Journal of The Mechanics and Physics of Solids最新文献

{"title":"Viscous solvent effect on fracture of predamaged double-network gels examined by pre-notch and post-notch crack tests","authors":"Yong Zheng ,&nbsp;Jian Ping Gong","doi":"10.1016/j.jmps.2024.105926","DOIUrl":"10.1016/j.jmps.2024.105926","url":null,"abstract":"<div><div>Double network (DN) gels, composed of two interpenetrating polymer networks with contrasting properties, garnered considerable attention since their invention due to large resistances to crack initiation and propagation. This study systematically investigates the effect of viscous solvent on the fracture behavior of DN gels through pre-notch and post-notch crack tests conducted on both water-swollen and ethylene glycol (EG)-swollen DN gels. Fracture energy analysis reveals that the chain dynamics changed by viscous solvent EG would remarkably reduce the two individual fracture energy contributions Γ_bulk and Γ_tip, originating from the energy dissipation in the bulk and in the crack tip vicinity, respectively. Furthermore, we observed that chain dynamics influence crack propagation behaviors and the molecular orientation of network strands ahead of crack tips in DN gels. Examination of the retardation patterns ahead of propagating crack tips allows for the analysis of the molecular orientation of network strands. Unusual butterfly-like retardation patterns were observed for the EG-swollen DN gels, in stark contrast to the conventional damage zone patterns seen in water-swollen DN gels. This suggests that the slowed chain dynamics induced by the viscous solvent EG lead to significant viscoelastic mechanical responses ahead of crack tips, which governs the stress/strain fields at the crack tip. This study offers valuable insights into the underlying toughening mechanism of DN gels, particularly regarding the effect of polymer chain dynamics. The experimental analysis, integrating findings on fracture energy contributions, crack propagation behaviors, and retardation observations from both pre-notch and post-notch crack tests, could be applied to characterize other soft materials with diverse toughening mechanisms, thereby aiding in the design and application of future soft materials.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"194 ","pages":"Article 105926"},"PeriodicalIF":5.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659775","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}

{"title":"Statistical-Physics-Informed Neural Networks (Stat-PINNs): A machine learning strategy for coarse-graining dissipative dynamics","authors":"Shenglin Huang ,&nbsp;Zequn He ,&nbsp;Nicolas Dirr ,&nbsp;Johannes Zimmer ,&nbsp;Celia Reina","doi":"10.1016/j.jmps.2024.105908","DOIUrl":"10.1016/j.jmps.2024.105908","url":null,"abstract":"<div><div>Machine learning, with its remarkable ability for retrieving information and identifying patterns from data, has emerged as a powerful tool for discovering governing equations. It has been increasingly informed by physics, and more recently by thermodynamics, to further uncover the thermodynamic structure underlying the evolution equations, <em>i.e.</em>, the thermodynamic potentials driving the system and the operators governing the kinetics. However, despite its great success, the inverse problem of thermodynamic model discovery from macroscopic data is in many cases non-unique, meaning that multiple pairs of potentials and operators can give rise to the same macroscopic dynamics, which significantly hinders the physical interpretability of the learned models. In this work, we consider the problem of deriving the macroscopic (continuum) equations from microscopic (particle) data, and encode knowledge from statistical mechanics to resolve this non-uniqueness for the first time. The proposed machine learning framework, named as Statistical-Physics-Informed Neural Networks (Stat-PINNs), is here developed for purely dissipative isothermal systems. Interestingly, it only uses data from short-time particle simulations to learn the thermodynamic structure, which can in turn be used to predict long-time macroscopic evolutions. We demonstrate the approach for particle systems with Arrhenius-type interactions, common to a wide range of phenomena, such as defect diffusion in solids, surface absorption, and chemical reactions. Our results from Stat-PINNs can successfully recover the known analytic solution for the case with long-range interactions and discover the hitherto unknown potential and operator governing the short-range interaction cases. We compare our results with direct particle simulations and an analogous approach that solely excludes statistical mechanics, and observe that, in addition to recovering the unique thermodynamic structure, statistical mechanics relations can increase the robustness and predictive capability of the learning strategy.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"194 ","pages":"Article 105908"},"PeriodicalIF":5.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659865","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}

