(Invited) Multi-Scale Battery Modelling: Understanding Coupled Electrochemical and Mechanical Effects

A multiscale modeling of damage accumulation and permeability variation in shale rocks under mechanical loading

This special section of Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, on multiscale modeling in life and materials sciences, is based on a summer program with the same theme, held August 4--30, 2003, at the Universita della Svizzera Italiana in Lugano, Switzerland, and organized by the ETH Zurich Computational Laboratory (CoLab). This workshop emphasized that multiscale modeling and simulation can serve as a commonscientific language enabling cross-fertilization across disciplines and the definition of new scientific frontiers. Materials science was one of the first disciplines that emphasized the need for novel multiscaling approaches bridging continuum and atomistic models. In life sciences, the rapid growth of the role played by computational methods has led to the early realization that a multiscaling approach is necessary to tackle inherently complex phenomena involving cells, organisms, and ecosystems. This summer program provided the opportunity to scientists from these different scientific fields to exchange ideas and to pursue research in an interdisciplinary fashion using multiscale computational methods as a common language. These interactions demonstrated that research in multiscaling is a fertile breeding ground for the development of novel powerful computational tools for domains where computing is established as a tool for scientific inquiry and for the dissemination of this know-how to disciplines for which computation is an emerging form of scientific investigation. This special section of MMS is a representative sample of theoretical and computational approaches to multiscale modeling and simulation in materials and life sciences. The papers discuss multiscale coupling of physical models and time/space multiresolution techniques. The problem of coupling models across space and time scales is a fundamental issue in multiscale modeling and simulation. Multiscale approaches often require the rigorous integration of diverse physical models, such as the case of blood flow in the circulatory system (Fernandez, Milisic}, and Quarteroni). The multiscale integration of diverse physical models in nanoscale magnetic systems involves the proper coupling of quantum effects in classical descriptions (Wessel, Trebst, and Troyer). Multiscale simulations of materials with heterogeneous properties are tackled using asymptotic expansions for the case of conducting inclusions embedded in a homogeneous matrix phase (Ben Hassen and Bonnetier). A broad class of multiscale problems involves the description of macroscale systems using only the information available by suitable microscopic models. This technique is demonstrated in simulating the evolution of parabolic homogenization problems with nonlinear reactions, using as a microscopic model a partial differential equation with rapidly oscillating coefficients (Samaey, Roose, and Kevrekidis). A related work extends this concept to problems with a wide separation in time scales. Using as an application a model of bacterial chemotaxis it is shown that projective time integration of coarse variables can be carried out in time scales that are long compared to those of the microscopic dynamics (Setayeshgar, Gear, Othmer, and Kevrekidis). In problems with time-dependent, large space scale variations a new class of multiresolution particle methods enhances their inherent adaptivity and presents an alternative to grid-based methods (Bergdorf, Cottet, and Koumoutsakos). Future research in multiscale modeling would continue to rely on synergies across disciplines for the development of novel computational methods, the integration of physical models, and the formulation of new challenges. This principle will continue to guide future workshops in multiscale modeling and simulations at the CoLab and, we believe, an ever-increasing number of scientific institutions. The editors in charge would like to credit, for their hard work and dedication, those editors who helped handle the peer-review process for papers submitted to this special section. Thanks to Emily Carter, Wolfgang Hackbusch, Robert Kohn, Hans Christian Ottinger, Olivier Pironneau, and John Willis. We also wish to thank SIAM Publications Manager Mitch Chernoff for his invaluable advice and help in preparing this section.

Influence of grit particles characteristics on the abrasive wear micro-mechanisms of NbC-Ni and WC-Co hard materials

Particle fracture due to diffusion-induced stress (DIS) in electrodes is a key factor for lithium-ion battery (LIB) failure. Among many ways to minimize DIS, optimization of particle size and C-rates using state of charge (SOC) dependent varying properties can be a noble approach. Herein, a comprehensive multiscale modeling approach has been proposed to optimize the particle size by studying the DIS in hard carbon (HC) particles as the potential anode materials for high-energy LIBs. To accomplish this, density functional theory (DFT) was used to calculate the SOC dependent coefficient of volume expansion (CVE). Similarly, SOC dependent diffusivity and elastic modulus are calculated via molecular dynamics (MD) simulations. These results are transferred to a continuum model to examine the evolution of concentrations and DISs in hard carbon particles of radius 100-1000 nm lithiated at various C-rates (1C, 2C, 5C, and 10C). Our model successfully incorporates the variation of Li+ diffusivity and elastic modulus with SOC and tracks stress relaxation and volume expansion in the particles during lithiation. An optimized particle size has been recommended for hard carbon, considering both stresses for different C-rates. Our study provides a more realistic multiscale modeling framework for optimizing the DIS and can act as a guiding method towards achieving an optimum particle size so that capacity fading due to cracking can be avoided.

