Pub Date : 2025-12-18DOI: 10.1016/j.jmps.2025.106477
Pradeep K. Bal , Adam Ouzeri , Marino Arroyo
Epithelial tissues undergo complex morphogenetic transformations driven by cellular and cytoskeletal dynamics. To understand the emergent tissue mechanics resulting from sub-cellular mechanisms, we formulate a fully nonlinear continuum theory for epithelial shells that coarse-grains an underlying 3D vertex model, whose surfaces are in turn patches of active viscoelastic gel undergoing turnover. Our theory relies on two ingredients. First, we relate the deformation of apical, basal and lateral surfaces of cells to the continuum deformation of the tissue mid-surface and a thickness director field. We explore two variants of the theory, a Cosserat theory accommodating through-thickness tilt of cells, and a Kirchhoff theory assuming that lateral cell surfaces remain perpendicular to the mid-surface. Second, by adopting a variational formalism of irreversible thermodynamics, we construct an effective Rayleighian functional of the tissue constrained by the cellular-continuum kinematic relations, which therefore depends on continuum fields only. This functional allows us to obtain the governing equations of the continuum theory and is the basis for efficient finite element simulations. Verification against explicit 3D cellular model simulations demonstrates the accuracy of the proposed theory in capturing epithelial buckling dynamics. Furthermore, we show that the Cosserat theory is required to model tissues exhibiting apicobasal asymmetry of active tension. Our work provides a general framework for further studies integrating refined subcellular models into continuum descriptions of epithelial mechanobiology.
{"title":"Continuum theory for the mechanics of curved epithelial shells by coarse-graining an ensemble of active gel cellular surfaces","authors":"Pradeep K. Bal , Adam Ouzeri , Marino Arroyo","doi":"10.1016/j.jmps.2025.106477","DOIUrl":"10.1016/j.jmps.2025.106477","url":null,"abstract":"<div><div>Epithelial tissues undergo complex morphogenetic transformations driven by cellular and cytoskeletal dynamics. To understand the emergent tissue mechanics resulting from sub-cellular mechanisms, we formulate a fully nonlinear continuum theory for epithelial shells that coarse-grains an underlying 3D vertex model, whose surfaces are in turn patches of active viscoelastic gel undergoing turnover. Our theory relies on two ingredients. First, we relate the deformation of apical, basal and lateral surfaces of cells to the continuum deformation of the tissue mid-surface and a thickness director field. We explore two variants of the theory, a Cosserat theory accommodating through-thickness tilt of cells, and a Kirchhoff theory assuming that lateral cell surfaces remain perpendicular to the mid-surface. Second, by adopting a variational formalism of irreversible thermodynamics, we construct an effective Rayleighian functional of the tissue constrained by the cellular-continuum kinematic relations, which therefore depends on continuum fields only. This functional allows us to obtain the governing equations of the continuum theory and is the basis for efficient finite element simulations. Verification against explicit 3D cellular model simulations demonstrates the accuracy of the proposed theory in capturing epithelial buckling dynamics. Furthermore, we show that the Cosserat theory is required to model tissues exhibiting apicobasal asymmetry of active tension. Our work provides a general framework for further studies integrating refined subcellular models into continuum descriptions of epithelial mechanobiology.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106477"},"PeriodicalIF":6.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In crystals, grains with different orientations form grain boundaries (GBs), while the meeting of three neighboring GBs gives rise to triple junctions (TJs). TJs are therefore ubiquitous crystalline defects in polycrystals and bear effect to the microstructural evolution of GB network via modulating GB migration and grain growth kinetics. Since the plastic deformation of TJs depend inherently on their atomic structures and migration pathways, it is crucial to establish a direct connection between the TJ kinetics and the grain growth of polycrystals. We propose a multiscale formulation to incorporate molecular dynamics (MD), kinetic Monte Carlo (kMC) simulation, and theoretical modeling of TJ kinetics to unravel the importance of structure-dependent TJ migration mechanisms in regulating GB network evolution in polycrystals. At an atomic scale, MD simulations have demonstrated that both the TJ disclinations and asymmetry can inhibit the glide of disconnections into TJs and thus obstruct the migration of TJs. Based on the atomistic insights, a theoretical model has been developed to describe the structure-dependent TJ migration kinetics, differing from the infinite TJ mobility hypothesis frequently utilized in existing formulations. The migration of an individual TJ, which is featured by the flux and accumulation of disconnections and their interactions with disclinations, can be captured by our model using kMC simulations, furnishing a dataset of TJ structure-mobility relationship. The atomistically-informed TJ kinetics and TJ mobility dataset are incorporated into a polycrystalline kMC model, which is capable of modelling TJ-influenced grain growth kinetics and grain size distribution evolution. Our work not only provides physical insights into the TJ-mediated GB migration mechanisms, but also offers a multiscale formulation for predicting the evolution of GB network in polycrystalline metals.
