Cell and Tissue Biology

Abstract Liquid metal interfacial flows occur in the fields of nuclear fusion and electromagnetic metallurgy. Due to the electrically conductive characteristics of the liquid metal, the presence of magnetic fields in these application scenarios has significant impacts on the interfacial flow behaviors. Then typical interfacial flows under the influence of magnetic fields, such as the free surface liquid metal flow, the liquid metal droplet impacting problems, and the bubble motion in liquid metal, are discussed in the present review. We comprehensively illustrate the flow characteristics of free surface liquid metal flow, the spreading of liquid metal droplets impacting onto solid or liquid surfaces, outcomes of collisions between metal droplets, and bubble dynamics in liquid metal, under the influence of magnetic fields along different directions. Meanwhile, we briefly review the current concepts of liquid metal free surface flow for the plasma facing comonents (PFCs) in fusion reactors and finally make a summary for the open questions related to the fundamental research and industrial applications of interfacial flow magnetohydrodynamics in the future.

Abstract With the increasing miniaturization of mechanical systems and the prevalence of rough surfaces in engineering applications, understanding and accurately characterizing the contact response at small scales has become crucial. This review article provides a comprehensive analysis of two significant aspects in the field of contact mechanics: the size-dependent response of single asperity due to strain gradients and surface effects, and the contact behavior of rough surfaces. The former forms the foundation for the latter analysis, as real surfaces are inherently rough and contact occurs at discrete asperities. At the microscale, strain gradients play a dominant role, as classical continuum mechanics fails to account for the intrinsic material length. Further downscaling to the nanoscale highlights the importance of surface effects due to the large surface-to-bulk ratio. The first section examines these distinct size-dependent effects and their implications for contact mechanics across different scales. The second section further focuses on the contact of rough surfaces, highlighting incremental contact models, contact behavior at large contact fraction where asperity interactions are significant, adhesive rough contact in soft materials, and experimental advances that improve the understanding and validation of these models. Together, these two topics underscore the need for refined theoretical and experimental approaches to accurately model and predict the contact behavior at small scales and with realistic multi-scale roughness.

Abstract In recent decades, origami has transitioned from a traditional art form into a systematic field of scientific inquiry, characterized by attributes such as high foldability, lightweight frameworks, diverse deformation modes, and limited degrees-of-freedom. Despite the abundant literature on smart materials, actuation methods, design principles, and manufacturing techniques, comprehensive reviews focusing on the mechanical properties of origami-inspired structures are relatively rare and unsystematic. This review aims to fill this void by analyzing and summarizing the significant studies conducted on the mechanical properties of origami-inspired structures from 2013 to 2023. We begin with an overview that includes essential definitions of origami, classical origami patterns, and their associated tessellated or stacked structures. Following this, we delve into the principal dynamic modeling method for origami and conduct an in-depth analysis of the key mechanical properties of origami-inspired structures. These properties include tunable stiffness, bistability and multistability, metamechanical properties demonstrated by origami-based metamaterials, and bio-inspired mechanical properties. Finally, we conclude with a comprehensive summary that discusses the current challenges and future directions in the field of origami-inspired structures. Our review provides a thorough synthesis of both the mechanical properties and practical applications of origami-inspired structures, aiming to serve as a reference and stimulate further research.

Abstract In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilities and the renewed advances of machine learning. In this context, new methods such as the virtual fields method and physics-informed neural networks have been developed as alternatives to the already established least-squares and finite element-based approaches. Moreover, model discovery problems are emerging and can also be addressed in a parameter estimation framework. These developments call for a new unified perspective, which is able to cover both traditional parameter estimation methods and novel approaches in which the state variables or the model structure itself are inferred as well. Adopting concepts discussed in the inverse problems community, we distinguish between all-at-once and reduced approaches. With this general framework, we are able to structure a large portion of the literature on parameter estimation in computational mechanics -- and we can identify combinations that have not yet been addressed, two of which are proposed in this paper. We also discuss statistical approaches to quantify the uncertainty related to the estimated parameters, and we propose a novel two-step procedure for identification of complex material models based on both frequentist and Bayesian principles. Finally, we illustrate and compare several of the aforementioned methods with mechanical benchmarks based on synthetic and experimental data.

