Meccanica最新文献

Reliability of structures is evaluated by considering uncertainties present in the system, which can be characterized into aleatory and epistemic. Inherent randomness in the physical environment leads to aleatory, whereas insufficient knowledge about the system leads to epistemic uncertainty. For the reliability evaluation, ascertaining the sources of uncertainties poses a great challenge since both uncertainties coexist widely in structural systems. Aleatory uncertainties are quantified by probabilistic measures (such as first order reliability method, second order reliability method and Monte Carlo techniques), whereas epistemic uncertainties are quantified by various non-probabilistic approaches (such as interval analysis methods, evidence theory, possibility theory and fuzzy theory). However, major issues like interval extension problem and duality conditions that lead to overestimation hinder the versatility of application of such methods, thus uncertainty theory has been emerged to overcome these limitations. Given the existing uncertainties and limitations, a hybrid strategy has been constructed and referred to as “belief reliability”. A belief reliability metric is integration of three key factors: design margin, aleatory and epistemic uncertainty factor to evaluate the reliability of the structural system. In this paper, Monte Carlo simulation is adopted to account for aleatory uncertainty. On the other hand, epistemic uncertainty is quantified through adjustment factor approach using FMEA (failure mode effective analysis). Numerical examples are presented to substantiate the proposed methodology being applied to variety of problems both implicit and explicit nature in structural engineering.

In this work, a technique to improve the magnetic plucking for frequency up-conversion in piezoelectric energy harvesters is presented. The technique involves shielded magnets with Neodymium-iron-boron alloy polarized in the opposite direction on a main magnet. The phenomenon is investigated both at the computational and at the experimental level. Subsequently, simulations on a mesoscale piezoelectric energy harvester are presented which demonstrate a gain of 17 times if the magnets are shielded in comparison with the classical plucking (i.e. without shielding). The technique finds useful applications and benefits in the field of low-speed and low-frequency vibration energy harvesting, as well as in actuation and sensing.

This paper presents a new combined fabrication method, named 3D-PLAST, aimed at overcoming inherent limitations of conventional additive manufacturing techniques when producing small flexure hinges in compliant mechanisms. Flexure hinges play a crucial role in various applications, offering advantages such as cost reduction, increased precision, and weight reduction. However, traditional additive manufacturing proves challenging in achieving satisfactory mechanical properties when manufacturing small-size hinges. To overcome these limitations, the 3D-PLAST process combines fused filament fabrication with compressive plastic deformation. This hybrid process exploits the advantages of both techniques, i.e., flexibility, low cost, and ease of use. This process enables the fabrication of small-size mechanisms with good dimensional accuracy. Finally, the paper reports experimental tests on two materials comparing flexure hinges manufactured by 3D-PLAST versus 3D printing methods to demonstrate the effectiveness of the proposed process.

Planning a smooth-running and effective extrusion-based bioprinting process is a challenging endeavor due to the intricate interplay among process variables (e.g., printing pressure, nozzle diameter, extrusion velocity, and mass flow rate). A priori predicting how process variables relate each other is complex due to both the non-Newtonian response of bio-inks and the extruder geometries. In addition, ensuring high cell viability is of paramount importance, as bioprinting procedures expose cells to stresses that can potentially induce mechanobiological damage. Currently, in laboratory settings, bioprinting planning is often conducted through expensive and time-consuming trial-and-error procedures. In this context, an in silico strategy has been recently proposed by the authors for a clear and streamlined pathway towards bioprinting process planning (Chirianni et al. in Comput Methods Appl Mech Eng 419:116685, 2024. https://doi.org/10.1016/j.cma.2023.116685). The aim of this work is to investigate on the influence of bio-ink polymer type and of cartridge-nozzle connection shape on the setting of key process variables by adopting such in silico strategy. In detail, combinations of two different bio-inks and three different extruder geometries are considered. Nomograms are built as graphical fast design tools, thus informing how the printing pressure, the mass flow rate and the cell viability vary with extrusion velocity and nozzle diameter.

A multimodal distribution based uncertainty analysis method for cross-domain aircraft morphing wing mechanisms is proposed to address the engineering issue of the reliability of morphing mechanisms. This method is based on Gaussian mixture model, isotropic sparse mesh method combined with maximum entropy method analysis. In the working environment of the morphing wings, the external load exhibits a multimodal distribution with changes in flight altitude and geographical location. Traditional uncertainty methods are difficult to accurately determine the reliability of aircraft under the influence of multiple variable influencing factors. Therefore, the proposed method is proposed to evaluate the reliability of morphing wing mechanisms. Firstly, a Gaussian mixture model is used to establish the mixture density function of the pressure and the leading edge size of the variant aircraft. Secondly, the integral points and weights of the multimodal random variables are calculated by the sparse grid method. Finally, an adaptive convergence mechanism is used to improve the uncertainty propagation accuracy. After a mathematical example and two engineering examples, it can be considered that the proposed method has a certain reference value in analyzing the uncertainty propagation under the multimodal distribution state of multiple factors.