{"title":"Multiscale modeling on evolving grain boundary network in polycrystals incorporating triple junction migration","authors":"Qishan Huang , Zhenghao Zhang , Haofei Zhou , Wei Yang","doi":"10.1016/j.jmps.2025.106485","DOIUrl":"10.1016/j.jmps.2025.106485","url":null,"abstract":"<div><div>In crystals, grains with different orientations form grain boundaries (GBs), while the meeting of three neighboring GBs gives rise to triple junctions (TJs). TJs are therefore ubiquitous crystalline defects in polycrystals and bear effect to the microstructural evolution of GB network via modulating GB migration and grain growth kinetics. Since the plastic deformation of TJs depend inherently on their atomic structures and migration pathways, it is crucial to establish a direct connection between the TJ kinetics and the grain growth of polycrystals. We propose a multiscale formulation to incorporate molecular dynamics (MD), kinetic Monte Carlo (kMC) simulation, and theoretical modeling of TJ kinetics to unravel the importance of structure-dependent TJ migration mechanisms in regulating GB network evolution in polycrystals. At an atomic scale, MD simulations have demonstrated that both the TJ disclinations and asymmetry can inhibit the glide of disconnections into TJs and thus obstruct the migration of TJs. Based on the atomistic insights, a theoretical model has been developed to describe the structure-dependent TJ migration kinetics, differing from the infinite TJ mobility hypothesis frequently utilized in existing formulations. The migration of an individual TJ, which is featured by the flux and accumulation of disconnections and their interactions with disclinations, can be captured by our model using kMC simulations, furnishing a dataset of TJ structure-mobility relationship. The atomistically-informed TJ kinetics and TJ mobility dataset are incorporated into a polycrystalline kMC model, which is capable of modelling TJ-influenced grain growth kinetics and grain size distribution evolution. Our work not only provides physical insights into the TJ-mediated GB migration mechanisms, but also offers a multiscale formulation for predicting the evolution of GB network in polycrystalline metals.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106485"},"PeriodicalIF":6.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.jmps.2025.106479
Ye Feng , Lu Hai
This paper develops a novel class of phase-field cohesive fracture models that naturally incorporate strong displacement discontinuities within a continuum framework. We derive the nonhomogeneous analytical solutions in one dimension (1D), demonstrating for the first time the emergence of a Dirac δ-function-type strain in phase-field models from crack nucleation to complete rupture, without requiring the limit of vanishing phase-field characteristic length ℓ. This enables the direct representation of discrete crack displacement jumps. We demonstrate the instability of homogeneous solutions through a second-order stability analysis, further highlighting the significance of the derived singular nonhomogeneous solutions. The proposed approach overcomes the limitation of conventional phase-field methods in capturing strong discontinuities, while retaining their advantages-such as mesh objectivity and the ability to handle complex crack topologies-due to the retained diffusive phase-field distribution. Furthermore, the implementation of the cohesive law into the phase-field model can be achieved in a more straightforward manner. The model’s effectiveness beyond 1D is validated by 2D and 3D numerical examples. These developments may open new possibilities for: (i) multiscale fracture analysis where competing length scales coexist, and (ii) multiphysics problems requiring precise kinematics of crack opening.
{"title":"Phase-field cohesive fracture models with strong displacement discontinuities","authors":"Ye Feng , Lu Hai","doi":"10.1016/j.jmps.2025.106479","DOIUrl":"10.1016/j.jmps.2025.106479","url":null,"abstract":"<div><div>This paper develops a novel class of phase-field cohesive fracture models that naturally incorporate strong displacement discontinuities within a continuum framework. We derive the nonhomogeneous analytical solutions in one dimension (1D), demonstrating <em>for the first time</em> the emergence of a Dirac <em>δ</em>-function-type strain in phase-field models from crack nucleation to complete rupture, without requiring the limit of vanishing phase-field characteristic length ℓ. This enables the direct representation of discrete crack displacement jumps. We demonstrate the instability of homogeneous solutions through a second-order stability analysis, further highlighting the significance of the derived singular nonhomogeneous solutions. The proposed approach overcomes the limitation of conventional phase-field methods in capturing strong discontinuities, while retaining their advantages-such as mesh objectivity and the ability to handle complex crack topologies-due to the retained diffusive phase-field distribution. Furthermore, the implementation of the cohesive law into the phase-field model can be achieved in a more straightforward manner. The model’s effectiveness beyond 1D is validated by 2D and 3D numerical examples. These developments may open new possibilities for: (i) multiscale fracture analysis where competing length scales coexist, and (ii) multiphysics problems requiring precise kinematics of crack opening.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106479"},"PeriodicalIF":6.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.jmps.2025.106480
Hio Konishi , Seishiro Matsubara , So Nagashima , Dai Okumura
In this study, we refine the strain energy function of fiber-reinforced hyperelastic materials by adding a unique nonlinear term with a negative exponent on , i.e., , where is the pseudo-invariant of the right Cauchy–Green tensor, defined as the squared stretch in a fiber direction. This additional term is comprehensively tested when combined with the simple linear form or the conventional quadratic form . The conventional quadratic form causes unphysical material instability under principal stretches, where the instantaneous stiffness changes negatively in certain deformation regions. Using the negative exponent on can prevent this instability. The specific linear combination, , is unconditionally free from the instability under principal stretches. The instantaneous stiffness is linearly enhanced by fiber reinforcement, unlike the complex responses by a quadratic combination. This refinement is not incompatible with the physical interpretation of the material instability under simple shear deformation. A comprehensive understanding is achieved through the sufficient condition for derived from the strong ellipticity inequality.
