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A new set of robust finite elements for the analysis of general anisotropic multilayered composite plates was presented. Recently, the enhanced-refined zigzag theory (en-RZT) has shown its accuracy in predicting the structural response of laminated plates exhibiting a high transverse anisotropy. Moreover, the en-RZT requires only C0 -continuous shape functions to formulate accurate and efficient finite elements. The formulation of three-node triangular (TRIA3) and four-node quadrilateral (QUAD4) flat finite elements based on the en-RZT kinematics was focused. In order to eliminate the shear-locking effect that affects low-order C0 -based elements, the constrained anisoparametric interpolation strategy and an appropriate element shear correction (ESC) factor were implemented at the element level. Then, the variational consistent triangular (enRZT-T3c) and quadrilateral (enRZT-Q4c) element were obtained and numerically assessed. A detailed analysis has been performed to evaluate the convergence behavior of the newly formulated elements for bending and free-vibration problems. The influence of the mesh distortion, load configurations, and boundary conditions were finely addressed. The results provided and comparisons with 3D models demonstrate the accuracy and robustness of enRZT-T3c and enRZT-Q4c elements for a wide variety of problems and laminate configurations, including ultra-thin arbitrarily oriented multilayered plates. [ABSTRACT FROM AUTHOR]

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods. [ABSTRACT FROM AUTHOR]

By utilizing computed tomography (CT) technology, we can gain a comprehensive understanding of the specific details within the material. When combined with computational mechanics, this approach allows us to predict the structural response through numerical simulation, thereby avoiding the high experimental costs. In this study, the tensile cracking behavior of carbon–silicon carbide (C/SiC) composites is numerically simulated using the bond-based peridynamics model (BB-PD), which is based on geometric models derived from segmented images of three-dimensional (3D) CT data. To obtain results efficiently and accurately, we adopted a deep learning-based image recognition model to identify the kinds of material and then the pixel type that corresponds to the material point, which can be modeled by BB-PD for failure simulation. The numerical simulations of the composites indicate that the proposed image-based peridynamics (IB-PD) model can accurately reconstruct the actual composite microstructure. It can effectively simulate various fracture phenomena such as interfacial debonding, crack propagation affected by defects, and damage to the matrix. [ABSTRACT FROM AUTHOR]

Muscular hydrostats, such as octopus arms or elephant trunks, lack bones entirely, endowing them with exceptional dexterity and reconfigurability. Key to their unmatched ability to control nearly infinite degrees of freedom is the architecture into which muscle fibers are weaved. Their arrangement is, effectively, the instantiation of a sophisticated mechanical program that mediates, and likely facilitates, the control and realization of complex, dynamic morphological reconfigurations. Here, by combining medical imaging, biomechanical data, live behavioral experiments, and numerical simulations, an octopus-inspired arm made of >200 continuous muscle groups is synthesized, exposing "mechanically intelligent" design and control principles broadly pertinent to dynamics and robotics. Such principles are mathematically understood in terms of storage, transport, and conversion of topological quantities, effected into complex 3D motions via simple muscle activation templates. These are in turn composed into higher-level control strategies that, compounded by the arm's compliance, are demonstrated across challenging manipulation tasks, revealing surprising simplicity and robustness. [ABSTRACT FROM AUTHOR]

Hard-magnetic soft materials, which exhibit finite deformation under magnetic loading, have emerged as a promising class of soft active materials for the development of phononic structures with tunable elastic wave band gap characteristics. In this paper, we present a gradient-based topology optimization framework for designing the hard-magnetic soft materials-based two-phase phononic structures with wide and magnetically tunable anti-plane shear wave band gaps. The incompressible Gent hyperelastic material model, along with the ideal hard-magnetic soft material model, is used to characterize the constitutive behavior of the hard-magnetic soft phononic structure phases. To extract the dispersion curves, an in-house finite element model in conjunction with Bloch's theorem is employed. The method of moving asymptotes is used to iteratively update the design variables and obtain the optimal distribution of the hard-magnetic soft phases within the phononic structure unit cell. Analytical sensitivity analysis is performed to evaluate the gradient of the band gap maximization function with respect to each one of the design variables. Numerical results show that the optimized phononic structures exhibit a wide band gap width in comparison to a standard hard-magnetic soft phononic structure with a central circular inclusion, demonstrating the effectiveness of the proposed numerical framework. The numerical framework presented in this study, along with the derived conclusions, can serve as a valuable guide for the design and development of futuristic tunable wave manipulators. [ABSTRACT FROM AUTHOR]

Motivated by the recent discoveries concerning the exceptional surface engineering capabilities offered by high-entropy alloys (HEAs), this article investigates the tribological behavior of pristine and graphene nano-engineered AlxCoCrFeNi HEA. The atomic-scale scratching is performed for different configurations of HEA in a molecular dynamics environment, wherein, at first, aluminum (Al) (x = 0.1, 0.3, and 0.5) concentration-dependent wear behavior of HEA configurations is compared. It is observed that with the increase in Al concentration, the normal and tangential forces, friction coefficients, and wear-rates were significantly reduced, due to the increased plastic deformation and phase transformation. Graphene-engineered HEA surfaces are perceived in two different ways, in the presented investigation: first, the graphene coating is applied directly over the HEA surface, and second, the graphene layers are embedded at a certain depth below the target surface. It is observed that graphene-engineered HEA surfaces exhibit exceptional performance against nano-scratching, wherein, the distribution and height of surface morphology (pile-ups) have seen significant improvement and elastic recovery, especially in the cases of graphene coating over the surface. The findings obtained from this study will be extremely helpful in bringing the bottom-up multi-scale design route for graphene-engineered HEA surfaces to reality. This will enable the development of a novel class of functionally engineered surfaces with enhanced wear and scratch resistance. [ABSTRACT FROM AUTHOR]

