1D and 2D carbon nanomaterials such as multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were investigated numerically. The thermophysical properties of water and nanofluids ...

By imploding fuel of hydrogen isotopes, inertial confinement fusion (ICF) aims to create conditions that mimic those in the Sun's core. This is fluid dynamics in an extreme regime, with the ultimate goal of making nuclear fusion a viable clean energy source. The fuel must be reliably and symmetrically compressed to temperatures exceeding 100 million degrees Celsius. After the best part of a century

The environmental setting of the Dead Sea combines several aspects whose interplay creates flow phenomena and transport processes that cannot be observed anywhere else on Earth. As a terminal lake with a rapidly declining surface level, the Dead Sea has a salinity that is close to saturation, so that the buoyancy-driven flows common in lakes are coupled to precipitation and dissolution, and large amounts

This paper aims to investigate the interlaminar shear properties and failure mechanisms of plain woven carbon fabric/polyetheretherketone (CF/PEEK) thermoplastic composites under high strain rate impact loads at different temperatures (25°C, 120°C, 295°C). A reliable hot air flow heating method with SHPB is creatively employed for short beam shear experiments. A multi-scale model was developed to predict

Based on the damage characteristics of multistage shear creep in weak intercalated layers (carbonaceous mud shale) of slopes under the influence of dynamic disturbance, the effective bearing area method was used. A new coupled damage equation (dynamic disturbance damage, shear creep damage, and initial damage) was established through further derivation, and its applicability was demonstrated. The calculation

The deformation and failure of a rock is closely related to the strain energy consumption during the load process of rock. To investigate the effect of water on energy evolution and damage characteristics of dolomite samples from a deep mine, the uniaxial compression tests were carried out on dry and water-saturated dolomite samples at different burial depths (900 m–1200 m). The effects of water on

This study proposes an improved dual shear unified strength model by introducing the plastic internal variable which reflects the collective effects of strain softening, intermediate principal stress and unequal strength under tension and compression. The improved model is then simplified into simple forms for typical stress states, including uniaxial tension and compression, plane stress pure shear

Metastable β titanium alloys are widely applied in many industries. These alloys can have plastic deformation via dislocation slip, twinning, stress-induced martensite (SIM), or a combination of these. These alloys fail in a ductile manner via a process of void nucleation, growth, and coalescence. Inherent defects, such as voids, are commonly attributed to poor mechanical properties. In this study

The water transfer project has alleviated the shortage of water resources, but frequent scheduling of water distribution hubs can harm the water transmission buildings. This article is based on the...

Damage measurement of materials is a crucial challenge for researchers and engineers in manufacturing industries. In this study, based on the image processing technique, a developed approach for determining the Lemaitre’s ductile damage parameter by the direct measurement method is proposed. For this purpose, first, the micrographs pictures are provided by a scanning electron microscope to attain the

In this study, a revised formulation based on the uniform strain method (Flanagan and Belytschko, 1981) and the scaled boundary finite element method (SBFEM) — a numerical method with arbitrarily shaped polyhedral elements — is introduced for three-dimensional elastoplastic analysis. The proposed formulation uses the average strain of each polyhedral element. By employing the octree decomposition algorithm

The daily operations of large hydropower stations on rivers induce frequent variations in downstream water levels and flow velocities, resulting in unsteady and complex hydraulic characteristics at...

This research uses 3D Computational Fluid Dynamics (CFD) simulations to investigate the downhole Natural Gas Separation Efficiency (NGSE) for multiphase pumping wells in the heterogeneous churn flo...

We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel, data-driven framework for learning surrogates for parametrized Partial Differential Equations (PDEs). It consists of a probabilistic, learning objective in which weighted residuals are used to probe the PDE and provide a source of data i.e. the actual PDE never needs to be solved. This is combined with a physics-aware implicit solver

Investigating the brittleness characteristics and damage evolution of deep rock masses under hydromechanical coupling has important significance. The variations in mechanical properties and brittleness characteristics of sandstone under different confining pressures and pore pressures were studied. Based on the stress threshold evolution and energy conversion analysis of the full stress-strain behavior

