Search Results (24)

This study investigates the nonlinear dynamic response and chaos evolution of a shape memory alloy hybrid composite (SMAHC) actuator beam under coupled thermal, harmonic, and aerodynamic excitations. A reduced-order nonlinear dynamic model was developed by combining Euler–Bernoulli beam theory, von Karman geometric nonlinearity, the Brinson SMA constitutive relation, and first-order piston-theory aerodynamics. The governing equations were derived from Hamilton’s principle, discretized by the weighted residual method, and solved using the Newmark-beta algorithm. Chaotic evolution was quantified using a largest Lyapunov exponent-based chaos intensity indicator rather than the exact Kolmogorov–Sinai entropy. The reduced-order model was compared with ABAQUS finite element simulations under representative coupled aerodynamic and harmonic loading. The MATLAB prediction and ABAQUS response gave a dominant frequency of approximately 9.50 Hz, close to the prescribed excitation frequency of 9.55 Hz, with peak displacement amplitudes of approximately 0.0285 mm and 0.0324 mm, respectively. A supplementary ABAQUS modal-frequency separation check supported the use of the two-mode reduced-order model for the dominant low-frequency response, while also clarifying its limitation for high-dimensional chaotic modal interactions. The parametric results showed that an increasing excitation amplitude and aerodynamic load promoted frequency broadening and chaotic transitions. The Lyapunov-based indicator rose near γ = 65 under λ* = 100 and near λ* = 328 under γ = 30. Temperature-dependent SMA recovery stress further shifted the transition threshold by modifying the effective stiffness and internal restoring action of the beam. These results provide a reduced-order framework for interpreting nonlinear response transitions in SMAHC actuator beams in thermo-aeroelastic environments. Full article

Recent advances in deep learning and artificial intelligence (AI) have significantly transformed the analysis of rock slopes and geotechnical structures. Rock slope stability is governed by complex interactions among rock mass discontinuity networks, mechanical properties, environmental loading conditions, and stress redistribution. Traditional analytical and numerical methods, including discrete element methods, finite element simulations, and limit equilibrium approaches, provide valuable insights; however, they often have limitations in capturing complex failure mechanisms and handling heterogeneous datasets. This review systematically synthesizes recent developments in AI-driven approaches for rock slope engineering, with particular emphasis on their integration with physical and numerical modeling frameworks and their role in improving the performance assessment of geotechnical systems. Key applications include machine learning-based slope stability prediction, automated discontinuity detection, surrogate modeling for numerical simulations, and spatiotemporal forecasting of slope deformation using monitoring data. The review further discusses emerging approaches such as physics-informed machine learning, digital twin systems, and hybrid AI–numerical frameworks, which combine data-driven learning with established rock mechanics principles. In addition, the potential of AI technologies to support sustainable rock slope management is evaluated, including early warning systems, optimal stabilization design, and resilient infrastructure monitoring. Finally, major challenges related to data quality, model interpretability, uncertainty, and integration with physical models are identified. The review suggests that future research should focus on integrating AI with physics-based modeling and uncertainty quantification, supported by rigorous validation strategies and high-quality datasets, to improve reliability and practical applicability in rock slope engineering. This paper provides a comprehensive perspective on how AI and deep learning can improve the understanding, prediction, and long-term management of rock slopes in modern geotechnical engineering practice. Full article

Structural health monitoring (SHM) of machinery represents a topic of significant engineering interest. In machine design, the development of diagnostic systems capable of tracking structural integrity during operation is essential, provided that such systems do not interfere with or compromise the machine’s performance. This paper presents a SHM designed to reconstruct, in real time, the full deformation field of a machine from discrete measurements collected at a limited number of locations. The proposed approach combines modal reconstruction techniques with mode selection criteria, further enhanced by the integration of a supervised machine learning classifier. This hybrid framework enables the continuous reconstruction of the structural deformation at each timestep of the measured signal. The methodology is validated on a benchmark geometry, a rectangular plate, by comparing finite element simulations with reconstructions obtained through the proposed modal–machine learning strategy. Results demonstrate the capability of the approach to approximate the global deformation with high fidelity from sparse measurements, establishing a foundation for the extension of the method to more complex machine geometries. The study highlights the potential of integrating data-driven and physics-based techniques for SHM, paving the way for future diagnostic tools in advanced mechanical systems. Full article