This paper presents the integration of a numerical structural model based on the redundancy matrix and experimental results of multi-layered randomized architected materials (MLRAM). It presents a combination of the relatively new field of architected materials with a load-independent performance indicator from theoretical structural mechanics. The redundancy matrix by itself provides a measure for structural assessment that is independent of a specific load case. Various layouts of the MLRAM samples and recorded testing allow the analysis of the redundancy distribution within the structure as it undergoes failure. An in-depth analysis of the tested MLRAM samples is provided, as they show a high degree of static indeterminacy and thus, multiple different load paths. A special focus lies on the change of the redundancy distribution as global progressive failure happens. Another focus is set on the investigation of the failure initiation, meaning that the redundancy distribution can help to identify critical elements. A simple introductory example shows the interdependence between the variation of the geometric location of nodes and the redundancy distribution. The study shows, that the distribution of static indeterminacy can be used as a measure to quantify vulnerability to failure and rank the individual element's importance. Furthermore, progressive collapse is identified as a series of local effects in the highly statically indeterminate MLRAM samples, underlining the fact that the spatial distribution of static indeterminacy is of central importance for the assessment of structural safety. [ABSTRACT FROM AUTHOR]

Scalar fields, such as stress or temperature fields, are often calculated in shape optimization and design problems in engineering. For complex problems where shapes have varying topology and cannot be parametrized, data-driven scalar field prediction can be faster than traditional finite element methods. However, current data-driven techniques to predict scalar fields are limited to a fixed grid domain, instead of arbitrary mesh structures. In this work, we propose a method to predict scalar fields on arbitrary meshes. It uses a convolutional neural network whose feature maps at multiple resolutions are interpolated to node positions before being fed into a multilayer perceptron to predict solutions to partial differential equations at mesh nodes. The model is trained on finite element von Mises stress fields, and once trained, it can estimate stress values at each node on any input mesh. Two shape datasets are investigated, and the model has strong performance on both, with a median R2 value of 0.91. We also demonstrate the model on a temperature field in a heat conduction problem, where its predictions have a median R2 value of 0.99. Our method provides a potential flexible alternative to finite element analysis in engineering design contexts. Code and datasets are available online. [ABSTRACT FROM AUTHOR]

This study focuses on the influence of the pile end soil on the torsional vibration of a pile based on a fictitious soil pile model considering stress diffusion. The pile end soil is simulated as a cone-shaped fictitious soil pile whose parameters remain the same as the soil. The dynamic equilibrium equations for the pile–soil system subjected to torsional dynamic loadings are developed and solved to obtain the corresponding analytical solution. Then, comparisons with other solutions are conducted to verify the reliability of the proposed solution. Finally, parametric analysis results are presented based on the proposed solution to portray the influence of the stress diffusion angle, thickness and shear wave velocity of the pile end soil. [ABSTRACT FROM AUTHOR]

Material parameter identification within the field of mechanics of materials is driven by different issues: experiments affecting the parameters, constitutive models describing the physical behavior of a material, discretization schemes for solving the initial boundary‐value problem of the underlying tests, as well as schemes calibrating the parameters at experimental observations. Furthermore, the question of the quality of the parameters must be addressed. The result concerns the issue of which influence parameter uncertainties have on the uncertainty of simulation results and can be addressed during validation. In this contribution, we provide a short overview of concurrent approaches to identify the parameters, where most of the methods can be distinguished into the reduced formulation, which is based on the implicit function theorem, and a particular form of the all‐at‐once approach. The latter represents the weighted sum of two contributions, first, the distances between physical state equations and zero, and, second, the distance between model response (simulation result) and experimental data. Since the formulation of the all‐at‐once approach is not well‐known in computational solid mechanics, it is investigated for the simple example of linear elasticity employing artificially noised data. It turns out that the all‐at‐once approach is not as robust as the well‐established reduced approach, especially for problems considering full‐field data. Moreover, a strong sensitivity to the noise and the initialization of the optimized parameters are observed indicating a pronounced ill‐posedness of the all‐at‐once formulation for specific applications. [ABSTRACT FROM AUTHOR]

While interactive simulations have been mostly limited to Computer Graphics applications, new generations of Graphics Processing Units (GPUs) allow the realization of industrial-grade interactive 3D physics simulations. By combining an immersed boundary method with efficient GPU-based MINRES and CG solvers using a GPU-based geometric multigrid preconditioner, we demonstrate a fast industrial 3D computational mechanics solver. The various implementation aspects - specifically how they differ from similar concepts used in the Computer Graphics community - are discussed in detail. The proposed concept opens up new classes of industrial simulation applications allowing a democratization beyond today's expert users, from designer centric simulation to operational and service decisions based on 3D simulations. To support this, we provide various benchmark cases including a real-world study of a simulation-based service decision for a damaged gear-box mount. [ABSTRACT FROM AUTHOR]

This work numerically explores the anisotropy, impact phase wave propagation, buckling resistance, and natural vibration of ultra-high performance concrete (UHPC) and UHPC-steel interpenetrating phase composite (IPC) with triply periodic minimal surfaces (TPMSs), including sheet and solid gyroid, primitive, diamond, and the Schoen I-graph-wrapped package (I-WP). The experiment is conducted to verify the accuracy of the numerical model in terms of Young's modulus of polylactic acid (PLA)-based TPMS lattices and PLA-cement IPCs with TPMS cores, with the highest percent difference of 15% found for IPCs and 17% found for lattice. The results indicate that UHPC material with sheet gyroid exhibits the least extreme anisotropy in response to the varying orientation among other lattices regardless of the change of solid density, making it the ideal candidate for construction materials. Interestingly, compared to UHPC-based TPMS lattice, IPCs possess a much smaller anisotropy and exhibit almost isotropy regardless the variation of solid density and TPMS topology, offering a free selection of TPMS type to fabricate IPCs without much care of anisotropy. The phase wave velocity and buckling resistance of UHPC- and IPC-based beams with TPMSs nonlinearly decrease with a drop of TPMS solid density, but it is the almost linear pattern for the case of natural vibration frequency. UHPC material and IPC with sheet gyroid lattice are found to possess the lowest phase wave velocity and exhibit the least anisotropy of wave propagation, showing it as an ideal candidate for UHPC material to suppress the destructive energy induced by the external impact. [ABSTRACT FROM AUTHOR]