Abstract Magnetoelectroelastic (MEE) materials and structures have been extensively applied in MEE devices such as sensors and transducers, microelectromechanical systems (MEMS) and smart structures. In order to assess the strength and durability of such materials and structures, exhaustive theoretical and numerical investigations have been conducted over the past two decades. The main purpose of this paper is to present a state-of-the-art review and a critical discussion on the research of the MEE fracture mechanics. Following an introduction, the basic theory of the fracture mechanics in the linear magnetoelectroelasticity is explained with special emphasis on the constitutive equations related to different fracture modes, magnetoelectrical (ME) crack-face boundary conditions and fracture parameters for 2D plane problems. Then, the state of the art of the research on the fracture mechanics of the MEE materials and structures is reviewed and summarized, including 2D anti-plane and in-plane as well as 3D analyses under both static and dynamic loadings. The ME effects on the fracture parameters are revealed and discussed. Moreover, numerical investigations based on the finite element method (FEM), boundary element method (BEM), meshless methods and other novel methods are also reviewed for MEE fracture problems. Finally, some conclusions are drawn with several prospects to open questions and demanding future research topics. In particular, experimental observations are urgently needed to verify the validity of the theoretical predictions of the various fracture criteria. Another great challenge is to tackle the non-linear phenomena and domain switching in the fracture process zone.

Abstract The investigation of statistical scaling in localization-induced failures dates back to da Vinci's speculation on the length effect on rope strength in 1500 s. The early mathematical description of statistical scaling emerged with the birth of the extreme value statistics. The most commonly known mathematical model for statistical scaling is the Weibull size effect, which is a direct consequence of the infinite weakest-link model. However, abundant experimental observations on various localization-induced failures have shown that the Weibull size effect is inadequate. Over the last two decades, two mathematical models were developed to describe the statistical size effect in localization-induced failures. One is the finite weakest-link model, in which the random structural resistance is expressed as the minimum of a set of independent discrete random variables. The other is the level excursion model, a continuum description of the finite weakest-link model, in which the structural failure probability is calculated as the probability of the upcrossing of a random field over a barrier. This paper reviews the mathematical formulation of these two models and their applications to various engineering problems including the strength distributions of quasi-brittle structures, failure statistics of micro-electromechanical systems (MEMS) devices, breakdown statistics of high– k gate dielectrics, and probability distribution of buckling pressure of spherical shells containing random geometric imperfections. In addition, the implications of statistical scaling for the stochastic finite element simulations and the reliability-based structural design are discussed. In particular, the recent development of the size-dependent safety factors is reviewed.

Abstract This paper contains review of the theory and applications of nonlinear normal modes, which are developed during last decade. This review has more than 200 references. It is a continuation of two previous review papers of the same authors (Mikhlin Y.V., Avramov K.V.: Nonlinear normal modes for vibrating mechanical systems. Review of Theoretical Developments. Appl. Mech. Rev. 63, 060802 (2010); Avramov, K.V., Mikhlin, Yu.V.: Review of applications of nonlinear normal modes for vibrating mechanical systems. Appl. Mech. Rev. 65, 020801 (2013)). The following theoretical issues of nonlinear normal modes are treated: basic concepts and definitions; application of the normal forms theory for nonlinear modes construction; nonlinear modes in finite degrees of freedom systems; resonances and bifurcations; reduced-order modelling; nonlinear modes in stochastic dynamical systems; numerical methods; identification of mechanical systems using nonlinear modes. The following applied issues of this theory are treated in this review: experimental measurement of nonlinear modes; nonlinear modes in continuous systems; engineering applications (aerospace engineering, power engineering, piecewise-linear systems and structures with dry friction); nonlinear modes in nanostructures and physical systems; targeted energy transfer and absorption problem.