{"title":"Using a negative exponent to prevent unphysical instability in fiber-reinforced hyperelastic materials","authors":"Hio Konishi , Seishiro Matsubara , So Nagashima , Dai Okumura","doi":"10.1016/j.jmps.2025.106480","DOIUrl":"10.1016/j.jmps.2025.106480","url":null,"abstract":"<div><div>In this study, we refine the strain energy function of fiber-reinforced hyperelastic materials by adding a unique nonlinear term with a negative exponent on <span><math><msub><mi>I</mi><mn>4</mn></msub></math></span>, i.e., <span><math><mrow><msubsup><mi>I</mi><mn>4</mn><mrow><mo>−</mo><mi>M</mi></mrow></msubsup><mo>−</mo><mn>1</mn><mspace></mspace><mrow><mo>(</mo><mi>M</mi><mo>></mo><mn>0</mn><mo>)</mo></mrow></mrow></math></span>, where <span><math><msub><mi>I</mi><mn>4</mn></msub></math></span> is the pseudo-invariant of the right Cauchy–Green tensor, defined as the squared stretch in a fiber direction. This additional term is comprehensively tested when combined with the simple linear form <span><math><mrow><mo>(</mo><mrow><msub><mi>I</mi><mn>4</mn></msub><mo>−</mo><mn>1</mn></mrow><mo>)</mo></mrow></math></span> or the conventional quadratic form <span><math><msup><mrow><mo>(</mo><mrow><msub><mi>I</mi><mn>4</mn></msub><mo>−</mo><mn>1</mn></mrow><mo>)</mo></mrow><mn>2</mn></msup></math></span>. The conventional quadratic form causes unphysical material instability under principal stretches, where the instantaneous stiffness changes negatively in certain deformation regions. Using the negative exponent on <span><math><msub><mi>I</mi><mn>4</mn></msub></math></span> can prevent this instability. The specific linear combination, <span><math><mrow><mrow><mo>(</mo><mrow><msub><mi>I</mi><mn>4</mn></msub><mo>−</mo><mn>1</mn></mrow><mo>)</mo></mrow><mo>+</mo><mrow><mo>(</mo><mrow><msubsup><mi>I</mi><mn>4</mn><mrow><mo>−</mo><mi>M</mi></mrow></msubsup><mo>−</mo><mn>1</mn></mrow><mo>)</mo></mrow><mo>/</mo><mi>M</mi></mrow></math></span>, is unconditionally free from the instability under principal stretches. The instantaneous stiffness is linearly enhanced by fiber reinforcement, unlike the complex responses by a quadratic combination. This refinement is not incompatible with the physical interpretation of the material instability under simple shear deformation. A comprehensive understanding is achieved through the sufficient condition for <span><math><msub><mi>I</mi><mn>4</mn></msub></math></span> derived from the strong ellipticity inequality.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106480"},"PeriodicalIF":6.0,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.jmps.2025.106481
Baixi Chen, Alessandro Fascetti
Concrete failure mechanics exhibit significant variability at the macroscopic scale, which is predominantly driven by stochasticity at the spatial scale of the coarse aggregate particles, generally referred to as mesoscopic scale. However, mesoscale material parameters are difficult to estimate, making uncertainty quantification a fundamental challenge. To address this limitation, a data-driven multiscale inverse inference framework is proposed to quantify the stochastic mesoscale behavior by integrating both mesoscale and macroscale observations. In this framework, a stochastic data-driven model using a hybrid Proper Orthogonal Decomposition–Gaussian Process Regression (POD-GPR) algorithm is first developed based on data generated by mesoscale Lattice Discrete Particle Model (LDPM) simulations. Leveraging this efficient data-driven model, a novel multiscale Bayesian inverse inference method is proposed to infer the stochastic distributions of the mesoscale features. When applied to experimental data, the proposed framework successfully captures the stochastic distributions of mesoscale material parameters, reproduces macroscale responses, and outperforms conventional single-scale Bayesian inference approaches. Additionally, SHapley Additive exPlanations (SHAP) are integrated to further interpret the effect of mesoscale stochastic material behavior on macroscale uncertainty, offering valuable insights for the accuracy improvement of LDPM simulations and future mesoscale-level optimization to achieve more robust macroscale performance.
