Structural Mechanics Research Articles

Optimization of cutout characteristics in AISI 1045 steel plates for high-cycle fatigue life extension

Crack growth is a pivotal concern in structural engineering, directly impacting structures’ stability, quality, and overall lifespan. This study addresses this challenge by investigating structural optimization strategies to mitigate fatigue crack growth (FCG) by strategically incorporating stop-drilled cutouts near the crack tip. The research introduces three optimization problems, focusing on determining the optimal cutout position with a fixed radius, the optimal cutout radius with a fixed position, and the optimal cutout shape with a constant position for the cutout’s center. Experimental assessments are conducted on an edge-cracked AISI 1045 steel plate to evaluate the effectiveness of these optimization strategies in enhancing fatigue life. Employing the Extended Finite Element Method (XFEM) for numerical simulations and Particle Swarm Optimization (PSO) for the optimization process, the findings reveal that strategically positioned cutouts substantially reduce the crack growth rate. Optimal cutout placement significantly improves fatigue life, demonstrating an approximate 287% increase. The analysis affirms the accuracy of the numerical model in predicting fatigue life and crack growth paths, validated through comparison with experimental data. In summary, this research underscores the effectiveness of optimization techniques in mitigating fatigue crack growth, providing valuable insights for structural integrity enhancement in structural mechanics.

Recognition of local fiber orientation state in prepreg platelet molded composites via deep learning

This paper presents a novel deep learning approach for reconstruction of local through-the-thickness fiber orientation distribution (FOD) in discontinuous long-fiber, prepreg-platelet molded composites (PPMC). The thermal-residual strains on composite surfaces are used as inputs to train a fully convolutional neural network, named U-Net, to predict spatially varying, local FOD. High fidelity synthetic data was generated via computational simulation of PPMC with stochastic material orientation state and was used for training the deep learning model. The proposed U-Net model allowed for rapid recognition of PPMC morphology by solving the inverse structural mechanics problem of determining the average fiber orientation through the composite thickness based on the provided surface strain measurements. Upon training and validation, the U-Net deep learning model was deployed to rapidly predict complex distributions of the local through-the-thickness FOD in PPMC.

Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials.

Numerical solutions to partial differential equations (PDEs) are instrumental for material structural design where extensive data screening is needed. However, traditional numerical methods demand significant computational resources, highlighting the need for innovative optimization algorithms to streamline design exploration. Direct gradient-based optimization algorithms, while effective, rely on design initialization and require complex, problem-specific sensitivity derivations. The advent of machine learning offers a promising alternative to handling large parameter spaces. To further mitigate data dependency, researchers have developed physics-informed neural networks (PINNs) to learn directly from PDEs. However, the intrinsic continuity requirement of PINNs restricts their application in structural mechanics problems, especially for composite materials. Our work addresses this discontinuity issue by substituting the PDE residual with a weak formulation in the physics-informed training process. The proposed approach is exemplified in modeling digital materials, which are mathematical representations of complex composites that possess extreme structural discontinuity. This article also introduces an interactive process that integrates physics-informed loss with design objectives, eliminating the need for pretrained surrogate models or analytical sensitivity derivations. The results demonstrate that our approach can preserve the physical accuracy in data-free material surrogate modeling but also accelerates the direct optimization process without model pretraining.

Development of 6-way CFD-DEM-FEM momentum coupling interface using partitioned coupling approach

Fluid-particle-structure interactions (FPSI) govern a wide range of natural and engineering phenomena, from landslides to erosion in abrasive water jet cutting nozzles. Despite the importance of studying FPSI, existing numerical frameworks often simplify or neglect certain physics, limiting their applicability. This work introduces a novel 6-way CFD-DEM-FEM momentum coupling for FPSI using a partitioned coupling approach, providing a flexible and adaptable solution.Our prototype uses the preCICE coupling library to couple three numerical solvers: OpenFOAM for fluid dynamics, eXtended Discrete Element Method (XDEM) for particle motion, and CalculiX for structural mechanics. The coupling approach extends existing adapters and introduces a novel XDEM preCICE adapter, allowing data exchange over surface and volumetric meshes.Numerical experiments successfully demonstrate the 6-way coupling, showcasing fluid-structure interactions and particle dynamics. The versatility of the partitioned coupling approach is highlighted, allowing the interchangeability of different single-physics solvers and facilitating the study of complex FPSI phenomena.This article offers a thorough description of the methodology, coupling strategies, and detailed results, offering insights into the advantages and disadvantages of the proposed approach. This work lays the groundwork for a scalable and customizable FPSI simulation framework with a wide range of applications.