Numerical approaches are elaborated to calculate the rheological response of laminated glass beams, whose viscoelastic interlayer is modeled via fractional calculus. This mathematical description is very effective when the relaxation function of the polymer can be expressed by continuously connected branches of power laws, as is the case for most materials used to laminate glass. The classical approach uses the Grünwald-Letnikov approximation of fractional derivatives, but it requires constant time-steps, which would become very large to reasonably cover the entire observation time, thus losing accuracy. The use of the L1 algorithm with increasing time-steps is proposed, which is well suited to the power law character of the relaxation function. This allows to follow the long-term creep response, providing a better approximation when needed. The method is implemented for beams laminated with viscoelastic interlayers whose relaxation is described by four branches of power laws, to cover most practical cases. Numerical experiments show their advantages over the Grünwald-Letnikov approach for characterizing the long-term structural response. [ABSTRACT FROM AUTHOR]

In computational homogenization approaches, data-driven methods entail advantages due to their ability to capture complex behavior without assuming a specific material model. Within this domain, constitutive model-based and model-free data-driven methods are distinguished. The former employ artificial neural networks as models to approximate a constitutive relation, whereas the latter directly incorporate stress–strain data in the analysis. Neural network-based constitutive descriptions are one of the most widely used data-driven approaches in computational mechanics. In contrast, distance-minimizing data-driven computational mechanics enables substituting the material modeling step entirely by iteratively obtaining a physically consistent solution close to the material behavior represented by the data. The maximum entropy data-driven solver is a generalization of this method, providing increased robustness concerning outliers in the underlying data set. Additionally, a tensor voting enhancement based on incorporating locally linear tangent spaces enables interpolating in regions of sparse sampling. In this contribution, a comparison of neural network-based constitutive models and data-driven computational mechanics is made. General differences between machine learning, distance minimizing, and entropy maximizing data-driven methods are explored. These include the pre-processing of data and the required computational effort for optimization as well as evaluation. Numerical examples with synthetically generated datasets obtained by numerical material tests are employed to demonstrate the capabilities of the investigated methods. An anisotropic nonlinear elastic constitutive law is chosen for the investigation. The resulting constitutive representations are then applied in structural simulations. Thereby, differences in the solution procedure as well as use-case accuracy of the methods are investigated. [ABSTRACT FROM AUTHOR]

Computational mechanics has become an essential tool in engineering, just as the use of hyperelastic materials has seen remarkable growth in everyday applications. Therefore, it is fundamental to study hyperelastic models that represent the behavior of these materials, such as elastomers and polymers. With that in mind, the Mooney-Rivlin, Neo-Hookean, Ogden, and Yeoh models were implemented in a computational code in FORTRAN using the Positional Finite Element Method with Reissner kinematics and the Newton-Raphson method for nonlinear analysis of plane frames with samples of elastomers added with different percentages of carbon black. Ultimately, it was concluded that the Yeoh and Ogden models presented coherent values and that the use of the formulation for nonlinear analysis of plane frame performs well after the modifications proposed by this work. These modifications consisted of adding the first and second strain invariants of the simple shear formulation to include the consideration of distortion in the specific strain energy of hyperelastic models. [ABSTRACT FROM AUTHOR]

Nature offers a valuable source of inspiration when designing optimized structures. For instance, when two or more shells are joined during growth processes, Nature prefers to exploit interlocking paradigms to enhance the strength of the bond—cranial sutures are one example. Mimicking this distinctive characteristic when welding thin metallic elements may offer some structural benefits. The present study theoretically investigates the possibility of mitigating the detrimental tensile residual stress that always originated by welding processes, by exploiting Computational Welding Mechanics. Specifically, two plates joined by laser welding following sinusoidal paths (i.e., wave‐like), alongside a linear one, will be studied and compared. Thanks to the distinctive temporal and spatial heat distributions of sinusoidal welding, both welding stress and strain develop in dissimilar ways compared with linear path welding—this is reflected to the resulting weldment distortion too. The results show that geometrically optimized sinusoidal welding can effectively reduce residual stress magnitudes compared with the linear configuration counterpart. The implications of these findings are of particular interest when dealing with the structural performance of welded structures, especially in fatigue contexts. Highlights: The effect of sinusoidal path welding on residual stress is investigated.The welding path significantly modifies the thermal history of the weld bead.Sinusoidal path welding offers an effective capability of tuning residual stresses. [ABSTRACT FROM AUTHOR]

Geologic fractures such as joints, faults, and slip surfaces govern the stability and performance of many subsurface systems in the built environment. As such, a variety of approaches have been developed for computational modeling of geologic fractures. Yet none of them lends itself to a straightforward utilization with the classical finite element method widely used in practice. Over the past decade, phase-field modeling has become a popular approach for simulating fracture, because it can be implemented simply with the standard finite element method without any surface-tracking algorithms. However, the standard phase-field formulations do not incorporate several critical features of geologic fractures, including frictional contact, pressure-dependence, quasibrittleness, mode-mixity, and their combined impacts on cracking. This article provides a brief report of a novel phase-field model that incorporates these features of geologic fractures in a well-verified and validated manner. Remarkably, the phase-field model allows one to simulate the combination of cohesive tensile fracture and frictional shear fracture without any algorithms for surface tracking and contact constraints. It is also demonstrated how phase-field modeling enables us to gain insights into geologic fractures that are challenging to investigate experimentally. [ABSTRACT FROM AUTHOR]

