1,720,997 research outputs found
Multimodal piezoelectric devices optimization for energy harvesting
Full text linkThe use of the piezoelectric effect to convert ambient vibration into useful electrical energy constitutes one of the most studied areas in Energy Harvesting (EH) research. This paper presents a typical cantilevered Energy Harvester device, which relates the electrical outputs to the vibration mode shape easily. The dynamic strain induced in the piezoceramic layer results in an alternating voltage output. The first six modes of frequencies and the deformation pattern of the beam are carried out basing on an eigenfrequency analysis conducted by the MEMS modules of the COMSOL Multiphysic® v3.5a to perform the Finite Element Analysis of the model. Subsequently, the piezoelectric material is cut around the inflection points to minimize the voltage cancellation effect occurring when the sign changes in the material. This study shows that the voltage produced by the device, increases in as the dimensions of the cuts vary in the piezoelectric layer. Such voltage reaches the optimum amount of piezoelectric material and cuts positioning. This proves that the optimized piezoelectric layer is 16% more efficient than the whole piezoelectric layer
Design sensitivity analysis of the electromechanical response of piezoelectric energy harvesters under structural vibrations from offshore floating structures
No full textIn this paper, we propose to use an innovative multi-scale approach for modeling the frequency and time domain response of piezoelectric energy harvesters (EH) under structural vibrations from offshore floating structures. In doing so, a simplified single degree of freedom (SDOF) model is used to describe the dynamic behaviour of a wave energy converter and/or a floating foundation of offshore wind turbines subjected to flow induced vibrations. The semi-empirical Morison equation is employed to compute the resulting load acting on the structure that is considered as a classically damped linear elastic system. Once the apparatus dynamic response is fully determined, we analyze the energy harvester behaviour which dynamic excitation corresponds to the structural response previously assessed. In practise, the floating device acts as a filter for the wave loads. Furthermore, with the aim to optimize the overall performances, a design sensitivity approach is used for EH modeling and design. The implementation is based on finite element method and automatic differentiation techniques where the overall algorithm is split in two stages: a primal problem and a secondary sensitivity problem
An Alternative Approach to Parallel A/D Conversion
No full textA functional diagram of a novel type of A/D converter, described as bit oriented, for high-speed application is discussed. Although the system can be regarded as the natural development of the well-known flash ADC, a different approach to the conversion process has led to a reduction in the number of comparators required, with no loss in flash performances. There is also no need for sample and hold (S/H) and digital circuits for the coding. A comparison with the most up-to-date high conversion techniques (subranging, folding and interpolation) points out the advantages of the techniqu
Non-stationary numerical covariance analysis of linear multi degree of freedom mechanical system subject to random inputs
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Random Dynamic Approach for Fragility Curves of Linear MDoF System Subjected to Earthquakes
No full textDescriptive and Inferential Statistics for Supervising and Monitoring the Operation of PV Plants
No full textThis paper deals with the problem of supervising and monitoring a photovoltaic (PV) plant. First, an offline descriptive and inferential statistical procedure for evaluating the goodness of system performance is presented. Then, an online inferential algorithm for real-time monitoring and fault detection is introduced. The two methodologies utilize the energy output of inverters as input data and are valid for both Gaussian and non-normal distribution of data. The procedures have been tested on a real PV installation, and results are reported for the case of a grid-connected PV plant in Italy for which one PV module over 132 resulted in being badly connecte
A feature extraction unsupervised neural network for an environmental data set
No full textEnvironmental data sets are characterized by a huge amount of heterogeneous data from external fields. As the number of measured points grows, a strategy is needed to select and efficiently analyze the useful information from the whole data set. One efficient way of obtaining the validation-compression of data sets is the adoption of a restricted set of features that describe, with an assigned accuracy a subset of the whole data set. One characteristic feature of the environmental data is time dependency: in the medium and long term they are not stationary data sets. The aim of this work is to propose a feature extraction technique based on a new model of an unsupervised neural network suitable to analyze this kind of data. The paper reports the results obtained utilizing the above extraction and analysis procedure on a real data set on chemical pollutants. It is shown that the proposed neural network is able to identify correctly human and/or meteorological effects in the environmental data set
Multivariate Data Projection Techniques Based on a Network of Enhanced Neural Elements
No full textIn this paper two techniques to project high dimensional data into a bidimensional space are introduced. These techniques are based on an unsupervised neural network of enhanced processing elements. The proposed approaches are compared with some widely known projection techniques based on unsupervised neural networks. These comparisons show that the new projection techniques perform comparably or slightly better than the traditional techniques and are promising in term of computational burden
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