Gas solubility enhancement and hydrogen bond recombination regulated by terahertz electromagnetic field for rapid formation of gas hydrates

The low solubility of methane in water and the formation of hydrate film at methane-water interface restrict interfacial mass transfer between methane and water, thereby impeding the rapid formation of hydrate. Herein, THz electromagnetic field (TEMF) is introduced to destroy hydrogen bonds through resonance with water molecules for decreasing methane-water interfacial tension (IFT) and improving methane solubility in water. Thereafter, TEMF is removed to restore hydrogen bonding interaction for hydrate nucleation within bulk-phase water rather than at methane-water interface, thereby accelerating hydrate formation. The results show that a TEMF at 15.8 THz can increase methane solubility by 50-fold and provide more nucleation sites for hydrate formation by lowering the IFT by 15-fold. Furthermore, the hydrate nucleation follows “blob” nucleation at low solubility and Local Structuring Mechanism nucleation at high solubility. The higher solubility of methane shortens the induction period of hydrate nucleation form 58.5 ns to 29 ns. Particularly, small 512 cages form preferentially regardless of nucleation pathway. The underlying mechanism for TEMF-induced the acceleration of hydrate formation is attributed to the transition of water phase from a disordered to an ordered structure. This transition arises from the resonance of the TEMF with water molecules, disrupting the hydrogen bond between water molecules and providing sufficient driving force for their rearrangement after removing the TEMF. In rearrangement process, weakly hydrogen-bond interactions correspondingly are converted into strongly hydrogen-bond ones. These findings are expected to improve the understanding of hydrate formation mechanism and promote the applications of terahertz technology in gas hydrates.

Physics-based reduced order modeling for uncertainty quantification of guided wave propagation using Bayesian optimization

Guided wave propagation (GWP) is commonly employed for the design of SHM systems. However, GWP is sensitive to variations in the material properties, often leading to false alarms. To address this issue, uncertainty quantification (UQ) is employed to improve the reliability of the predictions. Computational structural mechanics is a useful tool for the simulation of GWP. Even so, the application of UQ methods requires numerous solutions, while large-scale, transient GWP simulations increase the computational cost. In this paper, we propose a machine learning (ML)-based reduced order model (ROM), annotated as BO-ML-ROM, to decrease the computational time of the GWP simulation. The ROM is integrated with a Bayesian optimization (BO) framework to adaptively sample the data for the ROM training. The results showed that BO outperforms one-shot sampling methods, both in terms of accuracy and speed. The developed ROM, which is orders of magnitude faster than the original solver, is then applied for forward uncertainty quantification of the GWP in an aluminum plate and a composite panel under varying material properties. The UQ analysis reveals the higher impact of the uncertainties in the wave reflections. Furthermore, to compute the influence of the material properties on the predictions, a sensitivity analysis (SA) is performed using the variance-based Sobol’ indices and the Shapley additive explanations, where it is shown that the uncertainties in the elastic properties have a varying influence across time. The predicted results reveal the efficiency of BO-ML-ROM for the simulation of the GWP and demonstrate its utility for UQ and SA.

The axially-loaded behaviours of Schwarz Primitive (SP)-based structures: An experimental and DEM study

This study delves into the axial responses of Schwarz Primitive (SP) structures. Emphasising on bearing capacity, force distribution, cracking phenomena, load-displacement curve and energy absorption. Discrete element method (DEM) is adopted to analyse the axial load-bearing behaviour and fracture mechanics of Main SP and Secondary SP structures under various unit cell configurations after validated by experimental tests. The results indicates that despite sharing the same 1/8th cell structure, the axially-loaded behaviours of the Main SP and Secondary SP structures are not identical, particularly in scenarios involving a large unit cell size and a small number of unit cells. The Main SP structures display an axial load-bearing behaviour that is largely unaffected by variations in the number of unit cells along the x and y axes, while Secondary SP structures show a more pronounced improvement in average strength and energy absorption capacity when the number of unit cells increases. Main SP structures offer superior axial load-bearing capabilities and energy absorption efficiency, which is a significant consideration for their application in scenarios demanding high structural integrity. This distinction underlines the importance of tailored design and selection of SP configurations based on specific engineering requirements.

