Julio Blanco Fernández

Reversible Binding of Solvent to Naked PbII Centers in Unusual Homoleptic Alkynyl‐Based Pt2Pb2 Clusters

We report a series of luminescent sandwich-type clusters [Pt2 Pb2 (C≡CR)8] (R=Tol, 1; C6 H4 OMe-3, 2; C6 H4 OMe-4, 3) with a dynamic Pt2 Pb2 metallic core, which is key to their intriguing stimuli-responsive photophysical properties. The solvent-free solids 1-3 display an orange emission ascribed to charge transfer from Pt-alkynyl fragments to a delocalized orbital with mixed Pt2 Pb2 /C≡CR nature, with a predominant lead contribution and Pb⋅⋅⋅Pb bonding character ((3) MLCCT/(3) IL). They exhibit mechanical, color, and luminescence changes that are reversible and perceivable with the naked eye, which are attributed to small inter- and intramolecular structural modifications induced by gentle grinding. Interestingly, 1 and 2 also exhibit remarkable and fast reversible vapochromic responses to donor solvent vapors (acetone, THFMe-2: yellow; NCMe: green, vs. dry solids: orange). The structures of 1(acetone)2 ⋅2(Me2 CO), 2(acetone)3 , and 2(THFMe-2)2 allow the vapochromic responses to be ascribed to the fast creation/disruption of solvate clusters [Pt2 Pb2 (C≡CR)8 Sx] (x≥2), with concomitant electronic and geometrical modifications within the Pt2 Pb2 core, which are easily accessible through a slight change in the stereochemical activity of the lone pair. The binding of one (or two) solvent molecules to Pb(2+) increases the Pb⋅⋅⋅Pb separation in the metallic core, causing a destabilization of the target orbital and larger energy gaps of the transitions. All the solvates exhibit remarkable rigidochromism upon a decrease in temperature, which is also associated with the gradual increase in the transannular Pb⋅⋅⋅Pb separation, as revealed by X-ray crystallography of 1(acetone)2 at different temperatures. Investigation of the crystal lattice of 1⋅CH2 Cl2 and 3⋅2 CH2 Cl2 further suggests that the lack of vapor stimuli response of complex 3 could be attributed to the presence of competitive additional secondary intermolecular Pb⋅⋅⋅O(OMe) contacts, which give rise to a more compact network built up from extended chains of clusters.

Novel mixed anion [trans-Pt(CCTol)2(CN)2]2− as a building block of new luminescent PtII–TlI and PtII–PbII coordination polymers

A new class of heteroleptic blue emitter anion, [trans-Pt(C[triple bond, length as m-dash]CTol)(2)(CN)(2)](2-), has been prepared and successfully used in the assembling of luminescent supramolecular heterometallic PtTl(2) and PtPb(2) species.

Artificial neural networks and acoustic emission applied to stability analysis in gas metal arc welding

Abstract The present paper describes the application of neural networks to obtain a model for estimating the stability of gas metal arc welding (GMAW) process. A neural network has been developed to obtain and model the relationships between the acoustic emission (AE) signal parameters and the stability of GMAW process. Statistical and temporal parameters of AE signals have been used as input of the neural networks; a multilayer feedforward neural network has been used, trained with back propagation method, and using Levenberg Marquardts algorithm for different network architectures. Different welding conditions have been studied to analyse the incidence of the parameters of the process in acoustic signals. The AE signals have been processed by using the wavelet transform, and have been characterised statistically. Experimental results are provided to illustrate the proposed approach. Finally a statistical analysis for the validation of the experimental results obtained is presented. As a main result of the study, the effectiveness of the application of the artificial neural networks for modelling stability analysis in welding processes has been demonstrated. The regression analysis demonstrates the validity of neural networks to predict the stability of welding process using the statistical characterisation of the signal parameters of AE that have been calculated.