Abstract Indentation measurement has emerged as a widely adapted technique for elucidating the mechanical properties of soft hydrated materials. These materials, encompassing gels, cells, and biological tissues, possess pivotal mechanical characteristics crucial for a myriad of applications across engineering and biological realms. From engineering endeavors to biological processes linked to both normal physiological activity and pathological conditions, understanding the mechanical behavior of soft hydrated materials is paramount. The indentation method is particularly suitable for accessing the mechanical properties of these materials as it offers the ability to conduct assessments in liquid environment across diverse length and time scales with minimal sample preparation. Nonetheless, understanding the physical principles underpinning indentation testing and the corresponding contact mechanics theories, making judicious choices regarding indentation testing methods and associated experimental parameters, and accurately interpreting the experimental results are challenging tasks. In this review, we delve into the methodology and applications of indentation in assessing the mechanical properties of soft hydrated materials, spanning elastic, viscoelastic, poroelastic, coupled viscoporoelastic, and adhesion properties, as well as fracture toughness. Each category is accomplished by the theoretical models elucidating underlying physics, followed by ensuring discussions on experimental setup requirements. Furthermore, we consolidate recent advancements in indentation measurements for soft hydrated materials highlighting its multifaceted applications. Looking forward, we offer insights into the future trajectory of the indentation method on soft hydrated materials and the potential applications. This comprehensive review aims to furnish readers with a profound understanding of indentation techniques and a pragmatic roadmap of characterizing the mechanical properties of soft hydrated materials.

Abstract The integration of ceramic matrix composites (CMCs) into safety-critical applications, such as turbine engines and aerospace structures, necessitates a sound understanding of their expected damage evolution under in-service conditions and real-time health-monitoring methods to assess their damage state. The measurement of acoustic emissions (AEs), the transient elastic waves emitted during damage formation, offers an enhanced capability for evaluating damage evolution and structural health in CMCs due to its high sensitivity, accurate temporal resolution, and relative ease of use compared to other nondestructive evaluation (NDE) techniques. Recent advances in numerical simulation methods and data-driven model development, in combination with improved multimodal experimental characterization methods and sensor hardware, are rapidly advancing AE to a mature technique for damage quantification. This review discusses the fundamental principles of acoustic emissions, provides practical guidelines on their experimental characterization and analysis, and offers perspectives on the current state-of-the-art.

Abstract For centuries, researchers have sought out ways to connect disparate areas of knowledge. While early scholars (Galileo, da Vinci, etc.) were experts across fields, specialization has taken hold later. With the advent of Artificial Intelligence, we can now explore relationships across areas (e.g., mechanics-biology) or disparate domains (e.g., failure mechanics-art). To achieve this, we use a fine-tuned Large Language Model (LLM), here for a subset of knowledge in multiscale materials failure. The approach includes the use of a general-purpose LLM to distill question-answer pairs from raw sources followed by LLM fine-tuning. The resulting MechGPT LLM foundation model is used in a series of computational experiments to explore its capacity for knowledge retrieval, various language tasks, hypothesis generation, and connecting knowledge across disparate areas. While the model has some ability to recall knowledge from training, we find that LLMs are particularly useful to extract structural insights through Ontological Knowledge Graphs. These interpretable graph structures provide explanatory insights, frameworks for new research questions, and visual representations of knowledge that also can be used in retrieval-augmented generation. Three versions of MechGPT are discussed, featuring different sizes from 13 billion to 70 billion parameters, and reaching context lengths of more than 10,000 tokens. This provides ample capacity for sophisticated retrieval augmented strategies, as well as agent-based modeling where multiple LLMs interact collaboratively and/or adversarially, the incorporation of new data from the literature or web searches, as well as multimodality.

Abstract Accurate multi-physics modelling is necessary to simulate and predict the long-term behaviour of subsurface porous rocks. Despite decades of modelling subsurface multi-physics processes in porous rocks, there are still considerable uncertainties and challenges remaining partly because of the way the constitutive equations describing such processes are derived (thermodynamically or phenomenologically) and treated (continuum or discrete) regardless of the way they are solved (e.g. finite-element or finite-volume methods). We review here continuum multi-physics models covering aspects of poromechanics, chemo-poromechanics, thermo-poromechanics, and thermo-chemo-poromechanics. We focus on models that are derived based on thermodynamics to signify the importance of such a basis and discuss the limitations of the phenomenological models and how thermodynamics-based modelling can overcome such limitations. The review highlights that the experimental determination of thermodynamics response coefficients (coupling or constitutive coefficients) and field applicability of the developed thermodynamics models are significant research gaps to be addressed. Verification and validation of the constitutive models, preferably through physical experiments, is yet to be comprehensively realized which is further discussed in this review. The review also shows the versatility of the multi-physics models to address issues from shale gas production to CO2 sequestration and energy storage and highlights the need for inclusion of thermodynamically consistent damage mechanics, coupling of chemical and mechanical damage and two-phase fluid flow in multi-physics models.

Abstract Many animals in nature travel in groups either for protection, survival, or endurance. Among these, certain species do so under the burden of aero/hydrodynamic loads, which incites questions as to the significance of the multibody fluid-mediated interactions that are inherent to collective flying/swimming. Prime examples of such creatures are fish, which are commonly seen traveling in highly organized groups of large numbers. Indeed, over the years, there have been numerous attempts to examine hydrodynamic interactions among self-propelled fish-like swimmers. Though many have studied this phenomenon, their motivations have varied from understanding animal behavior to extracting universal fluid dynamical principles and transplanting them into engineering applications. The approaches utilized to carry out these investigations include theoretical and computational analyses, field observations, and experiments using various abstractions of biological fish. Here, we compile representative investigations focused on the collective hydrodynamics of fish-like swimmers. The selected body of works are reviewed in the context of their methodologies and findings, so as to draw parallels, contrast differences, and highlight open questions. Overall, the results of the surveyed studies provide foundational insights into the conditions (such as the relative positioning and synchronization between the members, as well as their swimming kinematics and speed) under which hydrodynamic interactions can lead to efficiency gains and/or group cohesion in two- and three-dimensional scenarios. They also shed some light on the mechanisms responsible for such energetic and stability enhancements in the context of wake-body, wake-wake, and body-body interactions.

Abstract The primary objective of this research is to present a comprehensive discussion on the fundamental concepts, innovations, and applications of triboelectric nanogenerators. It describes the various triboelectric nanogenerators' modes and mechanisms. Here, materials and associated development that exhibit triboelectric properties are also examined. Additionally, various triboelectric nanogenerator applications have been discussed. Triboelectric nanogenerators, a revolutionary power generation technology, were introduced in 2012 and are characterized as devices that efficiently generate electrical energy from kinetic energy in the environment. The development of triboelectric nanogenerators has advanced significantly since the invention of this innovative power-generation technology. Moreover, in order to provide a deeper understanding of the technology, the modes include Freestanding Triboelectric-Layer, Single-Electrode, Lateral Sliding, and Vertical Contact-Separation Modes as well as their operating mechanism have been thoroughly examined. Likewise, this study describes and offers concepts for future research on the important applications of TENGs, including high voltage power supply, blue energy, self-power sensors, and micro/nano energy. In conclusion, triboelectric nanogenerator is a crucial and alluring technology with the benefits of low cost, simple structure, easy fabrication, high efficiency, and relatively high output energy. Since a broad range of materials can be adopted, scientists can apply this cutting-edge technology in novel ways. For a more comprehensive understanding of this innovative and ground-breaking technology, numerous fundamental scientific considerations and limitations are also addressed.

Abstract For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel artificial materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is growing exponentially, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano- and micromechanics, architected materials, and two-dimensional materials. Finally, we highlight some current challenges of applying ML to multimodality and multifidelity experimental datasets, quantifying the uncertainty of ML predictions, and proposing several future research directions. This review aims to provide valuable insights into the use of ML methods and a variety of examples for researchers in solid mechanics to integrate into their experiments.

Abstract The ability to predict and characterize bifurcations from the onset of unsteadiness to the transition to turbulence is of critical importance in both academic and industrial applications. Numerous tools from dynamical system theory can be employed for that purpose. In this review, we focus on the practical computation and stability analyses of steady and time-periodic solutions with a particular emphasis on very high-dimensional systems such as those resulting from the discrete Navier-Stokes equations. In addition to a didactically concise theoretical framework, we introduce nekStab, an open source and user-friendly toolbox dedicated to such analyses using the spectral element solver Nek5000. Relying on Krylov methods and a time-stepper formulation, nekStab inherits the flexibility and high performance capabilities of Nek5000 and can be used to study the stability properties of flows in complex three-dimensional geometries. The performances and accuracy of nekStab are presented on the basis of standard benchmarks from the literature. For the sake of pedagogy and clarity, most of the algorithms implemented in nekStab are presented herein using Python pseudocode. Because of its flexibility and domain-agnostic nature, the methodology presented in this work can be applied to develop similar toolboxes for other solvers, most importantly outside the field of fluid dynamics.