Meccanica最新文献

Precise medicine strategies for vascular autografts aim to conceive smart and biomechanically mimetic reinforcements able to support the arterialization process of autologous vessels, by bearing systemic loads while fostering virtuous microenvironments conducive to remodeling and growth. However, the mechanical discrepancy between synthetic devices and the biologically active host vessel often determine a conflict between requirements for robust short-term performance and for long-term functionality. Composite architectures should indeed compensate the compliance mismatch between aortic and venous tracts, on a side avoiding aneurysmal dilation and, on the other one, preventing excessive stress-shielding subverting tissue adaptation. Consequently, addressing the mechanical design challenges of reinforced grafts is essential to resolve the trade-off, providing structural support and replicating homeostasis. With this in mind, this work provides insights in the mechanistic interplay between reinforcement and tissue response in autografts, focusing on the effect of reinforcement stiffness on their arterialization. This is achieved by first introducing a mechanobiological model where growth and material remodeling are in full coupling with the nonlinear tissue elasticity, employing ad hoc analytical solutions to study the potential adaptation scenarios emerging from the interaction with implants at varying stiffness. Theoretical projections are then used to tailor a design optimization of reinforcement geometrical and mechanical features, implementing finite-element procedures to analyze the influence of filament number, dimensions and stiffness on realistic meshes. Overall, outcomes suggest how mild stiff reinforcements with mid-density filament architectures offer the most mechanically stable and physiologically consistent solution, opening possible biomechanically-based approaches for enhancing long-stable graft integration.

Bubble/oil mixture are prevalent in end-sealed squeeze film dampers (SFDs). Based on a homogeneous mixture model, this paper develops a comprehensive numerical method for analyzing cavitation and dynamic characteristics in end-sealed SFDs by coupling the Reynolds (RE) and complete Rayleigh-Plesset (RPE) equations, both incorporating temporal and advection inertia effects. To enhance numerical stability, the RPE is solved using Numerical Differentiation Formulas (NDFs) rather than the explicit Euler method. Compared with experimental and numerical film pressure results, the proposed method more accurately predicts pressure in cavitation regions due to the inclusion of bubble dynamics. Numerical simulations investigate how inertial effects, surface dilatational viscosity, oil supply pressure, orifice loss coefficient affect hydrodynamic pressure, void fraction, inertia, and damping coefficients in end-sealed SFDs varying whirl frequencies and amplitudes. Accounting for bubble dynamics, the fluid withstands tensile stress that, along with valley pressure, decreases with lower surface dilatational viscosity; using 7.85 × 10⁻⁴ N s/m is adequate since reasonable variations negligibly impact inertia and damping. Introducing inertia raises peak pressures by over 15% near the feed-port at high whirl frequencies. Feed-ports enhance film inertia, distort the pressure field, and reduce local cavitation by more than 50%. Higher supply pressure also suppresses cavitation and increases inertia and damping, depending on frequency and bubble dynamics. Conversely, larger orifice loss coefficients, relating to the structural parameters and inlet flow velocity, increase the backflow rate and reduce inertia and damping. These results highlight the need to tailor optimal oil supply and orifice design to specific operating conditions.

This paper presents a numerical methodology for three-dimensional crack propagation analysis based on remeshing. A crack tip opening displacement (CTOD) calculation method employing radial basis function (RBF) interpolation is proposed, and a critical CTOD criterion for crack propagation is established through a combined experimental and computational approach. The accuracy of the RBF-based CTOD calculation method is validated. Furthermore, a mesh deformation mapping strategy based on local RBF interpolation is developed, and quasi-static tensile fracture experiments and corresponding numerical simulations are conducted on stiffened panels containing cracks. Comparative analysis of experimental and simulation results demonstrates the feasibility of the proposed mesh deformation mapping strategy and elucidates the fracture mechanism governing crack propagation through the stiffener.

Designing and engineering a passenger vehicle suspension requires numerous compromises to maintain adequate levels of passenger comfort, handling and safety. That has been often achieved using sophisticated state-of-the-art suspension technologies, namely, semi-active or fully active suspension systems. However, even the semi-active magnetorheological or valve-based damping systems have maintained their price tags high. Therefore, the demand for an adaptive passive damping technology that would enhance the passive damper performance without the need for expensive electronics has arisen. This work presents a novel concept of a frequency-dependent (FD), passive-adaptive automotive suspension damper. The non-linear dynamic model of the passive twin-tube damper is complemented by the FD valve concept model, verified via experimental tests in a laboratory using a FD damper prototype. In the case of the tested prototype, rebound damping forces at medium and high frequencies were reduced down to 68% compared to the base damping force at low frequencies while the basic damping function was maintained when operating within the low speed/low frequency range of the excitation inputs. Thus, the proposed solution may contribute to improving passenger comfort while maintaining good steering response and body control of a vehicle. The presented FD damper effectively fits in between conventional passive dampers and state-of-the-art semi-active systems, while maintaining a price tag approaching the former. The presented concept may be a significant aid in designing FD valves, not only for the automotive industry.