A variational formulation of the non-smooth contact dynamics method is proposed to address the dynamic response of historical masonry structures modeled as systems of 3D rigid blocks and subjected to ground excitation. Upon assuming a unilateral-frictional contact law between the blocks, the equations of motions are formulated in a time-discrete impulse theorem format in the unknown block velocities

Coronary Artery Disease (CAD) is one of the most common forms of heart disease, caused by a buildup of atherosclerotic plaque in the coronary arteries. When this buildup is extensive, it can result in obstructions in the lumen of the blood vessels (known as stenosis) that lead to insufficient delivery of essential molecules like oxygen to the heart. Fractional Flow Reserve (FFR), defined as the ratio

Time-variant reliability-based robust design optimization (TRBRDO) has achieved certain progress recently for its ability to ensure both robustness of design and feasibility of time-variant probabilistic constraints. However, the existing TRBRDO methods have not specifically addressed the dynamic uncertainty of material degradation, and there is lack of a universal and efficient approach for this class

Aligning lattice infills with the principal stress directions in loaded objects is crucial for improving stiffness. However, this principle only works for a single loading condition, where the stress field in 2D is described by two orthogonal principal stress directions. In this paper, we introduce a novel approach for designing and optimizing triangular lattice structures to accommodate multiple loading

This research proposes a novel method for designing fracture-resistant structures. By analyzing the relationship between tensile strain energy and phase field brittle fracture, it has been found that minimizing tensile strain energy can delay fracture and enhance resistance. Capitalizing on this insight, a new topology optimization method is proposed. This method focuses on minimizing tensile strain

We present a framework for parallel isogeometric boundary element analysis (BEA) of elastic solids on CUDA. To deal with traction discontinuities, we propose a BEA model that supports multiple nodes and semi-discontinuous elements. The multiplicity of a node is defined by the number of regions containing any element influenced by the node. A region is a group of connected elements delimited by a closed

The contact between a rigid Hertzian indenter and an adhesive broad-band viscoelastic substrate is considered. The material behavior is described by a modified power law model, which is characterized by only four parameters, the glassy and rubbery elastic moduli, a characteristic exponent and a timescale . The maximum adherence force that can be reached while unloading the rigid indenter from a relaxed

Predicting stress fluctuations in granular media under steady-state shear loading is crucial for applications ranging from geophysical processes to construction engineering. Stress fluctuations emerge from particle rearrangement, usually enabled by frictional slip and force-chain buckling. Existing models used to predict stress fluctuations are largely phenomenological, often accounting for the force

The superior stiffness-to-weight and strength-to-weight mechanical advantages of sandwich structures can be fully exploited through concurrent design of entire topology, infill configuration and density, where the high-performance yet complicated structure can be fabricated through additive manufacturing. However, the emerging design challenges are concurrent design updating related to sandwich topology

This paper presents an efficient algorithm for the approximation of the rank-one convex hull in the context of nonlinear solid mechanics. It is based on hierarchical rank-one sequences and simultaneously provides first and second derivative information essential for the calculation of mechanical stresses and the computational minimisation of discretised energies. For materials, whose microstructure

The Finite Element method, widely used for solving Partial Differential Equations, may result in suboptimal computational costs when computing smooth fields within complex geometries. In such situations, IsoGeometric Analysis often offers improved per degree-of-freedom accuracy but building analysis-suitable representation of complex shapes is generally not obvious. This paper introduces a non-invasive

This study is focused on a reduced-order model (ROM) based on proper orthogonal decomposition (POD) for unsteady flow around a stationary object, which allows prediction with different object geometry as a parameter. The conventional POD method is applicable only to data with the same computational grid for all snapshots. This study proposed a novel POD methodology that performs on flow snapshots,

Despite the success of various methods in addressing the issue of spatial reconstruction of dynamical systems with sparse observations, spatio-temporal prediction for sparse fields remains a challenge. Existing Kriging-based frameworks for spatio-temporal sparse field prediction fail to meet the accuracy and inference time required for nonlinear dynamic prediction problems. In this paper, we introduce

We consider the simulation of slender structures immersed in a three-dimensional (3D) flow. By exploiting the special geometric configuration of the slender structures, this particular problem can be modeled by mixed-dimensional coupled equations. Taking advantage of the slenderness of the structure and thus considering 3D/1D coupled problems raise several challenges and difficulties. From a mathematical