Mechanical interactions between rigid rings and flexible cables find broad application in both daily life (hanging clothes) and engineering system (closing a tether-net). A reduced-order method for the dynamic analysis of sliding rings on a deformable one-dimensional (1D) rod-like object is proposed. In contrast to the conventional approach of discretizing joint rings into multiple nodes and edges for contact detection and numerical simulation, a single point is used to reduce the order of the model. To ensure that the sliding ring and flexible rod do not deviate from their desired positions, a new barrier function is formulated using the incremental potential theory. Subsequently, the interaction between tangent frictional forces is obtained through a delayed dissipative approach. The proposed barrier functional and the associated frictional functional are C2 continuous, hence the nonlinear elastodynamic system can be solved variationally by an implicit time-stepping scheme. The numerical framework is initially applied to simple examples where the analytical solutions are available for validation. Then, multiple complex practical engineering examples are considered to showcase the effectiveness of the proposed method. The simplified ring-to-rod interaction model has the capacity to enhance the realism of visual effects in image animations, while simultaneously facilitating the optimization of designs for space debris removal systems. [ABSTRACT FROM AUTHOR]

A direct approach is developed using Streamline Upwind Petrov Galerkin (SUPG) concepts to determine the spatially varying property distribution in a nominally heterogeneous material. The approach is based on successful development of a SUPG-stabilized inverse finite element approach to solve the differential equations of equilibrium in terms of material properties, resulting in a matrix form [A] {E} = {R}, where [A] is a known function of measured axial strains (e.g., from StereoDIC) and axial positions, {R} is a known function of axial body forces, applied loads and reactions, and {E} is a vector of unknown material properties at discrete axial locations. Theoretical and computational developments for the SUPG-stabilized approach are described in detail for one-dimensional applications (e.g., heterogeneous tensile/compression specimens, tensile/compressive surfaces of beams). Property predictions using the SUPG method with analytic strains and additive Gaussian noise are shown to be in excellent agreement with known property values, whereas predictions using the classical Bubnov-Galerkin method exhibit large, spurious oscillations in the predicted material properties. To demonstrate the methodology using experimental measurements, a 3D-printed heterogeneous tensile specimen with independently measured material properties is tested and full-field strains measured at several load levels. Results confirm that SUPG finite element property predictions are in very good agreement with independently determined values at each load level along the specimen length, providing confidence that the SUPG FE analysis framework developed in this work is stable and extendable to multiple dimensions. [ABSTRACT FROM AUTHOR]

Topology optimization is a powerful tool for structural design, while its computational cost is quite high due to the large number of design variables, especially for multilateral systems. Herein, an incremental interpolation approach with discrete cosine series expansion (DCSE) is established for multilateral topology optimization. A step function with shape coefficients (i.e., ensuring that no extra variables are required as the number of materials increases) and the use of the DCSE together reduces the number of variables (e.g., from 8400 to 120 for the optimization of the clamped-clamped beam with four materials). Remarkably, the proposed approach can effectively bypass the checkerboard problem without using any filter. The enhanced computational efficiency (e.g., a ~89.2% reduction in computation time from 439.1 s to 47.4 s) of the proposed approach is validated via both 2D and 3D numerical cases. [ABSTRACT FROM AUTHOR]

The calibration of constitutive models using experimental observations is a well‐established task in computational solid mechanics. Especially for complex constitutive models, multistep calibrations are regularly applied. While this approach is advantageous for reducing the complexity of the calibration problem, the quantification of parameter uncertainties becomes more challenging. In this study, we perform two‐step statistical parameter inference for an elasto‐plastic constitutive model utilizing a frequentist approach and Bayesian inference. The uncertainties are quantified by different methods, in particular, we compare an asymptotic normality analysis and Gaussian error propagation in the frequentist setting with sampling‐based Bayesian inference. Although frequentist and Bayesian parameter inference differ significantly in the way parameters are calibrated, both methods yield similar estimates. Additionally, the uncertainties quantified by asymptotic normality considerations and Gaussian error propagation are quite similar. However, the uncertainties of the elasticity parameters in the frequentist approach are found to be significantly smaller compared to Bayesian inference, due to the underlying approximations in the frequentist setting. Thus, uncertainties can be estimated in both settings, but it is crucial to be aware of the inherent assumptions and simplifications when interpreting the results. [ABSTRACT FROM AUTHOR]

Objective: Intravascular lymphatic valves often occur in proximity to vessel junctions. It is commonly held that disturbed flow at junctions is responsible for accumulation of valve‐forming cells (VFCs) at these locations as the initial step in valve creation, and the one which explains the association with these sites. However, evidence in favor is largely limited to cell culture experiments. Methods: We acquired images of embryonic lymphatic vascular networks from day E16.5, when VFC accumulation has started but the developing valve has not yet altered the local vessel geometry, stained for Prox1, which co‐localizes with Foxc2. Using finite‐element computational fluid mechanics, we simulated the flow through the networks, under conditions appropriate to this early development stage. Then we correlated the Prox1 distributions with the distributions of simulated fluid shear and shear stress gradient. Results: Across a total of 16 image sets, no consistent correlation was found between Prox1 distribution and the local magnitude of fluid shear, or its positive or negative gradient. Conclusions: This, the first direct semi‐empirical test of the localization hypothesis to interrogate the tissue from in vivo at the critical moment of development, does not support the idea that a feature of the local flow determines valve localization. [ABSTRACT FROM AUTHOR]

The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational mechanics. Five main categories are identified and explored: simulation substitution, simulation enhancement, discretizations as neural networks, generative approaches, and deep reinforcement learning. This review focuses on deep learning methods rather than applications for computational mechanics, thereby enabling researchers to explore this field more effectively. As such, the review is not necessarily aimed at researchers with extensive knowledge of deep learning—instead, the primary audience is researchers on the verge of entering this field or those attempting to gain an overview of deep learning in computational mechanics. The discussed concepts are, therefore, explained as simple as possible. [ABSTRACT FROM AUTHOR]