{"title":"ViscoNet: A lightweight FEA surrogate model for polymer nanocomposites viscoelastic response prediction","authors":"Anqi Lin,&nbsp;Richard J․ Sheridan,&nbsp;Bingyin Hu,&nbsp;L. Catherine Brinson","doi":"10.1016/j.jmps.2024.105915","DOIUrl":"10.1016/j.jmps.2024.105915","url":null,"abstract":"<div><div>Polymer-based nanocomposites (PNCs) are formed by dispersing nanoparticles (NPs) within a polymer matrix, which creates polymer interphase regions that drive property enhancement. However, data-driven PNC design is challenging due to limited data. To address the challenge, we present ViscoNet, a surrogate model for finite element analysis (FEA) simulations of PNC viscoelastic (VE) response. ViscoNet leverages pre-training and finetuning to accelerate predicting VE response of a new PNC system. By predicting the entire VE response, ViscoNet surpasses previous scalar-based surrogate models for FEA simulation, offering better fidelity and efficiency. We explore ViscoNet's effectiveness through generalization tasks, both within thermoplastics and from thermoplastics to thermosets, reporting a mean absolute percentage error (MAPE) of &lt; 5 % for rubbery modulus and &lt; 1 % for glassy modulus in all cases and 1.22 % on tan δ peak height prediction. With only 500 FEA simulations for finetuning, ViscoNet can generate over 20k VE responses within 2 min with 1 CPU, compared to 97 days with 4 CPUs via FEA simulations.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"194 ","pages":"Article 105915"},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659776","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}

{"title":"Homogenization of two-dimensional materials integrating monolayer bending and surface layer effects","authors":"Huichao Liu ,&nbsp;Yan Chen ,&nbsp;Wen Wang ,&nbsp;Luqi Liu ,&nbsp;Yilun Liu ,&nbsp;Quanshui Zheng","doi":"10.1016/j.jmps.2024.105911","DOIUrl":"10.1016/j.jmps.2024.105911","url":null,"abstract":"<div><div>Two-dimensional (2D) materials hold great promise for future electronic, optical, thermal devices and beyond, underpinning which the predictability, stability and reliability of their mechanical behaviors are the fundamental prerequisites. Despite this, due to the layered crystal lattice structure, extremely high anisotropy and the independent deformation mechanism of out-of-plane bending, the proper homogenization for such materials still faces challenge. That is because the monolayer bending is of independent deformation mechanism distinct from the traditional bulk deformation which thereby brings couple stress to the bulk 2D materials, while the different interlayer constraints of bulk and surface layers bring surface layer effect. In this paper, by considering the two effects, a continuum mechanics framework for extremely anisotropic 2D materials (CM2D) is proposed, without necessities of ad hoc experiments for the unclassical parameters. Under the framework of the CM2D, beam-like deformation, plate-like deformation and indentation of 2D materials are studied to showcase its ability and applicability. An analytical expression of the effective bending rigidity is derived, which can be characterized by several dimensionless parameters. It is found that the overall bending deformations of 2D materials are controlled by the competition between the intralayer deformation mode and the interlayer shear deformation mode. Besides, the size-dependent modulus is also identified on the indentation of 2D materials at the pure elastic deformation regime, distinct from the size effect caused by plasticity. In addition, we discussed the effects of monolayer bending and surface layer on the mechanical behaviors of 2D materials. Our work not only provides guidance for the studies and applications of 2D materials, but also serves as a good example with well-defined physical meanings for the strain gradient, high-order moduli and couple stress in high-order continuum mechanics theories.</div></div>","PeriodicalId":17331,"journal":{"name":"Journal of The Mechanics and Physics of Solids","volume":"194 ","pages":"Article 105911"},"PeriodicalIF":5.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659773","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}