This study presents a strong form based meshfree collocation method, which is named Continuum Point Cloud Method, to solve nonlinear field equations derived from classical mechanics for deformed bodies in three-dimensional Euclidean space. The method and its implementation are benchmarked against a nonlinear vector field using manufactured solutions. The analysis of mechanical fields firstly focuses

An approach is proposed to accelerate multiscale simulations of heterogeneous quasi-brittle materials exhibiting an anisotropic damage response. The present technique uses unsupervised machine learning classification based on k-means clustering to select integration points in the macro mesh within an FE strategy to track redundant micro nonlinear problems and to avoid unnecessary Representative Volume

Extremal elastic materials here refer to a specific class of elastic materials whose elastic matrices exhibit one or more zero eigenvalues, resulting in soft deformation modes that, in principle, cost no energy. They can be approximated through artificially designed solid microstructures. Extremal elastic materials have exotic bulk wave properties unavailable with conventional solids due to the soft

We model the constitutive equation for nonlinear electro-viscoelastic transversely isotropic solids with short term memory via a generalized strain method, where the method is a change with respect to the methods that have been done in the last decades regarding mechanics of nonlinear solids. Our generalized strain model uses spectral invariants with a clear physical interpretation and hence they are

Despite advancements in predicting the constitutive relationships of composite materials, characterizing the effects of microstructural randomness on their mechanical behaviors remains challenging. In this study, we propose a data-driven convolutional neural network (CNN) to efficiently predict the stress-strain curves containing three key material features (Tensile strength, modulus, and toughness)

In the field of damage identification, traditional regularization methods neglect the impact of uncertainty factors on the selection of regularization parameters, leading to a decrease in the accuracy of damage identification. Therefore, this study proposes a damage identification based on interval truncated singular value decomposition (DI-ITSVD) method that considers the uncertainty in the selection

The aim of this work is to develop a neural network for modelling incompressible hyperelastic behaviour with isotropic damage, the so-called Mullins effect. This is obtained through the use of feed-forward neural networks with special attention to the architecture of the network in order to fulfil several physical restrictions such as objectivity, polyconvexity, non-negativity, material symmetry and

In this work a generalized phase-field cohesive zone model () is proposed within the framework of the unified phase-field theory for brittle and cohesive fracture. With the introduction of an extra dissipation function for the crack driving force, in addition to the geometric function for the phase-field regularization and the degradation function for the constitutive relation, theoretical and application

The vast changes in temperature are what produce thermal fatigue damage to concrete. In this study, concrete specimens in three different categories—C20, C40, and C60—are tested for thermal fatigue at temperatures ranging from 10°C to 80°C in an atmosphere with constant relative humidity. Utilizing ultrasonic nondestructive testing, the elastic modulus of concrete is determined. After thermal cycling

The mechanical behavior under static and fatigue loading induced by mechanical forces is examined in this article through the utilization of a non-destructive methodology. However, it is worth noting that the dynamics of elastic waves become notably more intricate when dealing with composite materials. In order to provide a comprehensive description of the green flax/epoxy system, a crucial component

Accurately and efficiently achieving the uncertainty quantification of engineering structures is a challenging issue. The direct probability integral method (DPIM) provides an effective pathway to address this issue. However, the key partition technique via Voronoi cell of DPIM requires a prohibitive computational burden for multi-dimensional probability space. Moreover, due to the distributed nonuniformity

Shear wave elastography (SWE) is an innovative method that allows for the nondestructive and quantitative characterization of muscular mechanical properties. This method finds extensive utility in fields such as sports medicine, sports rehabilitation, and the diagnosis of muscle-related ailments. Existing studies have demonstrated the promise of SWE in probing intramuscular pressure (IMP), a factor

Our understanding of respiratory flow phenomena has been consolidated over decades with the exploration of in vitro and in silico canonical models that underscore the multiscale fluid mechanics spanning the vast airway complex. In recent years, there has been growing recognition of the significant intersubject variability characterizing the human lung morphometry that modulates underlying canonical

Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural

This study presents a unified formulation of topology optimization with a finite strain nonlocal damage model using the continuous adjoint method. For the primal problem to describe the material response including deterioration, we consider the standard Neo–Hookean constitutive model and incorporate crack phase-field theory for brittle fracture within the finite strain framework. For the optimization