This paper presents a computational study of wave–bathymetry and wave–structure interaction problems using advanced numerical techniques based on high-fidelity, two-phase Navier–Stokes (TpNS) flow and reduced-order, fully nonlinear potential flow models. For high-fidelity simulations, the TpNS equations are discretized using the finite-element method, with free-surface evolution captured through a hybrid level-set (LS) and volume-of-fluid (VOF) formulation. A monolithic, phase-conservative LS equation is introduced to mitigate mass loss and interface smearing, combined with a semi-implicit projection scheme. Hydrodynamic forces are resolved using a high-order, phase-resolving cut finite-element method (CutFEM), which enables the representation of complex solid geometries within a fixed background mesh. An equivalent polynomial of Heaviside and Dirac distributions ensures accurate evaluation of surface and volume integrals. Hence, no explicit generation of cut cell meshes, adaptive quadrature, or local refinement is required. For reduced-order modeling, a fast regularized boundary integral method (RBIM) is employed to solve the fully nonlinear potential flow. Singular and near-singular integrals are treated using a subtract-and-addition technique based on auxiliary functions derived from Stokes’ theorem, allowing direct application of high-order quadrature without conventional boundary element discretization. An arbitrary Lagrangian–Eulerian (ALE) formulation is adopted to enforce free-surface boundary conditions while avoiding excessive mesh distortion. The proposed approaches are applied to investigate highly nonlinear wave transformation over complex bathymetry and wave-induced dynamics of floating structures, including eddy-making damping effects. Numerical results are validated against experimental measurements. These two modeling approaches represent complementary levels of physical fidelity and computational efficiency, and their systematic comparison clarifies the trade-offs between computational accuracy, efficiency, and cost for practical marine problems. Full article

This study analyzes a hybrid computational framework that combines peridynamics (PD) and the finite element (FE) method to model wave propagation in a one-dimensional bar, focusing on their integration for enhanced accuracy and efficiency. The analysis investigates PD’s ability to capture non-local interactions in regions near loading points, with computationally efficient coarse discretization in other areas through finite element methods. The dynamic response to symmetric and asymmetric axial loading, including loading and unloading phases, is analyzed through time-dependent external forces, solving displacement, velocity, and acceleration fields at each time step. The effects of PD-specific parameters, such as the horizon size, and the FE–PD node spacing size ratios on the performance of the hybrid model in wave propagation are investigated. Additionally, the study examines the von Neumann stability for PD to ensure stability and reliability, offering a robust framework for integrating PD and FE in dynamic analyses. Full article

We present a time-dependent framework that combines a hybrid basis, consisting of Gaussian-type orbitals (GTOs) and finite-element discrete-variable representation (FEDVR) functions, with a multicenter grid to simulate strong-field and attosecond dynamics in atoms and molecules. The method incorporates the construction of the orthonormal hybrid basis, the evaluation of electronic integrals, a unitary time-propagation scheme, and the extraction of optical and photoelectron observables. Its accuracy and robustness are benchmarked on one-electron systems such as atomic hydrogen and the dihydrogen cation (${\mathrm{H}}_2^{+}$) through comparisons with essentially-exact reference results for bound-state energies, high-harmonic generation spectra, photoionization cross sections, and photoelectron momentum distributions. This work establishes the groundwork for its integration with quantum-chemistry methods, which is already operational but will be detailed in future work, thereby enabling ab initio simulations of correlated polyatomic systems in intense ultrafast laser fields. Full article

The dynamic fracture of rocks under impact loads has many engineering applications such as rock blasting. This study reviews the recent achievements of investigating rock dynamic fracturing and its application in rock blasting using computational mechanics methods and highlights the prospects of modelling them with a hybrid finite-discrete element method (HFDEM) originally developed by the authors. The review first summarizes the peculiarities of rock dynamic fracturing compared with static fracturing, which are that the physical-mechanical properties of rocks, including stress wave propagation, strength, fracture toughness, energy partition and cracking mechanism, depend on loading rate. Then the modelling of these peculiarities and their applications in rock blasting using fast developing computational mechanics methods are reviewed with a focus on the advantages and disadvantages of prevalent finite element method (FEM) as representative continuum method, discrete element method (DEM) as representative discontinuum method and combined finite-discrete element (FDEM) as representative hybrid method, which highlights FDEM is the most promising method for modelling rock dynamic fracture and blasting application as well as points out the research gaps in the field of modelling the dynamic fracture of rocks under impact loads. After that, the progress of shortening some of these gaps by developing and applying HFDEM, i.e., the authors’ version of FDEM, for modelling rock dynamic fracture and applications in rock blasting are reviewed, which include the features of modelling the effects of loading rate; stress wave propagation, reflection and absorbing as well as stress wave-induced fracture; explosive-rock interaction including detonation-induced gas expansion and flow through fracturing rock; coupled multiaxial static and dynamic loads; heterogeneous rock and rock mass with pre-existing discrete fracture network; and dynamic fracturing-induced fragment size distribution. Finally, the future directions of modelling the dynamic fracture of rocks under impact loads are highlighted and a systematic numerical approach is proposed for modelling rock blasting. Full article