{"title":"Stochastic data-driven inference of mesoscale lattice discrete particle model parameters via multiscale observations","authors":"Baixi Chen, Alessandro Fascetti","doi":"10.1016/j.jmps.2025.106481","DOIUrl":"10.1016/j.jmps.2025.106481","url":null,"abstract":"<div><div>Concrete failure mechanics exhibit significant variability at the macroscopic scale, which is predominantly driven by stochasticity at the spatial scale of the coarse aggregate particles, generally referred to as mesoscopic scale. However, mesoscale material parameters are difficult to estimate, making uncertainty quantification a fundamental challenge. To address this limitation, a data-driven multiscale inverse inference framework is proposed to quantify the stochastic mesoscale behavior by integrating both mesoscale and macroscale observations. In this framework, a stochastic data-driven model using a hybrid Proper Orthogonal Decomposition–Gaussian Process Regression (POD-GPR) algorithm is first developed based on data generated by mesoscale Lattice Discrete Particle Model (LDPM) simulations. Leveraging this efficient data-driven model, a novel multiscale Bayesian inverse inference method is proposed to infer the stochastic distributions of the mesoscale features. When applied to experimental data, the proposed framework successfully captures the stochastic distributions of mesoscale material parameters, reproduces macroscale responses, and outperforms conventional single-scale Bayesian inference approaches. Additionally, SHapley Additive exPlanations (SHAP) are integrated to further interpret the effect of mesoscale stochastic material behavior on macroscale uncertainty, offering valuable insights for the accuracy improvement of LDPM simulations and future mesoscale-level optimization to achieve more robust macroscale performance.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106481"},"PeriodicalIF":6.0,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.jmps.2025.106478
Xin Liu , Hyunsoo Lee , Yang Li , Liam Myhill , David Rodney , Pierre-Antoine Geslin , Nikhil Chandra Admal , Giacomo Po , Enrique Martinez , Yinan Cui
Multi-principal element alloys (MPEAs) continue to attract considerable attention. However, one fundamental question regarding their plasticity remains far from well understood, namely, how the nanoscale heterogeneity and chemical short-range order (SRO) control dislocation motion and plasticity. Different from previous studies incorporating statistical variations of the energy landscape into full dislocation dynamics, the current work proposes an innovative atomistically informed partial dislocation dynamics (PDD) method, which directly considers the spatially-correlated non-uniform planar fault energy (PFE) at the atomic scale, and at the same time benefits from the larger temporal and spatial scales of the dislocation dynamics methods. Through systematic analysis, we find that the PFE field exhibits a negative correlation along the atomic slip direction, which reduces the critical stress required for dislocation motion in that direction. In contrast, the correlation characteristics along other directions can be approximated as uncorrelated noise, which also contributes to strengthening. In addition, it is found that SRO only slightly enhances the correlation strength along certain crystallographic directions, while it weakens the degree of negative correlation along the slip direction. Overall, the increase in the mean PFE induced by SRO significantly contributes to the strengthening of the dislocation depinning transition. The proposed model provides new opportunities for designing MPEAs with tailored macroscopic mechanical properties by manipulating their atomic distribution and spatial correlations.