Thin annular membranes under torsional loading exhibit periodic wrinkling phenomena, leading to surface irregularities. In this study, we introduce a homeomorphic transformation approach to equivalently address the torsional wrinkling of circular membranes by mapping them to rectangular domains. We establish a numerical analysis framework that integrates an implicit difference method with a perturbation technique to solve the nonlinear governing equations. This framework effectively connects the reference rectangular topology with the target annular topology. Based on this geometric equivalence, we analyze the correlation between rectangular shear and annular torsion. Furthermore, under a variable separation assumption, we derive semi-analytical and semi-empirical explicit expressions specifically for the critical load and critical wavelength of torsional wrinkling. The proposed homeomorphic transformation significantly extends the applicability of traditional difference methods. Comparisons with numerical results and existing literature demonstrate the accuracy of the derived expressions.

Nonlinear energy sinks (NES) have attracted significant research interest due to their exceptional energy dissipation capabilities. However, conventional single-type stiffness NES designs often fail to provide effective vibration suppression across varying vibration intensities and frequency bandwidths. To achieve the efficiency of broadband energy dissipation, this study proposes a coupled system integrating a piecewise linear and cubic stiffness NES with a piezoelectric energy harvesting system, termed the “piezoelectric, piecewise-linear and NES”(PPNES), into a single-degree-of-freedom primary structure. Based on Newton’s second law, the electromechanical coupling dynamic governing equation of the primary structure coupled PPNES system was established and explored analytically and numerically. By employing the complex variable averaging method and the multi-scale method, the energy dissipation and transfer of the coupled model were investigated. Additionally, the effects of the PPNES on vibration suppression and energy harvesting performance in a single-degree-of-freedom primary structure were analyzed using wavelet transform and time-history response analysis. The results reveal that parametric variations of the PPNES system can trigger targeted energy transfer (TET) mechanisms in the coupled oscillator system. Compared with traditional smooth NES, the proposed non-smooth PPNES exhibits better vibration suppression performance in the medium to high energy level range.

Rotating machines are widely used in industry and are susceptible to multiple faults, commonly including rotor unbalance and bearing degradation. Regarding hydrodynamic bearings, oil supply and cooling are critical; however, they cannot be closely monitored in machines without dedicated sensors in the oil lines. Thus, this paper proposes a non-intrusive, model-based method to identify oil inlet flow rates, oil inlet temperature, and rotor unbalance through vibration measurements, without instrumenting the lubrication circuit. The method solves a nonlinear inverse problem by matching selected vibration features with a coupled rotor–thermo-hydrodynamic bearing model to estimate the unknown operating and fault parameters. Unlike most related works that focus on discrete lubrication-state classification, the proposed approach performs continuous, quantitative regression of oil-inlet conditions, which can support the detection of incipient changes that may not be captured by coarse state classification. In addition, the method simultaneously identifies lubrication inputs and rotor unbalance in a coupled rotor–bearing system, addressing a multi-fault setting that is rarely considered in the literature. Numerical results show that the method can track time-varying oil inlet temperature and flow while estimating rotor unbalance from noisy measurements (10 dB SNR), with a maximum error of 5.8% and an average identification time of 25.3 s, supporting online monitoring.

This work presents a modified numerical model for predicting the transverse free vibration of stepped beams with improved accuracy over the classical Timoshenko beam theory (TIM model). While TIM model performs well for beams with constant cross-sections, it fails to capture the dynamic response near abrupt changes in geometry, as is typical in stepped beams. To overcome these limitations, a new formulation is proposed (MOD model) that introduces smooth transitions in cross-sectional properties via a parametrized sigmoid function applied to stiffness and mass matrices. A systematic optimization of the transition parameters is conducted using a genetic algorithm to best approximate high-fidelity finite element results (REF model). The MOD model is validated across 35 beam configurations and outperforms the TIM model in most cases, especially for beams with high geometric discontinuities. Additional case studies involving asymmetric and multi-step beams demonstrate the generalization capability of the proposed approach. The MOD model thus offers a computationally efficient yet accurate tool for dynamic analysis of complex beam geometries.

This study investigates the application of dynamic neural networks for predicting unsteady aerodynamic loads on airfoils under dynamic stall conditions. The dynamic stall phenomenon is characterized by highly nonlinear and transient flow separation at high angles of attack. It poses significant challenges for traditional modeling approaches such as Computational Fluid Dynamics (CFD) and semi-empirical methods. To address these limitations, this work innovatively proposes a reduced-order model, trained exclusively on experimental data, that uses dynamic neural networks to rapidly and reliably evaluate aerodynamic loading on pitching airfoils under nonlinear dynamic stall conditions. Two neural network architectures, Time-Delay Neural Networks (TDNN) and Recurrent Neural Networks (RNN), are trained on experimental wind tunnel data for a NACA0012 airfoil undergoing pitching oscillations. The TDNN incorporates time-delayed inputs to capture temporal dependencies, while the RNN leverages internal feedback loops to model sequential aerodynamic behavior. Both models were trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated on their ability to predict lift, drag, and pitching moment coefficients. Results indicate that both architectures effectively capture dynamic stall’s nonlinear and hysteretic characteristics, with the RNN demonstrating marginally superior performance due to its inherent memory retention. The models achieved high accuracy in predicting lift and drag. However, challenges persisted in estimating the pitching moment, particularly in light stall regimes, likely due to noise sensitivity in moment data. Generalization tests on unseen cases confirmed the neural network models’ robustness in severe dynamic stall scenarios, including vortex shedding and flow reattachment.