We present an isogeometric analysis (IGA) framework for structural vibrations involving complex geometries. The framework is based on the Complex-Geometry IGA Mesh Generation (CGIMG) method. The CGIMG process is flexible and can accommodate, without a major effort, challenging complex-geometry applications in computational mechanics. To demonstrate how the new IGA framework significantly increases the computational effectiveness, in a set of structural-vibration test computations, we compare the accuracies attained by the IGA and finite element (FE) method as the number of degrees-of-freedom is increased. The results show that the NURBS meshes lead to faster convergence and higher accuracy compared to both linear and quadratic FE meshes. The clearly defined IGA mesh generation process and significant per-degree-of-freedom accuracy advantages of IGA over FE discretization make IGA more accessible, reliable, and attractive in applications of both academic and industrial interest. We note that the accuracy of a structural mechanics discretization, which may be assessed through eigenfrequency analysis, plays an important role in the overall accuracy of fluid–structure interaction computations. [ABSTRACT FROM AUTHOR]

Drip irrigation using a high-performance pressure-compensating (PC) emitter is one of the essential components for precision agriculture, and it is necessary to accurately predict its performance prior to design. In this study, an efficient two-way fluid–structure interaction (FSI) simulation model was developed and verified through an enlarged model experiment. The computational fluid dynamics (CFD) and computational solid mechanics (CSM) models of the FSI simulation were systematically verified, and a calibration method for the overestimated flow rate in the re-rising range was applied. The CFD model was determined to be the shear stress transport turbulence model, and the CSM model was determined to be the Ogden hyperelastic model for the PC emitter. The minimum prediction error for the flow rate was 7.93%, which was within 10% for all cases. The simulation model demonstrated its efficiency by analysing the performance of a single PC emitter with an average total analysis time of 18.6 h. In addition, by comparing various cases according to the design parameters, it is considered that the hardness of the diaphragm has a significant impact on the design of low-pressure PC emitters. The simulation model of this study can accurately predict the performance of PC emitter under specific conditions, yet improvement of simulation model is required to be applied in design optimisation. Future studies may benefit from combining an improved FSI simulation with a surrogate model to further enhance optimisation efforts. • The performance experiment of a pressure-compensating (PC) emitter was modelled. • The accuracy of two-way FSI simulation suggested was within 10% in all the cases. • Hardness of diaphragm was considered as design parameter along with groove size. • Hardness of a diaphragm played a key role in the design of low-pressure PC emitters. [ABSTRACT FROM AUTHOR]

In the realm of computational mechanics, the reproducing kernel particle method (RKPM) shines in addressing challenges associated with intricate geometries and substantial deformations. However, in the Galerkin framework, RKPM has a substantial computational workload due to the complexities of numerical integration. To mitigate this limitation, we present a pioneering approach: the finite volume reproducing kernel particle method (FV-RKPM). FV-RKPM amalgamates the strengths of meshfree methods and finite volume methods, offering benefits such as grid independence, conservation properties, and numerical stability. In this paper, we elaborate on the construction methodology for FV-RKPM, alongside its associated integration strategies. We consistently validate the efficacy of this innovative methodology through a series of numerical results. [ABSTRACT FROM AUTHOR]

The advent of digital image and volume correlation has attracted wide use in fracture mechanics. The full‐field nature of digital image and volume correlation allows for the integration of computational fracture mechanics to analyse cracked samples quantitatively. This review provides a comprehensive overview of current methods used to extract fracture properties from full‐field displacement data. The term full‐field fracture mechanics is introduced to highlight the uniqueness of using inherently noisy experiential data to extract fracture properties. The review focuses on post‐processing‐based approaches rather than integrated approaches, as these have less limitations and are more commonly employed. There are four approaches that are discussed in extracting fracture properties from experimentally computed displacement data: field‐fitting, integral, crack‐opening and cohesive zone modelling approaches. This is further developed to discuss problems associated with using digital image and volume correlation to extract properties, including application examples. [ABSTRACT FROM AUTHOR]

Significant uncertainties can be found in the modelling of geotechnical materials. This can be attributed to the complex behaviour of soils and rocks amidst construction processes. Over the past decades, the field has increasingly embraced the application of artificial intelligence methodologies, thus recognising their suitability in forecasting non-linear relationships intrinsic to materials. This review offers a critical evaluation AI methodologies incorporated in computational mechanics for geotechnical engineering. The analysis categorises four pivotal areas: physical properties, mechanical properties, constitutive models, and other characteristics relevant to geotechnical materials. Among the various methodologies analysed, ANNs stand out as the most commonly used strategy, while other methods such as SVMs, LSTMs, and CNNs also see a significant level of application. The most widely used AI algorithms are Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), representing 35%, 19%, and 17% respectively. The most extensive AI application is in the domain of mechanical properties, accounting for 59%, followed by other applications at 16%. The efficacy of AI applications is intrinsically linked to the type of datasets employed, the selected model input. This study also outlines future research directions emphasising the need to integrate physically guided and adaptive learning mechanisms to enhance the reliability and adaptability in addressing multi-scale and multi-physics coupled mechanics problems in geotechnics. [ABSTRACT FROM AUTHOR]

The complex vibration phenomenon occurs in the downhole environment of the gas-liquid hydrocyclone, which affects the flow field in the hydrocyclone. In order to study the influence of vibration on hydrocyclone separation, the characteristics of the flow field in the downhole gas-liquid hydrocyclone were analyzed and studied under the condition of vibration coupling. Based on Computational Fluid Dynamics (CFD), Computational Solid Mechanics Method (CSM) and fluid-solid coupling method, a fluid-solid coupling mechanical model of a gas-liquid cyclone is established. It is found that under the condition of vibration coupling, the velocity components in the three directions of the hydrocyclone flow field change obviously. The peak values of tangential velocity and axial velocity decrease, and the asymmetry of radial velocity increases. The distribution regularity of vorticity and turbulence intensity in the overflow pipe becomes worse. Among them, the vorticity intensity of the overflow pipe is obviously enhanced, and the higher turbulence intensity near the wall occupies more area distribution range. The gas-liquid separation efficiency of the hydrocyclone will decrease with the increase of the rotational speed of the screw pump, and the degree of reduction can reach more than 10%. However, this effect will decrease with the increase of the rotational speed of the screw pump, so the excitation effect caused by the rotational speed has a maximum limit on the flow field. [ABSTRACT FROM AUTHOR]