{"title":"Atomistically informed partial dislocation dynamics of multi-principal element alloys","authors":"Xin Liu , Hyunsoo Lee , Yang Li , Liam Myhill , David Rodney , Pierre-Antoine Geslin , Nikhil Chandra Admal , Giacomo Po , Enrique Martinez , Yinan Cui","doi":"10.1016/j.jmps.2025.106478","DOIUrl":"10.1016/j.jmps.2025.106478","url":null,"abstract":"<div><div>Multi-principal element alloys (MPEAs) continue to attract considerable attention. However, one fundamental question regarding their plasticity remains far from well understood, namely, how the nanoscale heterogeneity and chemical short-range order (SRO) control dislocation motion and plasticity. Different from previous studies incorporating statistical variations of the energy landscape into full dislocation dynamics, the current work proposes an innovative atomistically informed partial dislocation dynamics (PDD) method, which directly considers the spatially-correlated non-uniform planar fault energy (PFE) at the atomic scale, and at the same time benefits from the larger temporal and spatial scales of the dislocation dynamics methods. Through systematic analysis, we find that the PFE field exhibits a negative correlation along the atomic slip direction, which reduces the critical stress required for dislocation motion in that direction. In contrast, the correlation characteristics along other directions can be approximated as uncorrelated noise, which also contributes to strengthening. In addition, it is found that SRO only slightly enhances the correlation strength along certain crystallographic directions, while it weakens the degree of negative correlation along the slip direction. Overall, the increase in the mean PFE induced by SRO significantly contributes to the strengthening of the dislocation depinning transition. The proposed model provides new opportunities for designing MPEAs with tailored macroscopic mechanical properties by manipulating their atomic distribution and spatial correlations.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106478"},"PeriodicalIF":6.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-07DOI: 10.1016/j.jmps.2025.106462
M.A. Kumar , T. Virazels , J. García-Molleja , F. Sket , J. A. Rodríguez-Martínez Rodríguez-Martínez , K. Ravi-Chandar
<div><div>In this paper, we have conducted dynamic ring expansion tests on 3D-printed AlSi10Mg porous samples utilizing both electromagnetic and mechanical testing techniques. The electromagnetic loading setup developed by Zhang and Ravi-Chandar (2006, 2008) is employed as a benchmark for evaluating and comparing the performance of the experimental configuration recently proposed by Nieto-Fuentes et al. (2023) to investigate the fragmentation of metallic rings using a pneumatic launcher. A total of 67 experiments have been carried out covering a wide range of strain rates from <span><math><mrow><mn>2200</mn><mspace></mspace><msup><mtext>s</mtext><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> to <span><math><mrow><mn>16300</mn><mspace></mspace><msup><mtext>s</mtext><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>. The tests performed with both experimental techniques were imaged using high-speed cameras to obtain time-resolved information on the mechanics of sample deformation and fragmentation. The recorded data allowed us to determine the number of fragments, the elongation of the specimens at the onset of fracture, and the fragmentation time. Moreover, the fragments ejected from the samples have been soft recovered, measured, and weighed. A good correlation is observed between the results obtained from electromagnetic and mechanical loading setups regarding the fragments size distribution and the evolution of the number of fragments with the loading rate. This agreement serves as a robust validation for the experimental configuration put forth by <span><span>Nieto-Fuentes et al. (2023)</span></span>, which allowed reaching higher strain rates than the setup of <span><span>Zhang and Ravi-Chandar, 2006</span></span>, <span><span>Zhang and Ravi-Chandar, 2008</span></span>, and it is notable for its simplicity, fast operation, and quick assembly. In addition, scanning electron microscopy and X-ray tomography analysis performed on recovered fragments from tests conducted at different expansion velocities with both testing techniques has provided indications on the evolution of the porous microstructure of the material at high strain rates, showing that the porosity of 3D-printed AlSi10Mg is instrumental for the propagation of cracks leading to the fragmentation of the rings. Moreover, fractography analysis of the crack surfaces revealed that while the fractures occurred without the preceding formation of necks, yet the fracture at the microscopic level was essentially ductile. The influence of the porous microstructure on the fragmentation mechanisms has been further investigated through finite element simulations that incorporate the voids’ size distribution of the specimens obtained from X-ray tomography analysis (Marvi-Mashhadi et al., 2021). The numerical results have demonstrated both quantitative and qualitative agreement with the experiments, showing that large pores and clusters favor stress concentration and subs
在本文中，我们利用电磁和机械测试技术对3d打印的AlSi10Mg多孔样品进行了动态环膨胀测试。采用Zhang和Ravi-Chandar（2006, 2008）开发的电磁加载装置作为基准，评估和比较Nieto-Fuentes等人（2023）最近提出的实验配置的性能，以研究使用气动发射器的金属环的破碎。总共进行了67次实验，涵盖了从2200s−1到16300s−1的应变速率范围。使用这两种实验技术进行的测试使用高速摄像机进行成像，以获得关于样品变形和破碎力学的时间分辨信息。记录的数据使我们能够确定碎片的数量，断裂开始时标本的伸长率和碎片时间。此外，从样品中喷射出的碎片已被软回收、测量和称重。在电磁加载和机械加载条件下得到的碎片尺寸分布和碎片数量随加载速率的变化具有良好的相关性。该协议是对Nieto-Fuentes等人（2023）提出的实验配置的有力验证，该实验配置可以达到比Zhang和Ravi-Chandar， 2006， Zhang和Ravi-Chandar， 2008的设置更高的应变速率，并且其简单，快速操作和快速组装值得注意。此外，对两种测试技术在不同膨胀速度下进行的测试中恢复的碎片进行扫描电子显微镜和x射线断层扫描分析，提供了高应变速率下材料多孔微观结构演变的迹象，表明3d打印AlSi10Mg的孔隙率有助于裂纹的扩展，从而导致环的破碎。此外，裂纹表面的断口分析表明，虽然断裂发生时没有预先形成颈，但在微观层面上断裂基本上是延展性的。通过结合x射线断层扫描分析获得的样品的孔隙尺寸分布的有限元模拟，进一步研究了孔隙微观结构对破碎机制的影响（Marvi-Mashhadi et al., 2021）。数值计算结果与实验结果在定性和定量上都一致，表明大孔隙和大簇有利于应力集中，有利于裂缝的萌生和扩展。与Mott（1947）关于弹塑性材料无颈缩断裂的统计碎裂理论一致，从大孔缺陷和早期断裂发出的释放波似乎在确定印刷AlSi10Mg试样中碎裂尺寸分布的规模方面起着关键作用。
