Search Results (589)

Large-scale frames are increasingly used in engineering structures, particularly in aerospace structures. Among them, planar phased array satellite antennas used for global observations and target tracking have received much attention. Considering that structural deformation will degrade the coherence of antennas, a frame with inherent diagonal cables that serves to control the antennas’ static configuration is thoroughly studied. These inherent cables of planar phased arrays are pre-tensioned to preserve the structural integrity and increase the stiffness of the antenna. However, they are also used as actuators in our research; in this way, additional control devices are not needed. As a result, the antenna’s mass will decrease, and its reliability will increase. For high observation accuracy, the antennas tend to be very large. Accordingly, there is a significant deformation of space antennas when they are loaded. For this reason, a nonlinear finite element method is used to consider the structures’ geometrical nonlinearity. In order to achieve shape adjustment, the difference between active and passive cables must be carefully investigated. Furthermore, for the nonlinear structure in this paper, the active cables will deform in tandem with the structure as a whole so that the direction of the active cables’ control forces will also change during the entire control process. This paper elaborates on this problem and proposes a nonlinear optimization method considering this characteristic of the cables. Simulations of a simplified 2-bay and 18-bay satellite antenna are performed to validate the proposed method. Results of the numerical simulation demonstrate that the proposed method can successfully adjust the large-scale antenna’s static shape and achieve high precision. Full article

Aerostatic bearings incorporating micro-hole restrictors with diameters on the order of tens of microns demonstrate superior static and dynamic stiffness characteristics, while significantly reducing air consumption, and are increasingly adopted in precision engineering applications. This paper investigates the modelling of aerostatic bearings with micro-hole restrictors. First, a refined discharge coefficient formula is developed, incorporating the orifice length-to-diameter ratio effect using the computational fluid dynamics (CFD) simulation results on a centrally fed circular aerostatic bearing. A numerical solution scheme is proposed using the developed discharge coefficients to enable more accurate and efficient prediction of the bearing performance and flow characteristics. Finally, the proposed numerical approach is implemented using the finite difference method (FDM) and demonstrated through a circular thrust air bearing case study. The results are validated against both CFD simulations and experimental measurements, showing excellent agreement and confirming the reliability of the FDM-based numerical model. Numerical and experimental investigations consistently demonstrate that micro-hole-restricted air bearings can achieve both high load capacity and high stiffness, having the potential for application in more complex air bearing designs and systems. Full article

In this work, a high-order meshless framework is developed for the numerical resolution of the temporal–fractional Black–Scholes equation arising in option pricing with long-memory effects. The spatial discretization is carried out with a local radial basis function produced finite difference (RBF–FD) method on seven-node stencils. Analytical differentiation weights are constructed by employing closed-form second integrations of a variant of the inverse multiquadric kernel, which yields sparse differentiation matrices. Explicit formulas are derived for both first- and second-order operators, and a detailed truncation error analysis confirms sixth-order convergence in space. Numerical experiments for European options discuss better accuracy per spatial node than standard finite difference schemes. Full article

Extrusion of AlZnMgCu alloys is associated with a very high plastic resistance of the materials at forming temperatures and significant friction resistance, particularly at the contact surface between the ingots and the container. In technological practice, this translates into high maximum extrusion forces, often close to the capacity of hydraulic presses, and the occurrence of surface cracking of extruded profiles, resulting in a reduction in metal exit speed (production process efficiency). The accuracy of mathematical material models describing changes in the plastic stress of a material as a function of deformation, depending on the forming temperature and deformation speed, plays a very important role in the numerical modelling of extrusion processes using the finite element method (FEM). Therefore, three mathematical material models of the tested aluminium alloy were analysed in this study. In order to use the results of plastometric tests determined on the Gleeble device, they were approximated with varying degrees of accuracy using the Hnsel–Spittel equation and then implemented into the material database of the QForm-Extrusion^® programme. A series of numerical FEM calculations were performed for the extrusion of Ø50 × 3 mm tubes made of 7075 aluminium alloy using chamber dies for two different billet heating temperatures, 480 °C and 510 °C, and for three different material models. The metal flow was analysed in terms of geometric stability and dimensional deviations in the wall thickness of the extruded tube and its surface quality, as well as the maximum force in the extrusion process. Experimental studies of the industrial extrusion process of the tubes, using a press with a maximum force of 28 MN and a container diameter of 7 inches, confirmed the significant impact of the accuracy of the material model used on the results of the FEM numerical calculations. It was found that the developed material model of aluminium alloy 7075 number 1 allows for the most accurate representation of the actual conditions of deformation and quality of extruded tubes. Moreover, the material data obtained on the Gleeble simulator made it possible to determine the limit temperature of the extruded alloy, above which the material loses its cohesion and cracks appear on the surface of the extruded profiles. Full article