This paper provides a specific analysis strategy for tensegrity prism units with different complexities and connectivity. Through the nodal coordinate matrix and connectivity matrix, we can establish the equilibrium equation of the structure in the self-equilibrium state, and the equilibrium matrix can be obtained. The Singular Value Decomposition (SVD) method can find the self-equilibrium configuration. The torsional angle formula between the upper and bottom surfaces of the prismatic tensegrity structure, which includes complexity and connectivity, can be obtained through the SVD form-finding method. According to the torsional angle formula of the self-equilibrium configuration, we carry out the mechanical analysis of the single node, and the force density relationship between elements is gained. As one of the standards, the mass is used to evaluate the light structure. This paper also studied the minimal mass of the self-equilibrium tensegrity structure with the same complexity in different connectivity and got the minimal mass calculation formula. A six-bar tensegrity prism unit is investigated in this paper, which shows the feasibility of systematic analysis of prismatic structures. This paper provides a theoretical reference for prismatic tensegrity units. [ABSTRACT FROM AUTHOR]

Variational integrators play a pivotal role in the simulation and control of constrained mechanical systems. Current Lagrange multiplier-free variational integrators for such systems are concise but inevitably face issues with parameterization singularities, hindering global integration. To tackle this problem, this paper proposes a novel method for constructing variational integrators on manifolds without introducing Lagrange multipliers, offering the benefit of avoiding singularities. Our approach unfolds in three key steps: (1) the local parameterization of configuration space; (2) the formulation of forced discrete Euler-Lagrange equations on manifolds; and (3) the construction and implementation of high-order variational integrators. Numerical tests are conducted for both conservative and forced mechanical systems, demonstrating the excellent global energy behavior of the proposed variational integrators. [ABSTRACT FROM AUTHOR]

In this paper, we develop a nonlinear mixed finite element method for flexoelectric semiconductors and analyze the mechanically tuned redistributions of free carriers and electric currents through flexoelectric polarization in typical structures. We first present a macroscopic theory for flexoelectric semiconductors by combining flexoelectricity and nonlinear drift-diffusion theory. To use C0 continuous elements, we derive an incremental constrained weak form by introducing Langrage multipliers, in which the kinematic constraints between the displacement and its gradient are guaranteed. Based on the weak form, we established a mixed C0 continuous nine-node quadrilateral finite element as well as an iterative process for solving nonlinear boundary-value problems. The accuracy and convergence of the proposed element are validated by comparing linear finite element method results against analytical solutions for the bending of a beam. Finally, the nonlinear element method is applied to more complex problems, such as a circular ring, a plate with a hole, and an isosceles trapezoid. Results indicate that mechanical loads and doping levels have distinct influences on electric properties. [ABSTRACT FROM AUTHOR]

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An accurate prediction of nonlinear hydro‐mechanical (HM) coupling in subsurface structures with pronounced heterogeneity at multiple spatial scales is still an open topic and crucial for numerous engineering applications, for example, hydraulic fracturing and enhanced geothermal systems. In this study, novel high‐order multi‐scale asymptotic solutions are developed to accurately capture the locally oscillating characteristics of gas flow and solid displacement fields at multiple scales. First, the formal macro‐meso two‐scale asymptotic expansion is performed to establish the homogenized solutions and macro‐meso high‐order models which can predict the HM coupling problems at the macroscale and mesoscale. By directly expanding the cell functions defined at the mesoscale to the microscopic levels, the high‐order two‐scale expansion for the mesoscopic cell functions is built, and the upscaled relations for the flow parameters and constitutive coefficients from the microscale to the mesoscale and macroscale are developed correspondingly. The multi‐scale low/high‐order models are established by combining the high‐order expansion of all cell functions at the mesoscale and the developed macro‐meso two‐scale models. The main contributions are that the present approaches follow the reverse thought processes of reiteration homogenization methods, and offer a very innovative and efficient way to establish high‐accuracy high‐order models for the nonlinear HM coupling problems in the heterogeneous porous medium with any number of spatial scales. The effectiveness and accuracy of the present solution are validated by several representative cases with different constitutive coefficient contrasts. The results demonstrate that the high‐order solutions provide an accurate prediction for gas transport and solid deformation of heterogeneous porous media with multi‐scale configurations. [ABSTRACT FROM AUTHOR]

This article presents an elastic-gap free isotropic higher-order strain gradient plasticity theory that effectively captures dissipation associated to plastic strain gradients. Unlike conventional methods that divide the higher-order stress, this theory focuses on dividing the plastic strain gradient into energetic and dissipative components. The moment stress that arises from minimizing a dissipating potential demonstrates a nonlinear evolution over time, resembling the Armstrong-Frederick nonlinear kinematic hardening rule in classical plasticity. The thermodynamically consistent framework establishes additional dissipation in the dissipation inequality. The energetic moment stress saturates as the effective plastic strain increases during plastic flow. In contrast to the Gurtin-type nonincremental model, the proposed model smoothly captures the apparent strengthening at saturation without causing a stress jump. A passivated shear layer is analytically assessed to demonstrate that the proposed theory exhibits the same amount of dissipation as the existing Gurtin-type model when they show similar shear responses at saturation. It is also shown that the plastic flow remains continuous under nonproportional loading conditions using an intermediately passivated shear layer problem. Finally, the proposed theory is validated against a recent experiment involving combined bending torsion of an L-shaped beam using a 3D finite element solution. Overall, the proposed model provides an alternative approach to evaluating the size effect within the nonincremental isotropic strain gradient plasticity theory without introducing any stress jump. [ABSTRACT FROM AUTHOR]

