Defect Detection in Delaminated Glass-Fibre/Epoxy Composite Plates Using Local Defect Resonance Based Vibro-Thermography Technique

In the present scenario, composites are widely used for various applications in the field of aerospace, automobile, marine, sports, construction and electrical industries. The need of damage inspection for these composite structures has been of great importance. Complicated defects like delaminations present in the composite laminates can be detected effectively using nonlinear acoustic wave spectroscopy (NAWS). One of the NAWS techniques of detecting the delamination is based on intensification of vibration amplitudes at the delamination location, known as local defect resonance (LDR) technique. In this chapter, a numerical investigation for detecting delamination in glass fibre reinforced polymer (GFRP) composite based on vibrothermography technique will be discussed. A single periodic LDR frequency excitation is used to excite the GFRP plate, resulting in a local temperature rise at delamination region due to frictional heating at the damage interface. An explicit dynamic temperature displacement analysis will be carried out for a specific time period of LDR excitation. Subsequently, a heat transfer analysis will be performed to observe the temperature difference at top surface of the delaminated GFRP plate. Thus a numerical investigation will be carried out based on LDR excitation for high contrast imaging of delamination in composite materials using vibro-thermography

Sodium boiling Detection in a LMFBR Using Autoregressive Models and SVM

International audienceThis paper deals with acoustic detection of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) cooled by liquid sodium. As sodium boiling induces acoustic emission, the method consists in real time analysis of acoustic signals measured through wave guides. AutoRegressive (AR) models are estimated on sliding windows and are classified in boiling or non-boiling models using Support Vector Machines (SVM). One of the difficulties to cope with is disturbances due to the influence of some environment noises like the liquid coolant cavitation, vortex flow, shaft vibration and mechanical pump noise. These disturbances can generate false alarms or mask the boiling. The proposed method is designed to be robust toward these disturbances. Furthermore, the SVM are designed to be robust toward the operating mode changing. The application for online monitoring is made on data obtained from French nuclear power plant Phenix and boiling sound signals generated from Laboratory experiments. Different acoustic boiling sound levels are used and the effectiveness of the method is shown by the good detection rate and its low false alarm rate even for low acoustic boiling sound level

An efficient route to group 6 and 8 metallaborane compounds: synthesis of arachno-[Cp*Fe(CO)B₃H₈] and closo-[(Cp*M)₂B₅H₉] (M = Mo, W)

Reaction of [Cp*Fe(CO)2I] (Cp* = η5-C5Me5) with an excess amount of BH3·thf in toluene at 75 °C or with LiBH4 at –78 °C leads to the isolation of hydrogen-rich ferraborane arachno-[Cp*Fe(CO)B3H8] in good yield. In a similar fashion, reaction of [Cp*M(CO) 3Cl] (M = Mo and W) with an excess amount of BH3·thf at 80 °C or at –78 °C with LiBH4 yielded metallaboranes [(Cp*M)2B5H9] (M = Mo, W). Isolated yields of closo-[(Cp*M)2B5H9], (M = Mo and W), both from LiBH4 and BH3·thf, are good. All compounds were characterized in solution by IR, 1H, 11B, and 13C NMR spectroscopy and mass spectrometry and the structural types were unequivocally established by crystallographic analysis of arachno-[Cp*Fe(CO)B3H8]

Autoregressive model-based boiling detection in a Liquid Metal Fast Breeder Reactor

International audienceThis paper presents a new approach for acoustic detection of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) based on Autoregressive (AR) models. The AR models are estimated on a sliding window and classified into boiling or non-boiling models by comparing the on-line estimated values of their components to the predictions of their components from the environment parameters using linear regression. In order to avoid false alarms the proposed approach takes into account operating mode information. Promising results are obtained on the background noise data collected from the French Phenix nuclear power plant provided by the French Commission of Atomic and Alternative Energies (CEA)

Acoustic monitoring of sodium boiling in a Liquid Metal Fast Breeder Reactor from autoregressive models

International audienceThis paper deals with acoustic monitoring of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) based on Auto Regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium-water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and Support Vectors Machines (SVM). The proposed approach takes into account operating mode informations in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l'Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected