Abstract Indentation measurement has emerged as a crucial 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, adhesion properties, and 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 took 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 for extracting 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 × 109 to 70 × 109 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 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 Accurate multiphysics modeling is necessary to simulate and predict the long-term behavior of subsurface porous rocks. Despite decades of modeling subsurface multiphysics 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 multiphysics 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 modeling 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 multiphysics 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 multiphysics 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.

AbstractFor 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.

AbstractThe major aim of this study is to provide a broad review of the fundamental ideas, progress, and utilization of triboelectric nanogenerators (TENGs). The modes and operations of numerous triboelectric nanogenerator configurations along with applications and materials are also discussed. Triboelectric nanogenerators, a ground-breaking power production technology, were unveiled in 2012 and classified as one of the most effective generators to convert unused mechanical energy into electrical energy to run a wide range of devices. Triboelectric nanogenerators have made significant progress since the creation of this novel power-generation technology. The operating principles of various modes, such as freestanding triboelectric-layer, single-electrode, lateral sliding, and vertical contact-separation have also been carefully investigated in order to give readers a deeper understanding of the technology. The key applications of TENGs, such as high voltage power supply, blue energy, self-power sensors, and micro/nano-energy, are also described in this work along with concepts for further research. As a result, triboelectric nanogenerators are very important and attractive technology with advantages of low cost, straightforward construction, simple fabrication, high efficiency, and relatively high output performance. Wide range of material choice allows researchers to use the technology in many configurations with multiple applications. Numerous scientific modeling and analysis are also reviewed for a more solid understanding of this revolutionary and unique technology.

Abstract This paper represents a welcome synthesis of the results obtained by the authors over more than a decade. The reason why such an approach is perfectly justified is found in the novelty of the control techniques of decelerated swirling flows from the conical diffuser of hydraulic turbines. The results presented in this paper refer strictly to the new passive control techniques of the swirling flows instabilities from the conical diffuser of hydraulic turbines. Although the results of these new techniques have been disseminated in various papers, it is difficult to outline an overview from a collection of articles. In addition, a lot of valuable information about modern experimental and numerical investigations is not found in articles that usually distill only the most significant results. Therefore, the present paper achieves a welcome unitary synthesis, useful to specialists in the field of turbomachine hydrodynamics. The reluctance of the turbine manufacturers on active control techniques that use external/additional energy sources led us to the choice of passive control techniques review, especially the ones developed in the last years. The first part of the paper analyzes the specialized literature that includes a variety of passive solutions for mitigating self-induced instabilities of decelerated swirling flow downstream of hydraulic turbines. Such inherent instabilities manifest intensely at far from optimal operating regimes and represent one of the challenges of modern hydraulic turbines. The mitigation of these instabilities is an open problem, so far there are no unanimously accepted technical solutions implemented on prototype turbines. The second part of the paper includes detailed investigations on axial water injection with flow-feedback, but also more recent approaches using adjustable diaphragm in the conical diffuser.

AbstractIn fluid dynamics, predicting and characterizing bifurcations, from the onset of unsteadiness to the transition to turbulence, is of critical importance for both academic and industrial applications. Different tools from dynamical systems theory can be used for this purpose. In this review, we present a concise theoretical and numerical framework focusing on practical aspects of the computation and stability analyses of steady and time-periodic solutions, with emphasis on high-dimensional systems such as those arising from the spatial discretization of the Navier–Stokes equations. Using a matrix-free approach based on Krylov methods, we extend the capabilities of the open-source high-performance spectral element-based time-stepper Nek5000. The numerical methods discussed are implemented in nekStab, an open-source and user-friendly add-on toolbox dedicated to the study of stability properties of flows in complex three-dimensional geometries. The performance and accuracy of the methods are illustrated and examined using standard benchmarks from the fluid mechanics literature. Thanks to 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 mechanics.