With the development of data mining technology, the analysis of event-related potential (ERP) data has evolved from statistical analysis of time-domain features to data-driven techniques based on supervised and unsupervised learning. However, there are still many challenges in understanding the relationship between ERP components and the representation of familiar and unfamiliar faces. To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/unfamiliar face recognition using a multi-scale model. The model employs a variable-length filter generator to dynamically determine the optimal filter length for time-series samples, thereby capturing features at different time scales. Comparative experiments are conducted to evaluate the model’s performance against SOTA models. The results demonstrate that our model achieves impressive outcomes, with a balanced accuracy rate of 93.20% and an F1 score of 88.54%, outperforming the methods used for comparison. The ERP data extracted from different time regions in the model can also provide data-driven technical support for research based on the representation of different ERP components.

Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high‐performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

Permeable pavement is easy to be blocked, which significantly affects the functions of drainage and skid resistance. Relevant researches lack void clogging and cleaning for PAC with different air voids and nominal maximum particle sizes. This paper designed seven kinds of PAC specimens to study PAC's clogging characteristics and cleaning methods. Specifically, the permeability coefficient decay of PAC caused by clogging was tested. Then, a novel test called fluorescence tracing method was proposed to evaluate the clogging depth and area. Afterward, high-pressure washing, vacuum cleaning, and the combination of high-pressure washing and vacuum cleaning (H&V) were used to study void recovery characteristics. Results showed that the critical clogging particle size of PAC-13 with 16% air voids and PAC-10 with 20% air voids were 0.15-0.3 mm; the critical clogging particle size of PAC-13 with 18% air voids was 0.3-0.6 mm; the critical clogging particle size of PAC-13 and PAC-16 with air voids of 20%, 22%, 24% were 0.6-1.18 mm. Furthermore, the clogging depths of PAC-13 didn't exceed 28 mm, and the proportions of the clogging area didn’t exceed 37%. The average recovery rates of vacuum cleaning and H&V were more than 70%, obviously better than that of high-pressure washing.

A three dimensional adaptive multiscale method for crack growth in Silicon

A multi-scale damage model for fatigue accumulation due to short cracks nucleation and growth

Electrochemical–mechanical multi-scale model and validation with thickness change measurements in prismatic lithium-ion batteries

A wavelet-based multiscale methodology is presented that naturally addresses time scaling in addition to spatial scaling. The method combines recently developed atomistic-continuum models and wavelet analysis. An atomistic one-dimensional harmonic crystal is coupled to a one-dimensional continuum. The methodology is illustrated through analysis of the dispersion relation, which is highly dispersive at small spatial scales and, as usual, nondispersive at large (continuum) scales. It is feasible to obtain the complete dispersion relation through the combination of the atomistic and the continuum analyses. Wavelet analysis in this work is not only used for bridging the atomistic and continuum scales but also for efficiently extracting the dispersion relation from the solution of wave propagation problems.

Analytical descriptions of crater growth and numerical calculations of aerodynamic drag have been combined to evaluate the possible effects of drag on impact crater ejecta emplacement on Mars. Below a critical particle size, ejecta deposition is severely restricted in range. This critical particle size increases with increasing crater size because ejection velocity at the same relative position within a crater increases with size and because aerodynamic deceleration increases as the square of the velocity. Numerical models of ejecta trajectories in the current Martian atmosphere under hydrostatic equilibrium reveal that craters of 1, 5, 10, and 20 km exhibit critical particle diameters of 0.4–0.8, 2–4, 5–10, and 10–20 cm. Ejecta approaching the critical particle size may impact within a crater radius of the excavation crater rim. Once velocities are reduced sufficiently, ejecta paths can be modified by turbulence and winds, thereby setting up conditions for ejecta flow from the rim region. However, ejecta larger than the critical particle size are relatively undecelerated and form secondary impact craters subsequently modified by the later arriving decelerated ejecta cloud. Consequently, ejecta emplacement will be multiphased, but the importance of this process depends on the distribution of ejecta sizes.

Scale bridging damage model for quasi-brittle metals informed with crack evolution statistics

A two-scale model for predicting the multiple crack growth in viscoelastic solids due to an impact is presented. The cracks are considered only at the local scale through the use of a micromechanical viscoelastic cohesive zone model. The multiscale model has been implemented in a finite-element code. In order to minimize the computation time, the local finite-element meshes are solved in parallel by multiple processors. An example problem is given in order to demonstrate the capabilities of the model.

The interaction of fusion reactor plasma with the material of the first wall involves a complex multitude of interlinked physical and chemical effects. Hence, modern theoretical treatment of it relies to a large extent on multiscale modelling, i.e. using different kinds of simulation approaches suitable for different length and time scales in connection with each other. In this review article, we overview briefly the physics and chemistry of plasma–wall interactions in tokamak-like fusion reactors, and present some of the most commonly used material simulation approaches relevant for the topic. We also give summaries of recent multiscale modelling studies of the effects of fusion plasma on the modification of the materials of the first wall, especially on swift chemical sputtering, mixed material formation and hydrogen isotope retention in tungsten.