The extended Brinkman model is employed in this study to investigate the instability of double diffusion natural convection in porous layers caused by vertical variations in boundary temperature and solute concentration. The stability of fluid flow is determined by discussing the temporal evolution of normal mode disturbances superposed onto the fundamental state. The linear dynamics problem is formulated as an Orr–Sommerfeld eigenvalue problem and solved numerically using the Chebyshev collocation method. The effects of thermal/solute Darcy–Rayleigh number (Ra_T/Ra_S), Lewis number (Le), and Darcy–Prandtl number (Pr_D) on system instability are analyzed. Growth rate curves indicate that solute Darcy–Rayleigh numbers can induce flow instability. Neutral stability curves show that increasing Ra_T/Ra_S promotes instability. There is a critical threshold for Le, exceeding this amplifies instability, while falling below suppresses it. For large Ra_T values, varying Pr_D leads to different effects of increasing Ra_S on flow stability. The stability of the system is significantly dependent on Ra_T and Ra_S, with the critical value of the Le playing a decisive role in system stability. Additionally, Pr_D significantly affects system instability under certain conditions.

In this paper, we propose an extension of a previous model of cell motility in tissue engineering applications recently developed by the authors. Achieving large-scale production of neo-tissue through biofabrication technologies remains challenging owing to the need of thoroughly optimizing all the relevant process variables, a task hardly attainable through solely trial and error approaches. Therefore, the present work is intended to provide a valid and effective computational-based support for neo-tissue formation, with a specific focus on the preliminary phase of such process, in which cells move through a polymeric scaffold (hydrogel) and then compact into clusters. Cell motility is modeled by resorting to the phase-field method, and by incorporating diffusion of nutrients from the external culture bath as well as the expression by cells of chemoattractant substances that bias the random path they otherwise would follow. The previous model has been enriched by additionally encompassing the secretion of enzymes by cells that cleave the crosslinks between the hydrogel polymer chains. As such, in the present model hydrogel degradation exhibits spatio-temporal variations in its chemo-physical properties related to the local amount of enzymes, which deeply affects cell motility. Numerical results showcase the pivotal importance of the cells micro-environment properties for their crawling in hydrogel scaffolds, opening towards the development of a predictive computational-aided optimization tool for neo-tissue growth in bioprinted scaffolds.

In this paper, a robust (mu)-optimal controller is developed for a three-dimensional overhead crane system with hoisting mechanism using the (mu)-synthesis method. The 3D overhead crane system is modeled as an underactuated five degrees of freedom (5DOFs) system with uncertain parameters. The system receives only three input signals: Two forces that move the trolley along the (x) and (y) axes, and the hoisting force that moves the payload along the rope. In the first step to design the (mu)-optimal controller for the 3D overhead crane system, nonlinear equations of the system are linearized around the equilibrium point to obtain the transfer functions. Next, to ensure that the system performs well and is robust against uncertainties, efficient weight functions for both performance and uncertainty are calculated. Finally, the (mu)-optimal robust controller is designed using MATLAB’s Robust Control Toolbox, implementing the D-K iteration algorithm, and analyzing (mu)-plots. It is shown that not only does the proposed controller provide nominal stability and performance, but it also ensures robust stability and performance. The proposed controller is applied to the original nonlinear system and simulation results demonstrate that this controller satisfies the control objectives well and is also robust to severe parametric uncertainties and external disturbances. Moreover, this controller provides better results compared to a conventional sliding mode controller (SMC) and a second-order SMC, by applying much less control forces. Another advantage of the proposed controller is that, unlike the other two controllers, it does not need feedback from states at the speed level. Therefore, in practice, the proposed robust controller needs fewer and cheaper sensors.

Theoretical modeling is established for an all-composite honeycomb core sandwich panel (ACHCSP) using the higher-order shear deformation theory and Gibson equivalent theory. The central honeycomb layer is equivalently modeled as a thick layer of orthotropic material. The vibration characteristics are solved using energy methods and orthogonal polynomial approaches. Experimental specimens of ACHCSP are fabricated, and a dedicated experimental setup is constructed for vibration response testing. The experimental results validate the accuracy of the theoretical model in predicting the intrinsic properties and vibration response of ACHCSP. A comparison between experimental and theoretical vibration response values indicates a maximum error of 10.91%. Finally, the impact of different fiber layer thicknesses, honeycomb cell wall thicknesses, and honeycomb cell wall lengths on the vibration characteristics of ACHCSP is discussed.