Optimisation of friction-stir welding process using vibro-acoustic signal analysis

Abstract This paper presents a mathematical model to predict the tensile strength (TS) of friction-stir welded (FSW) AA1050 aluminium alloy joint. This model has been obtained from the optimisation of the parameters of models developed from vibro-acoustic signals produced during the process. The multiple response method was used to obtain the optimisation model. During experimental development, the process parameters selected to be assessed were rotation speed (RS), travel speed, and tool profile (TP). The TS of the welded joint and the root mean square (RMS) value of the vibro-acoustic signal in the frequency range of 3·2–6·4 kHz were analysed as response variables. In order to validate the model, the optimal values of the factors and responses are presented, showing their agreement with the experimental values obtained. This model can be used to develop and optimise the parameters of the automatic control of the FSW process, based on the vibro-acoustic signal generated.

Stability analysis of the gas metal arc welding process based on acoustic emission technique

A new criterion for stability analysis of the gas metal arc welding (GMAW) process is proposed and presented in this work, based on acoustic emission generated by the arc during short-circuiting metal transfer. For the experimental development an AWS ER70S-6 wire with a diameter of 0.8 mm and a DEP 401 rectifier were used. The weld bead was carried out on a 4-mm-thick AISI 1020 steel plate. Several welding conditions were studied with variation of the process parameters during the deposition of the beads. The acoustic emission signals were acquired using a measurement system composed of a MV-201 microphone, with a sensitivity of 10 ± 3 mV Pa− 1 and frequency bandwidth of 20 Hz to 170 dB to 100 kHz, and a data acquisition card coupled to a PC. A stability index was proposed. Eventually, a statistical analysis for validation of the obtained experimental results was carried out. The outputs allowed obtained a relationship between the acoustic signals and the arc voltage signals. The feasibility of the proposed index, and the effectiveness of the method as a novel means of analysing the stability of arc welding, was demonstrated based on acoustic emission for analyses of GMAW process stability.

Characterisation of friction stir spot welding process based on envelope analysis of vibro-acoustical signals

Abstract The evaluation of the relationship between the vibro-acoustical signals generated by friction stir spot welding (FSSW) process with its parameters and stages such as torque, dwell time, and tool rotation speed, is developed in this work. A dissimilar lap-joint was conducted using pure copper and AA1050 H24 aluminium alloy. The vibro-acoustical signals in Z and Y directions, and torque applied by the tool during the process have been acquired by the instrument NI USB-9234. The characterisation in the time and frequency domains of the vibro-acoustical signals has been performed in order to correlate them with the stages of processing and tool torque. The analysis has confirmed that the vibro-acoustical signals were significantly affected by changes in process forces and can thus be used as a non-destructive method for process monitoring, optimisation tasks, and control of FSSW process.

Environmental Impact of Modern Wind Power under LCA Methodology

Renewable energy sources constitute an alternative to fossil fuels and their problems, which are, on the one hand, the pollution and CO2 emissions that they produce and, on the other hand, the diminution of reserves, in addition to other economical and political problems, such as their increasing prices and the economic dependence of non-producers countries on those that produce fossil fuels. At the present time, renewable energy, and particularly wind power energy, is becoming increasingly relevant in the world’s electricity market, based on its advances and on the legislative support of governments in several countries (Rio del & Unruh, 2007; JagerWaldau, 2007; Karki, 2007; Breukers & Wolsink, 2007), for instance with legal frameworks presenting stable and lasting premiums. Figure 1 shows the contribution and the provisions of wind power to the electricity supply network in several countries, both at a European and world level; current forecasts predict that wind power will contribute 12% of the global demand for electricity by 2020 (GWEC, 2005). This huge boom in implementation and forecasts of this power source justify the need to increase its people’s understanding (Jungbluth et al., 2005; Gurzenich et al., 1999), based on scientific studies, especially from the point of view of its environmental impact. Wind power produces electrical energy from the kinetic energy of the wind, without directly producing any pollution or emissions during the conversion process, but this does not means that it is free of contamination or CO2 emissions. The question is that it should be considered that there is an environmental impact due to the manufacturing process of the wind turbine and the disposal process at the end of the wind turbine life cycle. And this environmental impact should be quantified in order to compare the effects of the production of energy, and to analyse the possibilities of improvement of the process from that point of view. Thus, the aim of this chapter is to show a methodology of analysis of the environmental impact of the wind energy technology, considering the whole life cycle of the wind power systems. The application of the ISO 14040 standard (ISO, 1998) allows us to quantify the overall impact of a wind turbine and each of its components from a Life Cycle Assessment (LCA) study. It also allows us to analyse the issues that produce more impact and the aspects that could be improved in order to reduce the effective impact. The LCA model has been developed with the purpose of determining and quantifying the related emissions and