Impact of Tissue Damage and Hemodynamics on Restenosis Following Percutaneous Transluminal Angioplasty: A Patient-Specific Multiscale Model.

Multiscale agent-based modeling frameworks have recently emerged as promising mechanobiological models to capture the interplay between biomechanical forces, cellular behavior, and molecular pathways underlying restenosis following percutaneous transluminal angioplasty (PTA). However, their applications are mainly limited to idealized scenarios. Herein, a multiscale agent-based modeling framework for investigating restenosis following PTA in a patient-specific superficial femoral artery (SFA) is proposed. The framework replicates the 2-month arterial wall remodeling in response to the PTA-induced injury and altered hemodynamics, by combining three modules: (i) the PTA module, consisting in a finite element structural mechanics simulation of PTA, featuring anisotropic hyperelastic material models coupled with a damage formulation for fibrous soft tissue and the element deletion strategy, providing the arterial wall damage and post-intervention configuration, (ii) the hemodynamics module, quantifying the post-intervention hemodynamics through computational fluid dynamics simulations, and (iii) the tissue remodeling module, based on an agent-based model of cellular dynamics. Two scenarios were explored, considering balloon expansion diameters of 5.2 and 6.2mm. The framework captured PTA-induced arterial tissue lacerations and the post-PTA arterial wall remodeling. This remodeling process involved rapid cellular migration to the PTA-damaged regions, exacerbated cell proliferation and extracellular matrix production, resulting in lumen area reduction up to 1-month follow-up. After this initial reduction, the growth stabilized, due to the resolution of the inflammatory state and changes in hemodynamics. The similarity of the obtained results to clinical observations in treated SFAs suggests the potential of the framework for capturing patient-specific mechanobiological events occurring after PTA intervention.

Exploring the structural mechanics of titanium nickel solid alloy using COMSOL Multiphysics: A Poisson equation and continuity equation perspective

This study investigates the structural mechanics of titanium-nickel (Ti-Ni) alloy thin film using computational modelling through COMSOL Multiphysics based on Poisson’s equation and continuity equation for stress check by considering its linear elastic, conservation of charge and providing insight into nanomaterial deformation. In the COMSOL environment the parameters for titanium nickel (Ti-Ni) are embedded in the COMSOL Simulink interface. The Thin film layer was designed by defining the layer geometry of the size and shape of the layer with a width of 500 μm, depth of 200 μm and height of 3 μm subjected to boundary conditions such as von – mises stress, surface temperature, iso-surface temperature, multi-slice electric potential, displacement component, surface elastic strain energy density and total enthalpy. The results displayed a trend that is, as the surface temperature increases there will be an increase in the current densities associated with high electrical conduction. On the same note, the designed thin film layer will pass the percolation threshold. The results of surface elastic strain energy density and total enthalpy imply that the designed thin film layer is effective and efficient as a structural pseudopotential device (photodiode).

The Roofing System of the Tholos of Athena Pronaia in Delphi: Archaeological Hypotheses and Structural Suggestions

ABSTRACT This study aims to provide an integrated understanding of archaeological findings by evaluating reconstruction hypotheses from a technological and structural perspective, focusing on the Tholos of Athena Pronaia in Delphi, a prime example of 4th and 3rd-century BC Greek architecture. Archaeological research primarily focuses on proportional rules and decorative aspects. The original roof configuration remains uncertain despite its architectural significance and characteristic interlocking marble tiles. The investigation of structural problems is crucial for selecting the most feasible reconstructions and formulating hypotheses on materials, dimensions of structural elements, and how connections between elements were constructed. Due to the many reconstruction uncertainties, a reverse design approach was employed, utilizing schematic technological sections based on the known structural and technical knowledge of the time to ensure consistency between archaeological images and potential underlying structural diagrams. Variables were considered for uncertain dimensions, and the structure’s safety was analysed in terms of material strength and stability. This highlighted not only unsafe solutions but also potential novelties in structural conceptions. This contribution serves as a starting point for research, placing structural mechanics at the service of archaeology with a focus on the tholos roof system.