{"title":"High-speed fragmentation of porous metal rings","authors":"M.A. Kumar , T. Virazels , J. García-Molleja , F. Sket , J. A. Rodríguez-Martínez Rodríguez-Martínez , K. Ravi-Chandar","doi":"10.1016/j.jmps.2025.106462","DOIUrl":"10.1016/j.jmps.2025.106462","url":null,"abstract":"<div><div>In this paper, we have conducted dynamic ring expansion tests on 3D-printed AlSi10Mg porous samples utilizing both electromagnetic and mechanical testing techniques. The electromagnetic loading setup developed by Zhang and Ravi-Chandar (2006, 2008) is employed as a benchmark for evaluating and comparing the performance of the experimental configuration recently proposed by Nieto-Fuentes et al. (2023) to investigate the fragmentation of metallic rings using a pneumatic launcher. A total of 67 experiments have been carried out covering a wide range of strain rates from <span><math><mrow><mn>2200</mn><mspace></mspace><msup><mtext>s</mtext><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> to <span><math><mrow><mn>16300</mn><mspace></mspace><msup><mtext>s</mtext><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>. The tests performed with both experimental techniques were imaged using high-speed cameras to obtain time-resolved information on the mechanics of sample deformation and fragmentation. The recorded data allowed us to determine the number of fragments, the elongation of the specimens at the onset of fracture, and the fragmentation time. Moreover, the fragments ejected from the samples have been soft recovered, measured, and weighed. A good correlation is observed between the results obtained from electromagnetic and mechanical loading setups regarding the fragments size distribution and the evolution of the number of fragments with the loading rate. This agreement serves as a robust validation for the experimental configuration put forth by <span><span>Nieto-Fuentes et al. (2023)</span></span>, which allowed reaching higher strain rates than the setup of <span><span>Zhang and Ravi-Chandar, 2006</span></span>, <span><span>Zhang and Ravi-Chandar, 2008</span></span>, and it is notable for its simplicity, fast operation, and quick assembly. In addition, scanning electron microscopy and X-ray tomography analysis performed on recovered fragments from tests conducted at different expansion velocities with both testing techniques has provided indications on the evolution of the porous microstructure of the material at high strain rates, showing that the porosity of 3D-printed AlSi10Mg is instrumental for the propagation of cracks leading to the fragmentation of the rings. Moreover, fractography analysis of the crack surfaces revealed that while the fractures occurred without the preceding formation of necks, yet the fracture at the microscopic level was essentially ductile. The influence of the porous microstructure on the fragmentation mechanisms has been further investigated through finite element simulations that incorporate the voids’ size distribution of the specimens obtained from X-ray tomography analysis (Marvi-Mashhadi et al., 2021). The numerical results have demonstrated both quantitative and qualitative agreement with the experiments, showing that large pores and clusters favor stress concentration and subs","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106462"},"PeriodicalIF":6.0,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The concurrent multi-scale methods for microstructure related macro-cracking face challenges in both physical fidelity and computational efficiency. The physical fidelity issue arises from the fact that few models can simultaneously simulate the spatial-temporal evolution of microstructures (e.g. dislocations, multi-phase) and macro-cracking. The computational efficiency issue stems from the mismatch in scale: spatially each grid of macro-simulation corresponds to the whole domain of a micro-simulation and temporally each time step of macro-simulation may encompass many time steps of micro-simulation. This disparity often results in substantial computational expense. In the present work, we significantly accelerate such simulations by developing a machine learning bridged concurrent multi-scale framework for microstructure-related macro-cracking, while preserving main micro-features. First, we establish a phase-field model to simulate the spatial-temporal co-evolution of microstructures under various stress boundary conditions. These simulations generate the data for machine learning models prior to the micro-macro concurrent multi-scale simulations. Subsequently, the well-established machine learning models efficiently provides micro-information to each macro-grid at every time step of macro-cracking, significantly reducing the computational cost. This enables a bidirectional coupling: the macro-cracking behavior is influenced by local microstructures, while the microstructures are continuously updated as macro-cracking progresses. The framework accommodates arbitrary stress-, strain-, and energy-based macro-cracking criteria. We preliminarily validate its accuracy and effectiveness by simulating microstructure-related macro-cracking during 2D high-temperature deformation of film-hole-structured single-crystal superalloys. Under the complex stress states induced by the film holes, the simulated spatial-temporal microstructure evolution and the resulting macro-cracking behavior exhibit good agreement with experimental observations. The present work highlights the possibility of machine learning to accelerate concurrent multi-scale simulations, while maintaining physical fidelity.
