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Vibration energy harvesting using piezoelectric cantilevers has been widely studied during the past decade. As an alternative to piezoelectric cantilevers, plate-like energy harvesters can be much more effective in marine, aerospace, and automotive applications. This work presents an exact two-dimensional model that can be used for analyzing thin, moderately thick, and thick piezoelectric bimorph plate vibration harvesters. The model allows to consider for the substrate layer both homogenous materials, and those with varying properties along the thickness direction. For the latter case, porous materials with various porosity distributions are herein considered, and the piezoelectric layers are assumed to be wired in both parallel and series configurations. Rayleigh damping assumptions are also used to model the structural damping of the harvesting system. Using Hamilton's principle and Gauss's law, the energy harvesting model is established based on the first-order and the third-order shear deformation theories. Applying an analytical procedure to the electromechanical governing equations, closed-form steady-state response expressions, which relate the voltage output and the vibration response of the harvester to harmonic input force, are derived. Finally, the proposed model is validated, and the power generation performance of the plate harvester is discussed through conducting extensive parametric studies, covering the effect of design parameters, such as the applied electric load, porosity characteristics, electrical configuration, and geometrical parameters. [ABSTRACT FROM AUTHOR]

The Intelligent Fault Diagnosis of rotating machinery currently proposes some captivating challenges. Although results achieved by artificial intelligence and deep learning constantly improve, this field is characterized by several open issues. Models' interpretation is still buried under the foundations of data driven science, thus requiring attention to the development of new opportunities also for machine learning theories. This study proposes a machine learning diagnosis model, based on intelligent spectrogram recognition, via image processing. The approach is characterized by the employment of the eigen-spectrograms and randomized linear algebra in fault diagnosis. Randomized algebra and eigen-spectrograms enable the construction of a significant feature space, which nonetheless emerges as a viable device to explore models' interpretations. The computational efficiency of randomized approaches provides reading keys of well-established statistical learning theories such as the Support Vector Machine (SVM). Machine learning applied to spectrogram recognition shows to be extremely accurate and efficient as compared to state of the art results. [ABSTRACT FROM AUTHOR]

The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using a Generative Adversarial Network (GAN) with cycle consistency loss function known as cycleGAN. The proposed method aims to generate synthetic data that could effectively replace real experimental data. The generative model is trained to transform wavelet images of simulated vibrational signals into authentic data obtained from machinery with damaged bearings. The utilization of Maximum Mean Discrepancy (MMD) and Fréchet Inception Distance (FID) demonstrates a noteworthy resemblance between synthetic and real experimental data. Also, the generative model enables the synthesis of data that may have been entirely lacking from the experimental observation, indicating generative zero-shot learning capabilities. The efficacy of synthetic data in training diagnosis algorithms by means of Transfer Learning (TL) on Convolutional Neural Networks (CNNs) has been demonstrated to be comparable to that of real data. The study has been validated by means of the test rig for medium-sized industrial bearings accessible at the Politecnico di Torino. [ABSTRACT FROM AUTHOR]

The aerospace industry is one of the leading figures in the development and improvement of techniques for the design of new products. One of the most promising developments of the last decades is the exploitation of digital models that make it possible to evaluate design solutions and simulate the behavior of the individual systems and their interactions. The goal is to be able to predict and analyze all aspects an aircraft much in advance of its industrialization in order to heavily reduce the time and costs of product development and to guarantee flexibility to test a multitude of solutions. The main issue in this context is the complexity of creating models that are capable of accurately sizing and simulating multiple interacting systems, thus considering the constraints imposed by the need for their mutual compatibility. The present contribution introduces two interconnected models regarding an aircraft system, in particular, the landing gear, that make it possible to size its main components and subsystems and to use the found parameters to populate a dynamic model that simulates the behavior of the aircraft during landing. These models provide a preliminary digitalization of the system itself and of the design process as well, thereby making it possible to define a potential configuration and to test it in a dynamic virtual environment, thus taking into account the interaction between the individual subsystems. The model was tested through three use cases, differentiated by class and scope, which made it possible to compare and validate the obtained results with actual values. [ABSTRACT FROM AUTHOR]

In recent years, the methodology of Model-Based System Engineering (MBSE) has become relevant to the design of complex products, especially when safety critical systems need to be addressed. It allows, in fact, the deployment of product development directly through some digital models, allowing an effective traceability of requirements, being allocated upon the system functions, components, and parts. This approach enhances the designer capabilities in controlling the product development, manufacturing and after-market services. However, the application of such a methodology requires overcoming several technological barriers, especially in terms of models integration. The interoperability and management of several models—developed within different software to cover multiple levels of detail across several technical disciplines—is still very difficult, despite the level of maturation achieved by Systems Engineering. This paper describes a possible approach to provide such a connection between tools to allow a complete multi-disciplinary and heterogeneous simulation to analyse complex systems, such as safety-critical ones, which are typical of aerospace applications. Such an application is within a defined industrial context, placing particular attention on the compatibility of the approach with the legacy processes and tools. [ABSTRACT FROM AUTHOR]

One of the several losses of a combustion chamber is the gas leakage toward the crankcase due to imperfect sealing of the rings. It is commonly known as blow by and it affects efficiency and emissions. The paper initially describes a bibliographic review of the phenomenon, together with the equations of the system. A typical piston ring pack for internal combustion engine is proposed to be analysed and solved using ©Ricardo RINGPAK Solver. A specific issue such as Bore distortion orders were used to investigate the sealing capacity of the ring-pack in terms of ring dynamics, inter-ring pressures and mass flows. Bore distortion orders and their magnitude showed to affect the ring pack behavior. Order zero distortion resulted to be the most important order due to the highest amount of gas lost in the crankcase, while orders three and four resulted to generate high blow-by values, even if their magnitude of distortion is lower in comparison to other orders.