In the immediate aftermath of the Big Bang, the universe existed in an extremely hot, dense state in which particle interactions occurred not in vacuum but within a thermal medium. Under such conditions, the standard framework of quantum field theory (QFT) requires a finite-temperature extension, wherein propagators—and hence the fundamental structure of the theory—are modified to reflect thermal background effects. These thermal modifications are central to understanding the nature of electroweak symmetry breaking (EWSB) as a high-temperature phase transition, potentially leading to qualitatively different vacuum structures for the Higgs field as the universe cooled. Finite-temperature corrections naturally regulate ultraviolet divergences in propagators, hinting at a possible route toward ultraviolet completion. However, these same thermal effects exacerbate infrared pathologies and can lead to imaginary contributions to the effective potential, particularly when analyzing metastable or multi-vacuum configurations. Additional theoretical challenges, such as gauge dependence and renormalization scale ambiguity, further obscure the precise characterization of the electroweak phase transition—even in minimal extensions of the Standard Model (SM). This review presents the theoretical foundations of finite-temperature QFT with an emphasis on how different field species respond to thermal effects, identifying the bosonic sector as the primary source of key theoretical subtleties. We focus particularly on the scalar extension of the SM, which offers a compelling framework for realizing first-order electroweak phase transitions, electroweak baryogenesis, and accommodating dark matter candidates depending on the underlying ${\mathbb{Z}}_2$ symmetry structure. Full article

By solving the three-dimensional Schrödinger equation with a second-order implicit Finite Difference Method (FDM), the combined effects of temperature, morphology, hydrostatic pressure, and transverse electric field on the nonlinear optical rectification (NOR) of GaAs/Al_εGa_1−εAs elliptical quantum rings are examined. The NOR amplitude is twelve times enhanced and a noticeable blue shift is induced in the THz region when the electric field is increased. Consequently, with the electric field of 1 × 10⁵ V/m, the NOR magnitude achieves its maximum value of 17.116 × 10⁵ m/V. Additionally, when the electric field is aligned along one side of the system’s in-plane cross-section, the strongest amplification takes place. However, with corresponding spectrum shifts, the NOR intensity rises with temperature and falls with hydrostatic pressure. Additionally, changing the transverse profile of the quantum ring from triangular to parabolic broadens the carrier wave functions and has a considerable impact on the NOR coefficient. These findings provide important information for the construction of high-performance, tunable THz optoelectronic devices by demonstrating effective external and structural tuning of NOR. Full article

The present work focuses on the inverse identification of loads acting on wing-like geometries, through strain measurements. These loads are considered quasi-static and considered acting at discrete stations across the span of the wing. A demonstrative case study is investigated, focusing on a complex composite structure, an Unmanned Aerial Vehicle (UAV) fin. To achieve this, a high-fidelity Finite Element model is developed, with “virtual” strain data generated through simulations. The technical challenge of optimal sensor placement is addressed with D-optimal designs, which promise sensor networks (sensor locations and strain components) that produce minimal uncertainty propagation from strain measurements to load estimates. These designs are obtained through the implementation of Genetic Algorithms. Different sensor networks with an increasing number of sensors are evaluated in order to identify a further reduction in epistemic uncertainty posed by the problem’s ill-conditioned nature. Full article

Plasmonic nanohole arrays (NAs) integrating Au and hydrogen-responsive Mg enable dynamic spectral tuning via the Mg → MgH₂ transition. Using finite-difference time-domain (FDTD) numerical simulations, we systematically investigate how layer sequence, Au/Mg ratio, total thickness, and stacking number govern extraordinary optical transmission (EOT) resonances. Mg–Au–Mg architectures exhibit the strongest hydrogen response, delivering resonance shifts up to 275 nm and FoM > 1, owing to direct plasmon–hydride coupling at surface Mg layers. Varying the Au/Mg ratio reveals a trade-off: Mg-rich stacks maximize spectral tunability but suffer from broadened, unstable resonances, while Au-rich stacks sustain sharp modes with limited sensitivity; optimal performance arises at intermediate compositions. Thickness dependence shows that ultrathin films (<50 nm) achieve giant shifts (>600 nm) with high contrast, whereas thicker multilayers lose responsiveness. Finally, stacking analysis uncovers an odd–even effect, with Mg-terminated arrays providing larger shifts than Au-terminated ones. These results establish design rules for hydrogen plasmonic sensors, emphasizing resonance engineering through rational layer ordering and composition control. Full article