Soft matter mechanics generally involve finite deformations and instabilities of structures in response to a wide range of mechanical and non-mechanical stimuli. Modeling plates and shells is generally a challenge due to their geometrically nonlinear response to loads; however, non-mechanical loads further complicate matters as it is often not clear how they modify the shell's energy functional. In this work, we demonstrate how to form a mechanical interpretation of these non-mechanical stimuli, in which the standard shell strain energy can be augmented with potentials corresponding to how a non-mechanical stimulus acts to change the shell's area and curvature via the natural stretch and curvature. As a result, the effect of non-mechanical stimuli to deform shells is transformed into effective external loadings, and this framework allows for the application of analytical and computational tools that are standard within the mechanics community. Furthermore, we generalize the effect of mass change during biological growth to account for its effect on the stress constitution. The theory is formulated based on a standard, stress-free reference configuration which is known a priori, meaning it can be physically observed, and only requires the solution of a single-field equation, the standard mechanical momentum or equilibrium equation, despite capturing the effects of non-mechanical stimuli. We validate the performance of this model by several benchmark problems, and finally, we apply it to complex examples, including the snapping of the Venus flytrap, leaf growth, and the buckling of electrically active polymer plates. Overall, we expect that mechanicians and non-mechanicians alike can use the approach presented here to quickly modify existing computational tools with effective external loadings calculated in this novel theory to study how various types of non-mechanical stimuli impact the mechanics and physics of thin shell structures. [ABSTRACT FROM AUTHOR]

Abstract: A contact mechanics analysis of interfacial delamination in elastic and elastic-plastic homogeneous and layered half-spaces due to normal and shear surface tractions induced by indentation and sliding was performed using the finite element method. Surface separation at the delamination interface was controlled by a surface-based cohesive zone constitutive law. The instigation of interfacial delamination was determined by the critical separation distance of interface node pairs in mixed-mode loading based on a damage initiation criterion exemplified by a quadratic relation of the interfacial normal and shear tractions. Stiffness degradation was characterized by a linear relation of the interface cohesive strength and a scalar degradation parameter, which depended on the effective separation distances corresponding to the critical effective cohesive strength and the fully degraded stiffness, defined by a mixed-mode loading critical fracture energy criterion. Numerical solutions of the delamination profiles, the subsurface stress field, and the development of plasticity illuminated the effects of indentation depth and sliding distance on interfacial delamination in half-spaces with different elastic-plastic properties, interfacial cohesive strength, and layer thickness. Simulations yielded insight into the layer and substrate material property mismatch on interfacial delamination. A notable contribution of the present study is the establishment of a computational mechanics methodology for developing plasticity-induced cumulative damage models for multilayered structures.

The research shows how to use swarming algorithms to rebuild the heat transfer coefficient, especially in regard to the continuous border condition. The authors utilized their application software to do numerical computations, employing classical variants of swarm algorithms. The numerical calculations employed a functional determining error to assess the accuracy of the estimated result. The functional minimization was conducted with the swarm algorithms (especially ABC and ACO). The geometry analyzed in this study consisted of a square shape referred to as the cast, enclosed within another square shape known as the casting mold. These two squares were separated by a layer facilitating heat conduction, characterized by the coefficient κ. The coefficient of the thermally conductive layer was recalibrated utilizing swarm methods within the range of 900 - 1500 [W/m^2K] and subsequently compared to a predetermined reference value. A finite element mesh consisting of 576 nodes was used for the calculations. The study involved simulations with populations of 5, 10, 15, and 20 individuals. Furthermore, each scenario also took into account noise of 0%, 2%, and 5% of the reference values. Results make evident the reconstructed value of the κ coefficient, cooling curves, and temperatures for the ABC and ACO algorithms are physically correct. The consequences indicate a notable level of satisfaction and strong concurrence with the anticipated of the κ parameter values. The results from the numerical simulations demonstrate considerable promise for applying artificial intelligence algorithms in optimizing production processes, analyzing data, and facilitating data-driven decision-making.

The layup quality of prepreg fibers onto complex surfaces is the most concerned issue in automated fiber placement (AFP), which is intimately related to the contact stress in tractive rolling contact. An analytical model of tractive rolling contact is proposed in this paper to clarify the defects mechanism during the AFP process, which considers the mold curvatures, mechanical properties of contacted objects, and pose information to make it suitable for irregular surfaces and arbitrary conditions. Classical Hertz theory in this model is expanded to 3D contact area, and multi‐scale deformations are united in the accurate calculation of contact stress under geometric constraints. Based on the model, the phenomena of adhesion, microslip, and the transition between them in AFP are systematically analyzed. For engineering applications, a discrete processing strategy is proposed to quickly obtain the local curvatures of the mold. Additionally, an algorithm is designed to calculate the contact stress of any path. Experimental results on an aircraft component demonstrate the accuracy of the proposed model in restoring the contact stress distribution during AFP. In conjunction with specific layup information, this model exhibits utility in forecasting laying defects and parameter optimization. Highlights: An analytical modeling approach based on Hertz theory is proposed to calculate the contact stress in automated fiber placement.A model‐based algorithm is designed and combined with the proposed discrete scheme to quickly restore the contact stress distribution.Contact states related to stress are systematically investigated and defects analysis based on computational mechanics results is illustrated. [ABSTRACT FROM AUTHOR]

Aeroelastic flutter is a dynamically complex phenomenon that has adverse and unstable effects on elastic structures. It is crucial to better predict the phenomenon of flutter within the scope of aircraft structures to improve the design of their wings. This review aims to establish fundamental guidelines for flutter analysis across subsonic, transonic, supersonic, and hypersonic flow regimes, providing a thorough overview of established analytical, numerical, and reduced-order models as applicable to each flow regime. The review will shed light on the limitations and missing components within the previous literature on these flow regimes by highlighting the challenges involved in simulating flutter. In addition, popular methods that employ the aforementioned analyses for optimizing wing structures under the effects of flutter--a subject currently garnering significant research attention--are also discussed. Our discussion offers new perspectives that encourage collaborative effort in the area of computational methods for flutter prediction and optimization. [ABSTRACT FROM AUTHOR]