Projection-based reduced-order models (ROMs) play a crucial role in simplifying the complex dynamics of fluid systems. Such models are achieved by projecting the Navier-Stokes equations onto a lower-dimensional subspace while preserving essential dynamics. However, this approach requires prior knowledge of the underlying high-fidelity model, limiting its effectiveness when applied to black-box data

Topology optimization commonly encounters several challenges, such as ill-posedness, grayscale issues, interdependencies among design variables, , and . Furthermore, addressing the latter two concurrently presents considerable difficulty. In this study, we introduce a framework aimed at mitigating all the above obstacles . The objective is to achieve optimal configurations in a notably reduced timeframe

Reliability-based design optimization (RBDO) provides a promising approach for achieving effective structural designs while explicitly accounting for the effects of uncertainty. However, the computational demands associated with RBDO, often due to its nested loop nature, pose significant challenges, thereby impeding the application of RBDO for decision-making in real-world structural design. To alleviate

This work presents an adaptive phase-field cohesive zone model (PF-CZM) for simulating mixed-mode crack nucleation and growth in isotropic rock-like materials subjected to thermo-mechanical interactions. The proposed approach combines an adaptive multi-patch isogeometric analysis (MP-IGA) and length-scale insensitive PF-CZM. The formulation captures the distinct critical energy release rates for Mode-I

Recently, the high-resolution topology optimization to promote engineering applicability has gained much more attentions. However, an accurate and highly-efficient design framework for implementing shape and topology optimization of engineering structures with integration of CAD model is still in demand. In the current work, the critical intention is to develop a CAD-oriented parallel-computing design

Hybrid-dimensional simulations can balance simulation accuracy and computational costs. However, applying hybrid-dimensional simulations to flight status remains challenging due to weak convergence...

Modeling of subcutaneous injections in soft adipose tissue – a common way to administer pharmaceutical medication – is a challenging multiphysics problem which has recently attracted the attention of the engineering community, as it could help optimize medical devices and treatments. The underlying continuum mechanics of this process is complex and involves finite strain poro-mechanics – where a viscous

In the phase-field modeling of fracture, the search for a physically reasonable and computationally feasible criterion to split the elastic energy density into fractions that may or may not contribute to crack propagation has been the subject of many recent studies. Within this context, we propose an energy split – or energy decomposition – aimed at accurately representing the evolution of a crack

We introduce a novel Fictitious Domain (FD) unfitted method for interface problems associated with a second-order elliptic linear differential operator, that achieves optimal convergence without the need for adaptive mesh refinements nor enrichments of the Finite Element spaces. The key aspect of the proposed method is that it extends the solution into the fictitious domain in a way that ensures high

Uncertainty quantification is a critical aspect of machine learning models for material property predictions. Gaussian Process Regression (GPR) is a popular technique for capturing uncertainties, but most existing models assume homoscedastic aleatoric uncertainty (noise), which may not adequately represent the heteroscedastic behavior observed in real-world datasets. Heteroscedasticity arises from

This work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace

While shape optimization using isogeometric shells exhibits appealing features by integrating design geometries and analysis models, challenges arise when addressing computer-aided design (CAD) geometries comprised of multiple non-uniform rational B-splines (NURBS) patches, which are common in practice. The intractability stems from surface intersections within these CAD models. In this paper, we develop

We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quantification (UQ) tasks. The proposed method possesses a unique capability: it seamlessly integrates SciML into UQ and vice versa, which allows it to quantify the uncertainties in SciML tasks effectively and leverage SciML

A generalized variational principle with five independent variables is proposed for strain gradient elasticity, including displacement, strain, strain gradient, stress, and double stress. Based on the principle, a one-point integration scheme is designed for the second order meshfree Galerkin method through nodal smoothed derivatives and their high order derivatives by Taylor's expansion. Since the

This work utilizes the modern synergy between flexible, rapid, simulations and quick assimilation of data in order to develop next-generation tools for precise biomass management of large-scale agricultural and forestry systems. Additionally, when integrated with satellite and drone-based digital elevation technologies, the results lead to digital replicas of physical systems, or so-called digital-twins