INVESTIGATION ON THE ARTIFICIAL NEURAL NETWORKS PREDICTION CAPABILITIES APPLIED TO VIBRATING PLATES IN SIMILITUDE

The prediction capabilities of artificial neural networks in similitude field are investigated. They have been applied to plates in similitude with two objectives: prediction of natural frequencies and model identification. The results show that the method is able to give accurate predictions and that an experimental training set can be created if the models are well characterized

Support of Dynamic Measurements Through Similitude Formulations

Up to now, similitude methods have been used in order to overcome the typical drawbacks of experimental testing and numerical simulations by reconstructing the full-scale model behavior from that of the scaled model. The novelty of this work is the application of similitude theory not as a tool for predicting the prototype dynamic response, but for supporting, and eventually validating, experimental measurements polluted by noise. Two Aluminium Foam Sandwich (AFS) plates are analyzed with Digital Image Correlation (DIC) cameras. First, an algorithm for blind source separation problems is used to extract information about the excitation; then, SAMSARA (Similitude and Asymptotic Models for Structural-Acoustic Research Applications) similitude method is applied to both the force spectra and velocity responses of prototype and model. The reconstruction of force and velocity curves demonstrates that the similitude results are coherent with the quality of the experimental measurements: when the spatial pattern in resonance is recognizable, then the curves overlap. Instead, when the displacement field of just one model is not well identified, the reconstruction exhibits discrepancies. Therefore, similitude methods reveal to be an interesting tool for understanding if a set of measurements is reliable or not and their application should not be underestimated, especially in the light of the expanding range of approaches which can extract important information from noisy observations

Experimental investigation into techniques to predict leak shapes in water distribution systems using vibration measurements

Water loss from leaking pipes represents a substantial loss of revenue as well as environmental and public health concerns. Leak location is normally identified by placing sensors either side of the leak and recording and analysing the leak noise. The leak noise contains information about the leak’s characteristics, including its shape. Whilst a tool which non-invasively provides information about a leak’s shape from the leak noise would be useful for water industry practitioners, no tool currently exists. This study evaluates the effect of various leak shapes on the vibration signal and presents a unique methodology for predicting the leak shape from the vibration signal. An innovative signal processing technique which utilises the machine learning method Random Forest classifiers is used in combination with a number of signal features in order to develop a leak shape prediction algorithm. The results demonstrate a robust methodology for predicting leak shape at several leak flow rates and backfill types, providing a useful tool for water companies to assess leak repair based on leak shape

Mechanical properties updating of a non-uniform natural fibre composite panel by means of a parallel genetic algorithm

This article presents an investigation on the mechanical properties of a composite panel made of unidirectional flax fibres embedded in a polyethylene matrix (flax-PE). An initial set of mechanical properties was identified by classical static tests. Then, an experimental modal analysis was performed in order to get information on natural frequencies and mode shapes, which are related to the mechanical properties. The experimental modal results were compared with numerical ones, obtained through finite element model using the initial set of mechanical properties. Finally, in order to get a good numerical-experimental correlation, the mechanical properties throughout the panel were updated using an inverse modelling method based on parallel genetic algorithms

Intensidades espectrales en compuestos de coordinación de los metales de transición: Aplicaciones a sistemas del tipo Cs2SnBr6:OsBr2-6

The luminiscence spectrum of the Cs2SnBr6:OsBr2-6 system is examined utilizing a generalized vibronic formalisms. For illustrative purposes we have chosen the most characteristic excitations, which show up a rich and unexpected vibronic structures. These absorptions are tackled with emphasis on both the electronic and the vibrational factors which are responsible for both the overall and the relative vibronic intensities associated with generic transitions of the Γm = Γl + νk (k = 3, 4, 6) type. The advantages and disavantages of the calculation models as well as a critical studies of the experimental data available are discussed. Relevant conclusions are drawn out in connection with the intensity spectral mechanism as well as the eventual influence on the calculated intensities due to the coupling among the internal and the external vibrations and some suggestions for improvements are put forward to advance the state of the art in the vibronic coupling theory

The Emission Spectra for the Cs2\text{}_{2}NaScCl6\text{}_{6}:MoCl63−\text{}_{6}^{3-} System in the Fm3m Space Group

On the basis of the data reported by Flint and Paulusz, we have undertaken a theoretical investigation of the intensity mechanism for the various emissions:Γ$\text{}_{8}$($\text{}^{2}$T$\text{}_{2g}$ ) → Γ$\text{}_{8}$($\text{}^{4}$A$\text{}_{2g}$), Γ$\text{}_{8}$($\text{}^{2}$E$\text{}_{g}$), Γ_8($\text{}^{2}$T$\text{}_{1g}$), Γ$\text{}_{6}$($\text{}^{2}$T$\text{}_{1g}$) for the Cs$\text{}_{2}$NaScCl$\text{}_{6}$:MoCl$\text{}_{6}^{3-}$ system in the Fm3m-space group. The experimental data available refer to the visible and near infrared luminescence spectra of MoCl$\text{}_{6}^{3-}$ complex ion in different hosts, such as Cs$\text{}_{2}$NaMCl$\text{}_{6}$ (M = Sc, Y, In), measured between 15,000 cm$\text{}^{-1}$ and 3,000 cm$\text{}^{-1}$ at liquid helium temperatures. At least, five luminescence transitions have been identified and assigned and each of them show extensive vibronic structure. A careful analysis of this experimental data shows that for the various observed electronic transitions, the vibrational frequencies change only slightly, and therefore there is no indication that the system undergoes both a significant and relevant Jahn-Teller distortion (along an active coordinate). There is however clear evidence that for the chloro-elpasolites, there is a strong resonance interaction between ν$\text{}_{3}$(τ$\text{}_{1u}$ : stretching) of the MoX$\text{}_{6}^{3-}$, complex ion and that of the host when M = In, Y. Thus for M = Sc, the slighter higher host ν$\text{}_{3}$, wave number is likely to minimize the effect of this coupling. This evidence will allow us for the Cs$\text{}_{2}$NaScCl$\text{}_{6}$:MoCl$\text{}_{6}^{3-}$ system to neglect, in the first-order approximation, the coupling among the internal and the external vibrations and to proceed using a both a molecular and the independent system models