AVERÍAS EN AEROGENERADORES: DETECCIÓN A PARTIR DE DATOS SCADA

This paper proposes a methodology for the prediction and detection of possible failures of major components of a wind turbine , from data collected by a SCADA system (Supervisory Control And Data Acquisition) monitoring incorporated therein . This artificial intelligence techniques are applied , such as trees or boosted neural networks for modeling the behavior and optimal selection of input parameters. Finally , once defined the methodology is applied to a real case of fault in a gearbox with data from a wind farm in the Aeolian Property Group Riojanas (GER ) located in La Rioja ( Spain ) . The combination of a detailed analysis of the optimal parameters to model the specific behavior of the temperature of the gearbox, along with the development of a neural network model based study to characterize efficiently the normal behavior of the gearbox without deterioration in performance . This allows you to analyze periodically the possible deterioration process of the gearbox and act on it before irreparable damage occurs and requiring replacement.

SEÑALES VIBROACÚSTICAS PARA EVALUAR GEOMETRIAS DE HERRAMIENTA EN LA SOLDADURA POR FRICCION AGITACION

RESUMEN: Este articulo presenta la investigacion realizada para evaluar la relacion existente entre el diseno de herramientas y las senales vibroacusticas emitidas durante el proceso de soldadura por friccion-agitacion del aluminio AA1050. Se ha realizado la caracterizacion de la senal vibroacustica en el dominio del tiempo y la frecuencia, asi como su analisis estadistico, para correlacionarlas con dos disenos de herramienta diferentes. Las senales vibroacusticas han sido filtradas con el objetivo de eliminar el ruido generado por la maquina empleada, y se ha empleado la transformada wavelet como una eficiente herramienta para el filtrado de la senal. El analisis estadistico confirma que las senales vibroacusticas se ven afectadas significativamente por el diseno de la herramienta, y como resultado fundamental se demuestra que las senales vibroacusticas generadas por este proceso pueden emplearse eficientemente en la caracterizacion del efecto de los parametros y el diseno de la herramienta Palabras clave: Soldadura por friccion-agitacion, vibraciones, procesamiento de senales, Aluminio, diseno de herramientas, transformada wavelet.

PREDICCIÓN Y DETECCIÓN DE AVERÍAS EN AEROGENERADORES A PARTIR DE DATOS SCADA

This article proposes a method for predicting and detecting potential failures in the main components of a wind turbine, based on data collected by a built-in SCADA (Supervisory Control And Data Acquisition) monitoring system. Artificial intelligence techniques such as neural networks and boosted trees are used to model the behaviour of the system and to select optimal input parameters. Once the method has been defined, it is applied to an actual case study of a malfunction in a multiplier, with data from a wind farm located in la Rioja (Spain), owned by Grupo Eolicas Riojanas (GER). The combination of a detailed study of the optimal parameters for modelling the specific behaviour of the temperature of the multiplier and the development of a model based on neural networks enables the normal behaviour of a multiplier to be modelled effectively with no deterioration in its operation. This means that the potential processes of deterioration in the multiplier can be analysed regularly and consequently actions can be taken accordingly before an irreparable malfunction requiring replacement.