Model reduction for fatigue life estimation of a welded joint driven by machine learning

In structural mechanics, the design of component assemblies is fundamental for ensuring structural integrity and durability. Fatigue, a common failure mode, particularly challenges the validation of welded joints' fatigue resistance. Various analytical and numerical methods estimate fatigue life but often involve costly processes requiring extensive parameter adjustments and software integration. This study applies machine learning (ML) for metamodeling of a complete finite element analysis and fatigue analysis workflow for estimating the fatigue life of welded joints, considering geometric characteristics, loading conditions, and weld classes. The comparison of the effect of learning database configuration for several regression estimators has led to a reduced model that provides design rules. Our findings demonstrate the significant potential of ML to streamline complex frameworks and accurately estimate the fatigue life of welded joints, advancing AI's application in mechanical engineering.

Reconstruction of the real 3D shape of the SARS-CoV-2 virus

The photographs of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus taken by electron transmission microscopy and cryoelectron microscopy provide only a 2D silhouette. The viruses appear to look like distorted circles. The present paper questions the real shape of the SARS-CoV-2 virus and makes an attempt to give an answer. Is this a general ellipsoid, a spheroid with rotational symmetry, a sphere, or something else? The answer requires the application of tools from three different disciplines: structural mechanics, microbiology, and statistics. A total of 590 virus photographs taken from 22 recently published papers were examined. From this experimental data pool, the histogram of diameter ratios was built from the 283 measurements where the virus images could be approximated as ellipses. The curve peaks at the diameter ratio of 1.22. The transformation equation for the spatial shape to the planar shade was derived for a fixed light source of the microscope. This equation involves an unknown orientation of the viruses with respect to the microscope. Two sets of models were developed, one with a uniform distribution of the virus orientation and the other with the orientation defined by the normalized beta distribution. In both sets of models, the unknown diameter ratio of the spheroidal virus was regarded as a random realization from translated gamma distributions. The parameters of the distribution of the kernel functions were determined by minimizing the mean square difference between the predicted and measured 2D histograms. The information included in the measured histograms was found to be insufficient to find an unknown distribution of the virus’s orientation. Simply too many unknown parameters render the solution physically unrealistic. The minimization procedure with a uniform probability of virus orientation predicted the peak of the aspect ratio of the 3D spheroid at 1.32. Based on this result, models of the virus will be developed in the continuation of this research for a full dynamic analysis.

Research and Practice of Structural Mechanics Teaching Design in the Digital Age

This paper explores innovative strategies and practical applications for teaching structural mechanics courses in the digital era. The focus is on analyzing how online-offline hybrid teaching models can optimize the educational process in structural mechanics and enhance teaching efficiency and student learning outcomes. The paper elaborates in detail on how digital technologies support the teaching of structural mechanics, including the use of various digital tools and platforms to foster student interaction and participation, as well as improve their understanding and mastery of course content. Additionally, the research discusses the effective integration of ideological and political education within structural mechanics teaching, aimed at fostering students' comprehensive abilities. This study provides new perspectives and methods for the teaching design of structural mechanics and related disciplines in a digital learning environment.

Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis

The dynamic response analysis of submerged floating tunnels (SFTs) under seismic action is a complex two-way fluid–structure coupling problem that requires expertise in structural dynamics, fluid mechanics, and advanced computational methods. The coupled Eulerian–Lagrangian (CEL) method is a promising method for solving fluid–structure interaction problems, but its application to SFTs is not well established. Therefore, it is crucial to verify the accuracy and reliability of the CEL method in fluid–structure coupling simulations. This study verified the applicability of the CEL method for simulating one-way and two-way fluid–structure coupling cylindrical flow problems, and then applied the CEL method for the analysis of a shaking table test of a model SFT. A comparison of results obtained with the CEL method with those obtained in a previous indoor model test of an SFT demonstrates the agreement between the results of the CEL method and the overall trend of the experimental results, indicating the reliability of the method for the seismic analysis of SFTs. Moreover, the analysis of the dynamic response characteristics of SFTs under seismic conditions provides data support and a technological means for the seismic design of SFTs.

DESIGN AND ANALYSIS OF THE PISTON USING COMPOSITE MATERIALS LIKE AA7275 STRUCTURAL AND THERMAL ANALYSIS DONE IN ANSYS SOFTWARE

This project focuses on the design and analysis of a piston utilizing composite materials, particularly AA7275, to enhance structural integrity and thermal efficiency. The study employs ANSYS software to conduct comprehensive structural and thermal analyses. The structural analysis evaluates the mechanical behavior, stress distribution, and deformation characteristics of the piston under varying operating conditions. Meanwhile, the thermal analysis investigates the heat transfer mechanisms, temperature distribution, and thermal stress accumulation within the piston. By integrating composite materials and utilizing advanced simulation techniques, this project aims to optimize the performance and reliability of the piston, contributing to advancements in engine technology. ANSYS software facilitates Multiphysics simulations, allowing engineers to study the interactions between different physical phenomena such as structural mechanics, fluid dynamics, electromagnetics, and thermal effects. Its robust solvers and algorithms enable accurate prediction of system behavior under various operating conditions, helping engineers optimize performance, reliability, and efficiency. Moreover, ANSYS provides a range of specialized modules tailored to specific industries and applications, including automotive, aerospace, electronics, healthcare, and renewable energy. These modules incorporate domain-specific features and workflows, empowering engineers to address industry-specific challenges and design requirements effectively. Recent advancements in ANSYS software include enhanced computational capabilities, improved user interfaces, and integration with emerging technologies such as artificial intelligence and additive manufacturing. These developments enable engineers to accelerate the design iteration process, reduce time-to-market, and achieve superior product performance. Furthermore, ANSYS supports collaborative engineering workflows through its integration with product lifecycle management (PLM) and computer-aided design (CAD) software. This integration facilitates seamless data exchange between design, simulation, and manufacturing teams, ensuring consistency and accuracy throughout the product development lifecycle. KEY WORDS : AA7275; Piston; Structural integrity and Thermal Efficiency; Structural analysis; ANSYS; CAD.

A computational approach to modeling flow-induced large deformation of thin-walled compliant vessels

We present a computational method capable of modeling 3D flow-induced deformation of thin, highly compliant, hyperelastic vessels conveying viscous, inertial fluid. The method can uniformly consider vessel extension and collapse. Very large inflation, transient deformation, complex flow features as well as highly complex buckling shapes are well resolved by this method. The methodology combines finite volume and spectral methods for fluid motion, finite element method for structural mechanics of the vessel wall, and the immersed boundary method for two-way coupling between the wall and fluid. A hybrid of the continuous forcing and the ghost node methodologies capitalizing on the strengths of each is developed. The method avoids the surface instability encountered with the continuous forcing methods, as well as the need for domain remeshing as required in the iterative and partitioned approaches. The vessel wall can follow linear or nonlinear (strain softening and hardening) material models, and the fluid inertia can vary over a wide range. We demonstrate the versatility of the method by considering vessel inflation and collapse with large, complex, and transient deformation. Remarkable differences in vessel inflation at low versus moderate inertia are observed; this includes steady versus oscillatory motion, emergence of flow recirculation and pressure wave reflection which are well resolved. For the collapsing vessels, well-defined shapes with different buckling modes as well as highly complex buckling with fine surface folds are predicted. Additionally, a second-order correction to the well-known law of Laplace is developed and used to validate our computational results for vessel inflation.

Toward Fatigue-Tolerant Design of Additively Manufactured Strut-Based Lattice Metamaterials

Abstract The advent of additive manufacturing (AM) has enabled the prototyping of periodic and non-periodic metamaterials (a.k.a. lattice or cellular structures) that could be deployed in a variety of engineering applications where certain combinations of performance features are desirable. For example, these structures could be used in a variety of naval engineering applications where lightweight, large surface area, energy absorption, heat dissipation, and acoustic bandgap control are desirable. Furthermore, combining the multifunctional design optimization of these structures with progressive degradation due to cyclic loading could lead to fatigue-activated attritable systems with potentially tailorable performances not yet in reach by current conventional systems. Nevertheless, in order to deploy these complex geometry structures their multiphysics response has to be well understood and characterized. The objective of the current effort is to describe an initial approach for designing a uniaxial metamaterial specimen for fatigue testing as the first step toward the design of multi-axial fatigue test coupons. In order to compare bending- and stretching-dominated structures, two strut-based lattices made of Ti-6Al-4V alloy consisting of the octet and tetrakaidecahedron (or Kelvin) cells are examined. The specimens are designed to fail in the central area of the specimen where edge effects are minimized. Finite element results of the relevant structural mechanics are implemented and exercised to compare the performance of the eight relevant geometries and to evaluate the effect of relative density on fatigue life.