Spectral Intensities for the Emission |4\text{}^{4}S3/2\text{}_{3}\text{}_{/}\text{}_{2} Γ8\text{}_{8}〉 → |4\text{}^{4}I15/2\text{}_{15}\text{}_{/}\text{}_{2} Γ〉in the Cs2\text{}_{2}NaErCl6\text{}_{6}

We report explicit vibronic intensity calculations for the |$\text{}^{4}$S$\text{}_{3}\text{}_{/}\text{}_{2}$ Γ$\text{}_{8}$> → |$\text{}^{4}$I$\text{}_{15}\text{}_{/}\text{}_{2}$ Γ>excitations in the Cs$\text{}_{2}$NaErCl$\text{}_{6}$ elpasolite-type systems, based upon new and updated experimental data, obtained from optical absorption measurements made at 10 K, in the energy range from 6,000 up to 26,000 cm$\text{}^{-1}$. Our calculation model is a generalization of the vibronic crystal field-ligand polarization method and the calculation is performed using a minimum set of adjustable parameters - all and each of them have a clear physical meaning. Our strategy was chosen so as to make a significant distinction with previous calculations, performed by other authors, who have worked out a model originated from a supra-parameterized scheme, within the framework of the superposition model of Newman. Throughout the course of the current work, it is shown that our model is suitable to handle this kind of calculations and also that the numerical results obtained are in fairly good agreement with experiment

Spectral Intensities for the Emission | 4

We report explicit vibronic intensity calculations for the |$\text{}^{4}$S$\text{}_{3}\text{}_{/}\text{}_{2}$ Γ$\text{}_{8}$> → |$\text{}^{4}$I$\text{}_{15}\text{}_{/}\text{}_{2}$ Γ>excitations in the Cs$\text{}_{2}$NaErCl$\text{}_{6}$ elpasolite-type systems, based upon new and updated experimental data, obtained from optical absorption measurements made at 10 K, in the energy range from 6,000 up to 26,000 cm$\text{}^{-1}$. Our calculation model is a generalization of the vibronic crystal field-ligand polarization method and the calculation is performed using a minimum set of adjustable parameters - all and each of them have a clear physical meaning. Our strategy was chosen so as to make a significant distinction with previous calculations, performed by other authors, who have worked out a model originated from a supra-parameterized scheme, within the framework of the superposition model of Newman. Throughout the course of the current work, it is shown that our model is suitable to handle this kind of calculations and also that the numerical results obtained are in fairly good agreement with experiment

Prediction of the Dynamic Behavior of Beams in Similitude Using Machine Learning Methods

The dynamic behavior of structures can be investigated using concepts of complete (exact) and incomplete (distorted) similitudes.The incompleteness is much more of interest since the complete similitudes are difficult to be achieved and the experiments are often executed using distorted models as test articles. In this work, beams in similitude have been investigated using machine learning to establish degrees of correlation between similar systems, without invoking governing equations and/or solution schemes. Machine learning is based on algorithms that derive models from sample inputs providing data-driven predictions. The absence of an explicit algorithm, being the process totally data-driven, confers to the approach a high versatility which allows its application even in the vibroacoustic research fields and problems. In view to validate the machine learning predictions, numerical investigation of beams in similitude has been performed. The good predictions obtained with machine learning highlight the potentialities of these algorithms and open the way to analyses with more complex structures

Evaluation of plates in similitude by experimental tests and artificial neural networks

In the last century, the introduction of similitude theory allowed engineers to define the conditions to design a scaled-up or down version of the full-scale structure by means of a set of tools known as similitude methods: the scaled structure can be tested more easily, and then, by using the scaling laws, the prototype behavior can be recovered. However, such a response reconstruction may become hard for complex structure under incomplete or distorted similitude frameworks. Machine learning methods, with their automating characteristics, may help to circumvent these difficulties. This work is divided into two parts. First, five clamped-free-clamped-free plates in similitude are experimentally tested. In the case of complete similitude, these laws allow to accurately reconstruct the response. When the similitude is distorted, these laws are not always valid, failing to predict the dynamic behavior in some of the frequency ranges. Then, the experimental results are used to validate the prediction and identification capabilities of artificial neural networks. The artificial neural networks proved to be robust to noise and very helpful in predicting the response characteristics and identifying the model type, although an adequate number of training examples is needed. Further tests proved that the number of samples is drastically reduced by choosing accurately the features