The harsh environment during airplane take-off and flights with complex operating conditions require a high dynamic and impact resistance capability of airplane engines. The design, development, and performance evaluation of new turbofan engines are generally performed through numerical simulations before a full-scale model or prototype experiment for certification. Simulations of fan blade containment tests can reduce trial–error testing and are currently the most convenient and inexpensive alternative for design; however, certification failure is always a risk if the calibration of material models is not correctly applied. This work presents a three-dimensional computational model of a turbofan for designing new engines that meet the certification requirements under the blade containment test. Two calibrated Johnson–Cook plasticity and damage laws for Ti64 are assessed in a simulation of a turbofan blade containment test, demonstrating the ability of the models to be used in the safe design of aircraft engine components subjected to dynamic impact loads with large deformations and adequate damage tolerance. [ABSTRACT FROM AUTHOR]

Composite solid propellant is a kind of viscoelastic composite with high filling ratio and multi‐scale composition characteristics, and its macroscopic mechanical properties strongly depend on the microstructure of the propellant materia. However, with the increasing complexity of composition, structure and properties of composite solid propellants, the traditional research paradigm based on experimental observation, theoretical modeling and numerical simulation has encountered new scientific challenges and technical bottlenecks in the mechanical behavior analysis, charge design and manufacturing of composite solid propellants. Among them, the problems such as insufficient experimental observation, lack of theoretical model, limited numerical analysis and difficult verification of results restrict the development of composite solid propellants in future‐oriented engineering applications to a certain extent. The data‐driven computational mechanics method can directly establish complex relationships between variables from high‐dimensional and high‐throughput data, which can capture trends that are difficult to be found by traditional mechanics research methods, and has inherent advantages in the analysis, prediction and optimization of complex systems. This paper mainly reviews and evaluates the research of neural network based modeling, model‐free data‐driven calculation and data‐driven multi‐scale calculation, which provides the correct direction for the subsequent research of multi‐scale mechanical behavior of composite solid propellants based on data‐driven. [ABSTRACT FROM AUTHOR]

In this work, we adopt a problem-based learning approach to develop an integrative bachelor project that relies on competences acquired from basic courses in mathematics, mechanics and computation. In this sense, the proposed project tries to pave the way from "apparently disconnected" concepts gained through previous studies toward the field of computational mechanics. On this basis, we study the motion of a particle along an arbitrary curve in space subject only to the gravitational field, the so-called sliding bead. Although this is a classic problem in mechanics, it has a substantial richness from a theoretical and practical point of view where the student reinforcing abstract skills and exploring different aspects of its numerical solution are allowed. Along this path, we start with the modeling process. This is followed by a stability analysis of the governing differential equations. Later on, we present a moderate introduction to classical numerical time integration by considering several numerical schemes and the well-known Matlab add-on Simulink. Finally, we present a brief discussion about how the proposed project allows articulating concepts of mathematics, mechanics, and computation in engineering programs of studies at horizontal and vertical levels. [ABSTRACT FROM AUTHOR]

A geometrical and mechanical design strategy is proposed for bioelectronics to improve wearability and comfort by reducing the magnitude of interfacial stresses and ensuring that the spatial stress distributions are below the somatosensory threshold for skin sensitivity. Conceptually, bioelectronic devices with soft polymeric encapsulations and internal rigid electronic components result in a mechanically hybrid composite structure, with intrinsically soft mechanics to facilitate integration with biological tissues through mechanical compliance. For accurate signal acquisition and sensing in curvilinear regions (e.g., limbs, chest, forehead), bioelectronic devices are pressed and bent to closely match the skin morphology, resulting in additional interfacial stresses. In the present work, we demonstrate how curvature-matching designs for the bioelectronic-skin interface can reduce the resulting normal and shear stresses generated from device adhesion and skin stretching during dynamic motions. Finite element modeling of the skin curvature, encapsulation, and internal electronic layouts was used to quantify the spatial distribution of the underlying stresses at the skin interface based on a mismatch curvature angle θ between the device and skin. The results show that curvature-matching designs for selected cases of θ = 30 deg and 60 deg can reduce the normal and shear stresses by up to 45% and 70%, respectively, even for a stretch of up to λ = 1.3. The proposed curvature-matching design strategy can inform the future design of user-specific bioelectronics to create anatomically compatible geometrical layouts that enhance mechanical compliance and enable physiological monitoring and integration in curved body structures. [ABSTRACT FROM AUTHOR]

In this study, a hybrid MMC-AABH plus approach is developed for the fast optimal design of shell-graded-infill structures. The key idea is to use a proper description about the graded microstructural infill and the coating shell. To this end, a set of moving morphable components is adopted to represent the boundary of the coating shell, while the graded-infill is embodied by spatially varying orthotropic porous configurations. Under such a treatment, with a small number of design variables, both the boundary of the coating shell and the graded microstructure infill can be optimized simultaneously. Other attractive features of the present study are summarized as follows. First, the smooth variation across the microstructural infill can be automatically satisfied based on the proposed approach compared with other similar methods. Second, with the use of the extreme value principle of Laplace equation, the minimum feature size can be explicitly controlled during the optimization. Finally, compared with other methods in the frontier, the approach proposed in the present study enjoys a considerable reduction in the computation cost and can obtain a near-optimal design of the coating structures. The effectiveness of the proposed approach is further demonstrated with numerical examples. [ABSTRACT FROM AUTHOR]

A novel approach for microstructure reconstruction using artificial intelligence (MR-AI) was proposed to nondestructively measure the through-thickness average stochastic fiber orientation distribution (FOD) in a prepreg platelet molded composite (PPMC) plate. MR-AI approach uses thermal strain components on the surfaces of a PPMC plate as input to the deep learning model, which allows to predict a distribution of local through-thickness average fiber orientation state in the entire PPMC volume. The experimental setup with a heating stage and digital image correlation (DIC) was used to measure thermal strains on the surface of the PPMC plate. Optical microscopy was then used to measure FOD in the cross section of the PPMC plate. FOD measurements from optical microscopy imagery compared favorably with FOD prediction by MR-AI. The proposed methodology opens the opportunity for rapid, nondestructive inspection of manufacturing-induced FOD in molded composites. [ABSTRACT FROM AUTHOR]