, and the design-dependent linear function are used for interpolating intermediate material properties. The minimal transient heat compliance is chosen as an objective function in this optimization problem. For the sensitivity analysis, a coupled-adjoint variable method is adopted to derive transient sensitivity formulation. The analysis shows that the proposed topology optimization model captures not only the transient heat but also the size effect of the microstructure in a transient heat analysis in porous material.

This study proposes a novel structural optimization framework based on quantum variational circuits, in which the multiplier acting on the cross-sectional area of each rod in a truss structure as an updater is used as a design variable. Specifically, we employ a classical processor for structural analysis with the finite element method, and the Quantum Approximate Optimization Algorithm (QAOA) is subsequently performed to update the cross-sectional area so that the compliance is minimized. The advantages of this framework can be seen in three key aspects. First, by defining design variables as multipliers, rather than simply reducing the design variable to a binary candidate of inclusion or exclusion (corresponding to qubit states, “0” and “1”), it provides greater flexibility in adjusting the cross-sectional area of the rod at each iteration of the optimization process. Second, the multipliers acting on rods are encoded with on-off encoding, eliminating additional constraints in the convergence judgement. As a result, the objective function is in a simple format, enabling efficient optimization using QAOA. Third, a fixed linear ramp schedule (FLRS) for variational parameter setting bypasses the classical optimization process, thereby improving the operational efficiency of the framework. In the two structural cases investigated in this study, the proposed approach highlights the feasibility and applicability potential of quantum computing in advancing engineering design and optimization. Numerical experiments have demonstrated the effectiveness of this framework, providing a firm foundation for future research on quantum-assisted optimization methods in engineering fields.

A Correction to this paper has been published: https://doi.org/10.1007/s10346-021-01642-4