We propose a hybrid numerical framework for solving time-fractional Navier–Stokes equations with nonlinear damping. The method combines the finite difference L1 scheme for time discretization of the Caputo derivative ($0<\alpha <1$) with mixed finite element methods (P1b–P1 and $P_2$–$P_1$) for spatial discretization of velocity and pressure. This approach addresses the key challenges of fractional models, including nonlocality and memory effects, while maintaining stability in the presence of the nonlinear damping term ${\gamma \left|\mathbf{u}\right|}^{r-2}\mathbf{u}$, for $r\ge 2$. We prove unconditional stability for both semi-discrete and fully discrete schemes and derive optimal error estimates for the velocity and pressure components. Numerical experiments validate the theoretical results. Convergence tests using exact solutions, along with benchmark problems such as backward-facing channel flow and lid-driven cavity flow, confirm the accuracy and reliability of the method. The computed velocity contours and streamlines show close agreement with analytical expectations. This scheme is particularly effective for capturing anomalous diffusion in Newtonian and turbulent flows, and it offers a strong foundation for future extensions to viscoelastic and biological fluid models. Full article

Gas turbines in land-based microgrids and shipboard-isolated power grids frequently face operational challenges, such as the startup and shutdown of high-power equipment and sudden load fluctuations, which significantly impact their performance. To examine the dynamic behavior of gas turbines under transitional operating conditions, a three-dimensional computational fluid dynamic simulation is employed to create a model of the gas turbine rotor, incorporating thermal inertia, which is then analyzed in conjunction with three-dimensional finite element methods. The governing equations of the flow field are discretized, providing results for the flow and temperature fields throughout the entire flow path. A hybrid approach, combining temperature differences and heat flux density, is applied to set the thermal boundary conditions for the walls, with the turbine’s operational state determined based on the direction of heat transfer. Additionally, mesh division techniques and turbulence models are selected based on the geometric dimensions and operating conditions of the compressor and turbine. The simulation results reveal that thermal inertia induces a shift in the dynamic characteristics of the rotor components. Under the same heat transfer conditions, variations in rotational speed have a minimal impact on the shift in the characteristic curve. The working fluid temperature inside the compressor components is lower, with a smaller temperature difference from the wall, resulting in less intense heat transfer compared to the turbine components. Overall, heat transfer accounts for only about 0.1% of the total enthalpy at the inlet. When heat exchange occurs between the working fluid and the walls, around 6–15% of the exchanged heat is converted into changes in technical work, with this percentage increasing as the temperature difference rises. Full article

Ground-penetrating radar (GPR) has been widely used for subsurface detection and testing. Numerical simulations of GPR signal are commonly performed to aid the interpretation of subsurface structures and targets in complex environments. To enhance the accuracy of GPR simulations on heterogeneous medium, this paper proposes a hybrid modeling method that combines the discrete element method with a component fusion strategy (DEM–CFS). Taking the asphalt pavement as an example, three 3D stochastic models with distinctly different porosities are constructed by the DEM–CFS method. Firstly, the DEM is utilized to establish the spatial distribution of random coarse aggregates. Then, the component fusion strategy is employed to integrate other components into the coarse aggregate skeleton. Finally, the GPR response of the constructed asphalt models is simulated using the finite-difference time-domain method. The proposed modeling method is validated through both numerical and laboratory experiments and demonstrates high precision. The results indicate that the proposed modeling method has high accuracy in predicting the dielectric constant of heterogeneous media, as generated models are closely aligned with real-world conditions. Full article

A hybrid enhanced inverse finite element method (E-iFEM) is proposed for real-time intelligent sensing of discontinuous aerospace structures. The method can improve the flight performance of intelligent aircrafts by feeding back the structural shape information to the control system. Initially, the presented algorithm combines rigid kinematics with the classical iFEM to discretize the aerospace structures into elastic parts and rigid parts, which will effectively overcome structural complexity due to fluctuating bending stiffness and a special aerodynamic section. Subsequently, the rigid parts provide geometric constraints for the iFEM in the shape reconstruction method. Meanwhile, utilizing the Fiber Bragg grating (FBG) strain sensor to obtain real-time strain information ensures lightweight and anti-interference of the monitoring system. Next, the strain data and the geometric constraints are processed by the iFEM for monitoring the full-field elastic deformation of the aerospace structures. The whole procedure can be interpreted as a piecewise sensing technology. Overall, the effectiveness and reliability of the proposed method are validated by employing a comprehensive numerical simulation and experiment. Full article

