International Journal of Mechanics and Materials in Design最新文献

In this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

In this paper, we extend our earlier single blade shedding studies by examining the dynamics of multiple blade shedding in a fan disc of an aviation gas turbine engine experimentally using a scaled-down test rig with improved instrumentation and numerically using nonlinear finite element simulations. The newly improved scaled-down rig is designed using dimensional analysis to maintain its dynamic equivalency with a fan disc in a medium size engine. The improved instrumentation includes additional strain gauges, accelerometer, temperature and speed sensors for improved characterisation of the shedding dynamics. High speed photography was also used to capture the time history of the multiply released blades. The shedding experiments were compared with high resolution finite element simulations of a fully bladed fan disc of a realistic gas turbine engine. We took account of blade airfoil, strain rate effects, and multiple contacts between the blades and the containment ring in our finite element simulations. The results of the current investigations reveal that (i) the released multiple blades interact with the trailing blades causing maximum damage to the trailing blades, (ii) large strains develop in the containment ring due to the multiple blade shedding and (iii) the predicted transient response of the finite element simulations of multiple blade interactions are in agreement with the findings of the scaled-down experiments, confirming the validity of our scaled-down test rig as a possible alternative or a compliment to full engine shedding tests.

The smoothed finite element methods (SFEM) have demonstrated their ability to generate more flexible models, offering increased reliability compared to traditional FEM in certain straightforward and idealized situations. To explore the potential of SFEM in complex engineering problems, this paper, for the first time, combining with multiple point constraints to develop a simple and general procedure to study various analysis types of multi-component structures, via (1) the global matrix is constructed by eliminating independent degrees of freedom; (2) the local matrix generated by the SFEM is divided into four kinds of sub-domains, and any entry of the local matrix is assembled to the global matrix depending on the type of sub-domain. By implementing this approach without augmenting the number of equations, the current method excels not only in the analysis of multi-component structures but also outperforms ABAQUS and NASTRAN in terms of effectiveness and efficiency. This superiority has been convincingly demonstrated through several numerical examples, providing strong validation for the proposed method.

The gossamer space structures can be stowed effortlessly because of a lack of out-of-plane stiffness, but structural strength is needed on partial or complete out-gassing to maintain their deployed state. This study demonstrates a novel approach to producing a self-maintaining shape ability of an inflatable cylindrical boom using heat-actuated SMA wires when the inflation gas is vented out from the assembly after complete deployment. Kapton-based and Kapton-SMA-based booms are analyzed numerically for bending stiffness under inflation and no-inflation pressure, followed by experimental validation. At this end, a customized heat test chamber is developed to conduct the required experiments. Furthermore, a parametric study is also performed to find the effect of materials and design parameters on the boom’s stiffness. Before all, the non-linear behavior of double-layered laminated Kapton is found by curve fitting of stretch test data with the optimized different material model parameters to find the best-fitted material model under the hyperelastic materials category. The study helps to find the membrane behavior and rigidization of the inflatable boom in a reversible manner.

The primary focus of traditional topological optimization in continuum structures is addressing stress, compliance, and other relevant factors associated with single-phase materials. However, the optimal design of structural buckling performance has gained increasing attention due to its significant economic loss and safety risk. Furthermore, the versatility, lightweight nature, and adjustability of composite multiple-phase materials offer significant potential for application in various fields. Therefore, this paper presents a novel methodology for optimizing multi-phase materials’ design by concurrently incorporating structural buckling criteria and compliance design. Linear buckling analysis is utilized to determine the critical buckling load of the structure, and a buckling constraint is incorporated into the topology optimization model to regulate its buckling performance. A refined material interpolation model scheme is introduced to enhance the algorithm’s robustness and eliminate pseudo-eigenmode in buckling analysis. The numerical results demonstrate that the final topology optimization design exhibits distinct and discernible boundaries for the topological configurations of multiple-phase materials. Moreover, it is possible to effectively regulate the buckling property while minimizing any compromise on stiffness.