This paper aims to explore the numerical solution of non-linear fractional-order Burgers’-Huxley equation based on Caputo’s formulation of fractional derivatives. The equation serves as a versatile tool for analyzing a wide range of physical, biological, and engineering systems, facilitating valuable insights into nonlinear dynamic phenomena. The fractional operator provides a comprehensive mathematical framework that effectively captures the non-locality, hereditary characteristics, and memory effects of various complex systems. The approximation of temporal differential operator is carried out through finite difference based L1 scheme, while spatial discretization is performed using modified cubic B-spline basis functions. The stability as well as convergence analysis of the approach are also presented. Additionally, some numerical test experiments are conducted to evaluate the computational efficiency of a modified fourth-order cubic B-spline (M43BS) approach. Finally, the results presented in the form of tables and graphs highlight the applicability and robustness of M43BS technique in solving fractional-order differential equations. The proposed methodology is preferred for its flexible nature, high accuracy, ease of implementation and the fact that it does not require unnecessary integration of weight functions, unlike other numerical methods such as Galerkin and spectral methods. Full article

This paper presents a finite difference approach for solving the time-fractional Burgers’ equation, which is a model for nonlinear flow with memory effects. The method leverages the $L1-2$ formula for the fractional derivative and provides a novel linearization strategy to efficiently transform the system into a stable linear problem. Rigorous analysis establishes the existence, uniqueness, and pointwise-in-time convergence of the numerical solution in the $L_2$ norm. The proposed formulation achieves second-order time accuracy and fourth-order spatial accuracy under smooth initial conditions, with numerically verified temporal convergence rates of $O({\tau }^{1+\alpha }+{\tau }^2{t}_n^{\alpha -2})$ for solutions with weak singularities. Critically, numerical findings demonstrate that the method is robust and highly efficient, offering high-resolution solutions at a substantially lower computational cost than equivalent graded-mesh formulations. Full article

Numerous experimental and numerical studies have extensively investigated the performance of reinforced deep beams made with natural coarse aggregate concrete. However, limited research has been carried out on reinforced deep beams made of concrete with coarse aggregate from recycled materials and steel fibers. The main goal of this research is to create an accurate finite element model that can mimic the behavior of deep beams using concrete with recycled coarse aggregate and different ratios of steel fibers. The suggested model represents the pre-peak, post-peak, confinement, and concrete-to-steel fiber bond behavior of steel fiber concrete, reinforcing steel, and loading plates by incorporating the proper structural components and constitutive laws. The deep beams’ nonlinear load–deformation behavior is simulated in displacement-controlled settings. In order to verify the model’s correctness, the ultimate loading capacity, load–deflection relationships, and failure mechanisms are compared between numerical predictions and experimental findings. The comparison outcomes of the performance of the beams demonstrate that the numerical model effectively predicts the behavior of deep beams constructed with recycled coarse aggregate concrete. The findings of the experiment and the numerical analysis exhibit a high degree of convergence, affirming the model’s capability to accurately simulate the performance of such beams. In light of how accurately the numerical predictions match the experimental results, an extensive parametric study is conducted to examine the impact of parameters on the performance of deep beams with different ratios of steel fibers, concrete compressive strength, type of steel fibers (short or long), and effective span-to-effective depth ratio. The effect of each parameter is examined relative to its effect on the fracture energy. Full article

Nanostructured targets are increasingly used as key components in high-power laser–matter interaction experiments due to their ability to substantially enhance laser absorption, increase ion/electron generation, or boost the secondary radiation (THz, X-ray, etc.) in accordance with the actual scientific requirements in ultraintense regimes. Their tailored surface features influence the way the energy is deposited in the material, leading to significantly enhanced interaction effects compared to the flat conventional targets. In this study, we numerically investigate the mechanisms of laser field intensification occurring in the interaction between an ultraintense laser pulse and a nanostructured conical target. In order to provide a complex spatio-temporal description of the laser intensity evolution in the interaction area, we developed a 2D finite-difference time-domain model in accordance with the relative spatial extension of the pulse. The laser field intensification is numerically investigated in the vicinity of the laser matter interaction point considering four different materials of the nanopatterned conical targets and variable laser beam parameters in order to determine the optimum conditions to streamline the laser field enrichment in the laser solid targets interaction area. The numerical results show that the designed nanostructured profile of the internal cone target walls under imposed particular conditions induces a highly controllable increase in laser field intensity. Consequently, this enhanced field localization highlights the essential role of nanostructured design in advancing ultraintense laser applications that require efficient energy coupling and extreme field concentrations. Full article