In this study, a method for the active shape control for carbon fiber-reinforced polymer (CFRC) reflectors using piezoelectric lead zirconate titanate (PZT) actuators is proposed. According to this method, a finite element model considering higher transverse shear deformation with independent voltage degrees of freedom is formulated by the Hamilton principle. An optimal shape controller that minimizes the discrete root mean square (RMS) error of a reflecting surface is applied. Then, the optimal arrangements of the PZT actuators are determined by numerical optimization methods, which are developed by modifying the classical Genetic Algorithm, with both single and multi-objective optimizations being studied. In the single optimization formulation, the number of actuators is considered as a constraint, and the RMS error of the reflector is regarded as the optimizing target. A hybrid method that combines the gradient projection method with an adaptive Genetic Algorithm is proposed to solve this problem. In the multi-objective optimization, the residual RMS errors and power consumption of the actuators are considered as the optimization targets. Pareto optimal solutions are obtained by an improved multi-objective Genetic Algorithm. Numerical examples are provided to show the effectiveness of the proposed methods. Full article

This work presents a novel methodology for the accurate and efficient elastic deformation reconstruction of thin-walled and stiffened structures from discrete strains. It builds on the inverse finite element method (iFEM), a variationally-based shape-sensing approach that reconstructs structural displacements by matching a set of analytical and experimental strains in a least-squares sense. As iFEM employs the finite element framework to discretize the structural domain and as the displacements and strains are approximated using element shape functions, the kind of element used influences the accuracy and efficiency of the iFEM analysis. This problem is addressed in the present work through a novel discretization scheme that combines beam and shell inverse elements to develop an iFEM model of the structure. Such a hybrid discretization paradigm paves the way for more accurate shape-sensing of geometrically complex structures using fewer sensor measurements and lower computational effort than traditional approaches. The hybrid iFEM is experimentally demonstrated in this work for the shape sensing of bending and torsional deformations of a composite stiffened wing panel instrumented with strain rosettes and fiber-optic sensors. The experimental results are accurate, robust, and computationally efficient, demonstrating the potential of this hybrid scheme for developing an efficient digital twin for online structural monitoring and control. Full article

Metal fiber hybrids (MFH) exhibit outstanding mechanical properties. They combine the advantages of ductile metallic materials with the well-known advantages of classical glass or carbon fibers in polymer matrices. Previous research has shown that these hybrid material concepts can improve structural integrity and energy absorption while maintaining their excellent weight-specific mechanical properties as well as allowing a wider range of multifunctional applications. In today’s component design process, simulation is a powerful tool for engineers to exploit the full mechanical potential of the material used. However, describing the material behavior including its multifunctional usability in numerically aided design processes of components is currently one of the major challenges for MFH. Against this background, this work focuses on the development and evaluation of a description method for MFH in the finite element analysis (FEA). A steel and carbon fiber reinforced epoxy resin (SCFRP) with hybridization at the laminate level is chosen as the reference material. To describe the behavior of unidirectional steel fiber reinforced plastic (SFRP) layers, a material model combining an orthotropic damage model and a 1D-plasticity model is proposed and implemented as a user-defined subroutine for LS-Dyna. In addition, SCFRP laminates are manufactured, tested under tensile loading, and used to parameterize the material models and to validate the description method for SCFRP. In this study, it is shown that the description method in combination with the newly developed material model is able to describe the complex failure mechanism of SCFRP. In particular, with respect to the material behavior up to the failure of the carbon fibers, a very good mapping accuracy can be achieved. Strain localization effects occur in both numerically predicted and experimentally measured post-failure behavior. Therefore, it could be concluded that the accuracy of the numerical predictions strongly depends on the geometric resolution of the discretization. Full article

In this paper, we consider the poroelasticity problem in heterogeneous media. The mathematical model is described by a coupled system of equations for displacement and pressure in the coupled dual continuum porous media. We propose a new method based on hybrid explicit–implicit (HEI) learning to solve the poroelasticity problem in dual continuum heterogeneous media. We use a finite element method with standard linear basis functions for spatial approximation. We apply the explicit–implicit time scheme, where the explicit scheme is used for the low-conductive continuum and the implicit scheme for the high-conductive. The fixed-strain splitting scheme is used to accelerate the computation and decouple the flow and mechanics problems. The main idea of the proposed method is partial learning of particular degrees of freedom of the high-conductive continuum’s pressure (implicit part of the flow). First, we train a deep neural network (DNN) to obtain values of the implicit part of the flow at some spatial points at some time moments. Then, we apply the Discrete Empirical Interpolation Method (DEIM) combined with Proper Orthogonal Decomposition (POD) to restore the complete implicit parts and perform linear interpolation over time. Consequently, we treat the high-conductive continuum’s pressure as a known function and use it to find the other continuum’s pressure and displacements. Numerical results for the two-dimensional model problem are presented. The results demonstrate that the proposed method provides fast and accurate predictions. Full article