{"title":"A machine learning bridged concurrent multi-scale computational framework for microstructure related macro-cracking","authors":"Ronghai Wu , Yufan Zhang , Jinze Pei , Zanpeng Shangguan , Yuxin Zhang , Lei Zeng , Zichao Peng , Heng Li","doi":"10.1016/j.jmps.2025.106469","DOIUrl":"10.1016/j.jmps.2025.106469","url":null,"abstract":"<div><div>The concurrent multi-scale methods for microstructure related macro-cracking face challenges in both physical fidelity and computational efficiency. The physical fidelity issue arises from the fact that few models can simultaneously simulate the spatial-temporal evolution of microstructures (e.g. dislocations, multi-phase) and macro-cracking. The computational efficiency issue stems from the mismatch in scale: spatially each grid of macro-simulation corresponds to the whole domain of a micro-simulation and temporally each time step of macro-simulation may encompass many time steps of micro-simulation. This disparity often results in substantial computational expense. In the present work, we significantly accelerate such simulations by developing a machine learning bridged concurrent multi-scale framework for microstructure-related macro-cracking, while preserving main micro-features. First, we establish a phase-field model to simulate the spatial-temporal co-evolution of microstructures under various stress boundary conditions. These simulations generate the data for machine learning models prior to the micro-macro concurrent multi-scale simulations. Subsequently, the well-established machine learning models efficiently provides micro-information to each macro-grid at every time step of macro-cracking, significantly reducing the computational cost. This enables a bidirectional coupling: the macro-cracking behavior is influenced by local microstructures, while the microstructures are continuously updated as macro-cracking progresses. The framework accommodates arbitrary stress-, strain-, and energy-based macro-cracking criteria. We preliminarily validate its accuracy and effectiveness by simulating microstructure-related macro-cracking during 2D high-temperature deformation of film-hole-structured single-crystal superalloys. Under the complex stress states induced by the film holes, the simulated spatial-temporal microstructure evolution and the resulting macro-cracking behavior exhibit good agreement with experimental observations. The present work highlights the possibility of machine learning to accelerate concurrent multi-scale simulations, while maintaining physical fidelity.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106469"},"PeriodicalIF":6.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.jmps.2025.106448
Srivatsa Bhat Kaudur, Claudio V. Di Leo
A continuum-scale thermo-chemo-mechanical modeling framework is developed to investigate the multiphysics behavior of thermochemical energy storage (TES) materials undergoing hydration and dehydration during thermal cycling. The formulation integrates species diffusion, chemical reaction kinetics, heat generation/transport, and mechanical deformation within a unified theoretical framework to resolve spatial and temporal evolution of species concentration, reaction progress, temperature, and stress across material domains. A series of non-dimensional parametric studies quantifies the influence of key material parameters, including thermal conductivity, diffusivity, and reaction kinetics, on transformation dynamics, revealing critical interdependencies among physical processes that govern TES performance. To illustrate the capabilities of the framework, simulations of representative potassium carbonate pellets are presented with constitutive models and material properties adopted from the literature. In order to isolate chemo-thermal effects and facilitate comparison with fully coupled simulations, initial case studies focus on pellet hydration/dehydration without mechanical coupling, demonstrating the predictive capability of the model in capturing chemo-thermal gradients and transient performance. Subsequently, a fully coupled simulation is presented to explicitly illustrate the influence of mechanical stresses on the progression of the reaction. Mechanical stress can alter local chemical equilibrium conditions, thereby enhancing or suppressing hydration and dehydration reactions. By systematically accounting for interactions between stress, reaction pathways, and transport phenomena, this framework enables a mechanistic understanding of the dynamic interplay of physical processes that govern energy storage efficiency and material reliability, ultimately supporting the design of more robust and high-performance TES systems.