Predicting wildland fire spread requires numerical schemes that can resolve sharp gradients at the fireline while remaining stable and efficient on practical grids. We develop a compact high-order finite-difference scheme for Hamilton–Jacobi level-set formulations of wildfire propagation, based on the anisotropic spread law of Mallet and co-authors. The spatial discretization employs a compact finite-difference derivative scheme to achieve spectral-like resolution with narrow stencils, improving accuracy and boundary robustness compared with wide-stencil ENO/WENO reconstructions. To control high-frequency artifacts intrinsic to non-dissipative compact schemes, an implicit high-order low-pass filter is incorporated and activated after each Runge–Kutta stage. Convergence is verified on the eikonal expanding-circle benchmark, where the method attains the expected high-order spatial accuracy as the grid is refined. The proposed scheme is then applied to wind-driven wildfire simulations governed by Mallet’s non-convex Hamiltonian, including a single ignition under moderate and strong wind. A complex topology test case is also considered, involving two ignitions that merge into a single front with the evolution of an internal unburnt island. The results demonstrate that the proposed method accurately reproduces fireline evolution even on coarse grids, achieving accuracy comparable to fifth-order WENO while maintaining superior fidelity in complex fireline topologies, where it better resolves multi-front interactions and topological changes in the fireline. This makes the method an efficient, accurate alternative for level-set wildfire modeling and readily integrable into existing frameworks. Full article

Bellows compensators are critical components in pipeline systems, designed to absorb thermal expansions, vibrations, and pressure reflections. Ensuring their operational reliability requires accurate prediction of the stress–strain state (SSS) and stability under internal pressure. This study presents a comprehensive mathematical model for analyzing corrugated bellows compensators, formulated as a boundary value problem for a system of partial differential equations (PDEs) within the Kirchhoff–Love shell theory framework. Two numerical approaches are developed and compared: a finite difference method (FDM) applied to a reduced axisymmetric formulation to ordinary differential equations (ODEs) and a finite element method (FEM) for the full variational formulation. The FDM scheme utilizes a second-order implicit symmetric approximation, ensuring stability and efficiency for axisymmetric geometries. The FEM model, implemented in Ansys 2020 R2, provides high fidelity for complex geometries and boundary conditions. Convergence analysis confirms second-order spatial accuracy for both methods. Numerical experiments determine critical pressures based on the von Mises yield criterion and linearized buckling analysis, revealing the influence of geometric parameters (wall thickness, number of convolutions) on failure mechanisms. The results demonstrate that local buckling can occur at lower pressures than that of global buckling for thin-walled bellows with multiple convolutions, which is critical for structural reliability assessment. The proposed combined approach (FDM for rapid preliminary design and FEM for final verification) offers a robust and efficient methodology for bellows design, enhancing reliability and reducing development time. The work highlights the importance of integrating rigorous PDE-based modeling with modern numerical techniques for solving complex engineering problems with a focus on structural integrity and long-term performance. Full article

Convolutional neural networks (CNNs) have strong noise resistance, and this study utilizes this property to weaken the impact of noise on structural damage identification data. After structural damage occurs, the modal parameters at the unit level are particularly sensitive to changes in damage and can therefore be used as important characteristic indicators for identifying damage. This article establishes a finite element model of steel truss and introduces damage at different positions and degrees. The free vibration process of the structure is simulated by the finite element method (FEM), and the first-order modal characteristic parameters, including modal strain energy and modal strain, are extracted for each damage situation. Subsequently, these modal parameters and the corresponding damage information are input as training samples into the CNN model for automatic identification of structural damage. The results show that the constructed CNN model can accurately identify the location and degree of structural damage, with a damage localization accuracy of 100% and a relative error of only 6.6% for damage degree identification. Among various characteristic indicators, modal strain energy difference exhibits better sensitivity and stability. Compared with traditional backpropagation (BP) neural networks, the CNN shows improved detection accuracy, by about 35%, and computation time is only 2.4% of BP networks. In addition, the CNN maintains good recognition performance in low order modes, which is of great significance for easily obtainable measurement data in practical engineering. In summary, the CNN method shows superior performance in damage localization, damage degree recognition, and noise resistance and has high engineering application value. Full article