Spectral Properties of Mimetic Operators for Robust Fluid–Structure Interaction in the Design of Aircraft Wings

This paper presents a comprehensive study on the spectral properties of mimetic finite-difference operators and their application in the robust fluid–structure interaction (FSI) analysis of aircraft wings under uncertain operating conditions. By delving into the eigenvalue behavior of mimetic Laplacian operators and extending the analysis to stochastic settings, we develop a novel stochastic mimetic framework tailored for addressing uncertainties inherent in the fluid dynamics and structural mechanics of aircraft wings. The framework integrates random matrix theory with mimetic discretization methods, enabling the incorporation of uncertainties in fluid properties, structural parameters, and coupling conditions at the fluid–structure interface. Through spectral and localization analysis of the coupled stochastic mimetic operator, we assess the system’s stability, sensitivity to perturbations, and computational efficiency. Our results highlight the potential of the stochastic mimetic approach for enhancing reliability and robustness in the design of aircraft wings, paving the way for optimization algorithms that integrate uncertainties directly into the design process. Our findings reveal a significant impact of stochastic perturbations on the spectral radius and eigenfunction localization, indicating heightened system sensitivity. The introduction of randomized singular value decomposition (RSVD) within our framework not only enhances computational efficiency but also preserves accuracy in low-rank approximations, which is critical for handling large-scale systems. Moreover, Monte Carlo simulations validate the robustness of our stochastic mimetic framework, showcasing its efficacy in capturing the nuanced dynamics of FSI under uncertainty. This study contributes to the fields of numerical methods and aerospace engineering by offering a rigorous and scalable approach for conducting uncertainty-aware FSI analysis, which is crucial for the development of safer and more efficient aircraft.

Multi-criteria parametric optimization of the displacement and weight of a shell of minimal surface on a circular contour consisting of two inclined ellipses under thermal and power loading with consideration of geometric nonlinearity

Thin shells are well suited to optimal design problems, and the finite element method and gradient descent method make it possible to solve inverse problems in structural and applied mechanics. The calculation process takes into account geometric nonlinearity. In modern numerical studies, a formulation taking into account geometric nonlinearity in the finite element method is used, namely, a stepwise loading procedure. It becomes necessary to take into account the relations between displacement vectors and their derivatives and strain increments. These relations help to determine the stiffness matrix of the finite element at each loading step, which leads to a qualitative study of real displacements in the structure. The geometrically nonlinear calculation of a shell of minimum surface on a circular contour consisting of two inclined ellipses in a multi-criteria parametric optimization is performed by the finite element method. The finite element method is a universal variational method that is focused on solving the most complex problems of elasticity theory and applied and structural mechanics using separate calculation complexes. Stiffness matrix - for the whole body is formed on the basis of the finite element stiffness matrix. The system of solving equations of the finite element method is formed using the Lagrange's variational principle, according to which the total potential energy P of a finite element model of a body in a state of stability and equilibrium has a minimum value. As part of the sensitivity analysis, gradients of the design variables of the structure, displacements in the form of partial derivatives of these characteristics along the design variables, and shell thickness are calculated. The sensitivity information serves as the basis for building an optimal design algorithm using the gradient descent method of the objective function. Standard multi-criteria parametric optimization allows for an average of 10% steel savings, with geometric nonlinearity increasing to 20%. At this study site, 24.4% of sheet steel was saved, which is a significant relative saving. This methodology of the authors shows high results for innovative design and production of steel thin shells in Ukraine and around the world, and also makes it possible to apply several types of optimization to one research object.

Grain size-dependent delay of avalanches during void evolution in a nanocrystalline Ni

The effect of grain size on the underlying dynamics of void evolution in a nanocrystalline (NC) Ni was dissected. Multiscale characterizations reveal that, unlike the suppression of void nucleation by the compressive stress in coarse-grained Ni, dispersive nano- and micro-voids formed readily in NC Ni during compression. Acoustic emission measurements and molecular dynamic simulations demonstrate that the unique structural features and deformation mechanics implicitly occurring in NC metals play multifaceted roles during void evolution. Although the presence of excessive nucleation sites, such as grain boundaries and triple junctions, together with the activation of intergranular plasticity facilitate void nucleation, high interface population retards dislocation-driven void expansion, resulting in sluggish void growth and a corresponding delay of coalescence avalanches.