{"title":"Coupled thermo-chemo-mechanical modeling of reactive solids: Applications to thermochemical energy storage materials","authors":"Srivatsa Bhat Kaudur, Claudio V. Di Leo","doi":"10.1016/j.jmps.2025.106448","DOIUrl":"10.1016/j.jmps.2025.106448","url":null,"abstract":"<div><div>A continuum-scale thermo-chemo-mechanical modeling framework is developed to investigate the multiphysics behavior of thermochemical energy storage (TES) materials undergoing hydration and dehydration during thermal cycling. The formulation integrates species diffusion, chemical reaction kinetics, heat generation/transport, and mechanical deformation within a unified theoretical framework to resolve spatial and temporal evolution of species concentration, reaction progress, temperature, and stress across material domains. A series of non-dimensional parametric studies quantifies the influence of key material parameters, including thermal conductivity, diffusivity, and reaction kinetics, on transformation dynamics, revealing critical interdependencies among physical processes that govern TES performance. To illustrate the capabilities of the framework, simulations of representative potassium carbonate pellets are presented with constitutive models and material properties adopted from the literature. In order to isolate chemo-thermal effects and facilitate comparison with fully coupled simulations, initial case studies focus on pellet hydration/dehydration without mechanical coupling, demonstrating the predictive capability of the model in capturing chemo-thermal gradients and transient performance. Subsequently, a fully coupled simulation is presented to explicitly illustrate the influence of mechanical stresses on the progression of the reaction. Mechanical stress can alter local chemical equilibrium conditions, thereby enhancing or suppressing hydration and dehydration reactions. By systematically accounting for interactions between stress, reaction pathways, and transport phenomena, this framework enables a mechanistic understanding of the dynamic interplay of physical processes that govern energy storage efficiency and material reliability, ultimately supporting the design of more robust and high-performance TES systems.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106448"},"PeriodicalIF":6.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.jmps.2025.106463
Denisa Martonová , Ellen Kuhl , Moritz Flaschel
Material Fingerprinting is an emerging approach for the rapid discovery of mechanical material models directly from experimental data. By interpreting a material’s response in standardized experiments as its fingerprint, Material Fingerprinting employs pattern recognition to match experimental data against a precomputed database, enabling real-time model discovery. This strategy is both fast and robust, as it avoids solving potentially non-convex optimization problems. Unlike traditional calibration methods, Material Fingerprinting simultaneously selects the most suitable material model and identifies its parameters. Since the fingerprint database is fully controllable, the method guarantees interpretable and physically meaningful models. In previous work, we showed the feasibility of this concept for experiments with homogeneous or heterogeneous deformation fields using synthetically generated data. Here we present the first experimental validation of Material Fingerprinting. We carefully design a fingerprint database for uniaxial tension/compression, equibiaxial tension as well as pure and simple shear experiments. Once computed in an offline phase, this database can be reused for rapid model discovery across diverse experimental datasets. We demonstrate that this single database enables the robust and efficient discovery of hyperelastic strain energy functions to accurately characterize the isotropic mechanical responses of rubber, hydrogel, and brain tissue in less than one second on a standard personal computer. To make this approach openly accessible for rapid material model discovery across laboratories, we release the database and the implementation of Material Fingerprinting as a pip-installable Python package alongside this publication.
{"title":"Material Fingerprinting for rapid discovery of hyperelastic models: First experimental validation","authors":"Denisa Martonová , Ellen Kuhl , Moritz Flaschel","doi":"10.1016/j.jmps.2025.106463","DOIUrl":"10.1016/j.jmps.2025.106463","url":null,"abstract":"<div><div>Material Fingerprinting is an emerging approach for the rapid discovery of mechanical material models directly from experimental data. By interpreting a material’s response in standardized experiments as its fingerprint, Material Fingerprinting employs pattern recognition to match experimental data against a precomputed database, enabling real-time model discovery. This strategy is both fast and robust, as it avoids solving potentially non-convex optimization problems. Unlike traditional calibration methods, Material Fingerprinting simultaneously selects the most suitable material model and identifies its parameters. Since the fingerprint database is fully controllable, the method guarantees interpretable and physically meaningful models. In previous work, we showed the feasibility of this concept for experiments with homogeneous or heterogeneous deformation fields using synthetically generated data. Here we present the first experimental validation of Material Fingerprinting. We carefully design a fingerprint database for uniaxial tension/compression, equibiaxial tension as well as pure and simple shear experiments. Once computed in an offline phase, this database can be reused for rapid model discovery across diverse experimental datasets. We demonstrate that this single database enables the robust and efficient discovery of hyperelastic strain energy functions to accurately characterize the isotropic mechanical responses of rubber, hydrogel, and brain tissue in less than one second on a standard personal computer. To make this approach openly accessible for rapid material model discovery across laboratories, we release the database and the implementation of Material Fingerprinting as a pip-installable Python package alongside this publication.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"208 ","pages":"Article 106463"},"PeriodicalIF":6.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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