Harvesting electrical energy from mechanical vibrations through piezoelectric-based resonant devices is a suitable form of generating alternative electrical sources for several applications, most dedicated to powering small electronic devices. This technique has attracted considerable attention over the past decades, mainly due to piezoelectric materials’ high electrical charge density. However, the amount of harvestable energy is usually small and sensitive to variabilities in design, manufacturing, operation, and environmental conditions. Hence, it is essential to account for predictable and potentially relevant uncertainties during the design of energy harvesting devices. This work presents strategies for the robust design of resonant piezoelectric energy harvesters, considering the presence of uncertainties in design, manufacturing, and mounting conditions, such as the bonding of the piezoelectric materials and the clamping of the resonant device. The work proposes and discusses strategies for finite element modeling, accounting for adhesive bonding of piezoelectric materials and imperfect clamping; harvestable power output mean value and dispersion estimation with Polynomial Chaos Expansion; and robust optimization using multiobjective optimization techniques. Relevant general conclusions concerning harvesting devices include but are not limited to, devices with shorter resonating beams and larger tip masses tend to present performances that are nominally better but also less robust. Additionally, reducing the effective electrical resistance may improve robustness without significantly losing the mean value performance. Also, through an assessment of the most relevant design variables and uncertain parameters, some aspects that should receive special attention when designing, manufacturing, and mounting these devices are discussed, such as the bonding of piezoelectric patches and the clamping of cantilever beams due to their essential effect on the robustness of the device. It is also shown that including well-selected design variables may mitigate the impact of uncertainties and, thus, improve the robustness of the device.

Based on stress transfer relationship of fiber reinforced composite layer, damping layer and stiffeners, this study presents a novel dynamic analytical model in order to evaluate the dynamic characteristics of a composite damping plate with randomly oriented carbon nanotube reinforced stiffeners. Both an energy method and complex modulus theory are used to derive the vibration equations. Experiments and numerical simulations are adopted for confirming the correctness of the analytical findings. Furthermore, the model is utilized to investigate the impact of structural variables on the dynamic properties, including the modal loss factor and first-order natural frequency.

In recent years, the utilization of renewable energy sources has emerged as a prevalent trend, both globally among countries as well as within engineering applications. Solar energy has attracted significant interest from the research community, primarily for its exceptional ability to produce electric energy in an eco-friendly and sustainable way. The current work is dedicated to introducing a powerful and effective numerical framework for analyzing the fundamental mechanical behavior of multilayered solar cell structures, namely static, vibration, and buckling problems. The key formulations are developed from a five-variable generalized higher-order shear deformation model in conjunction with NURBS-based isogeometric analysis (IGA). We, in this research, examine two typical kinds of flexible solar cell structures: Organic Solar Cells (OSCs) and Perovskite Solar Cells (PSCs), belonging to the latest generation and offering various excellent advantages in terms of efficiency and production costs. For the first time, we conduct comprehensive parametric investigations to evaluate how various input parameters affect the static deflections, natural frequencies as well as critical buckling parameters of two multilayered solar cell models under different conditions. The novel findings presented in this article can be referred to as valuable reference results for future analyses of static, buckling and vibration problems. Furthermore, the insights obtained will be pivotal for guiding future analyses, designs, and fabrications of multilayered solar cell structures.

A novel method based on a physics informed data driven model is developed to design an origami composite crash tube. The structure consists of two axially stacked basic components, called modules. Each module presents lower and upper square sections with an octagonal section in the middle. The parameters of the octagonal cross-section and the height of each module are optimized to maximize the energy absorption of the tube when subjected to an axial impact. In contrast to standard surrogate modelling techniques, whose accuracy only depends on the amount of available data, a Physics-informed Neural Network (PINN) scheme is adopted to correlate the crushing response of the single modules to that of the whole origami tube, constraining the data driven method to physically consistent predictions. The PINN is first trained on the results obtained with an experimentally validated Finite Element model and then used to optimize the structure. Results show that the PINN can accurately predict the crushing response of the origami tube, while consistently reducing the computational effort required to explore the whole design domain. Also, the comparison with a standard Feed Forward Neural Network (FFNN) shows that the PINN scheme leads to more accurate results.

Inflatable beam can be regarded as thin-walled beam structure with uniform pressure on the inner wall. In the bending behavior of inflatable beams, there is a noticeable shear effect, causing the cross-section to deviate from the beam's axis. By defining a local coordinate system, the shear effect can be described more accurately. However, the stiffness of the inflatable beam is inconstant under the varying inner pressure. And the inner pressure changes the geometric parameters of the inflatable beam through expansion, thereby changing its section characteristics, and ultimately affecting the expression of the shear effect. Therefore, the application scope of the results obtained by using fixed material parameters is limited. On the basis of previous studies, a revised bending model of inflatable beam considering the shear effect in varying inner pressure is proposed by establishing the relationship between internal pressure, dynamic stiffness and shear effect. The three-point central concentrated load bending experiment of a simply supported beam is then investigated. The computed outcomes of the model are juxtaposed with the results derived from three-dimensional finite element analysis and empirical experimentation, revealing a significant concordance. The model's reliability was further confirmed through comparisons with established models.