Time filter

Source Type

Esch - sur - Alzette, Luxembourg

Poncot M.,CNRS Jean Lamour Institute | Addiego F.,CRP Henri Tudor | Dahoun A.,CNRS Jean Lamour Institute
International Journal of Plasticity | Year: 2013

A model enabling the determination of the intrinsic mechanical constitutive equations of uniaxially stretched polymers is presented. This model takes into account the cavitation-induced volume strain which can occur during the deformation of such materials. In particular, the true intrinsic axial stress and strain depends on the overall volume strain and a form factor depicting the evolution of the voids shape. Based on our model, the true intrinsic behaviour of high-density polyethylene (HDPE), polypropylene/ethylene-propylene rubber (PP/EPR), and polyethylene terephtalate (PET) was assessed in tension. Compared to the overall true behaviour, the intrinsic true behaviour of the materials did not exhibit anomalies at large strain levels with changing experimental parameters (strain rate and temperature), and can be accurately predicted by means of phenomenological constitutive equations as the one proposed by G'sell and Jonas (1979). © 2012 Elsevier Ltd. All rights reserved.

CRP Henri Tudor | Date: 2014-08-01

The present invention relates to a method for producing a superamphiphobic coating on a substrate, said method comprising the steps of a) providing a substrate, b) generating a plasma in a treatment space, under atmospheric pressure, using a dielectric barrier discharge, by supplying a plasma gas (

This study aims to identify the influence of co-operation practices and the use of internal and external information sources on the propensity of firms to introduce new to the market innovations in the service sector. Data come from the 4th Community Innovation Survey, which covers the years 20022004. A logistic regression model is applied with the degree of novelty of good/service innovation as dependent variable. The analysis of the parameter estimates shows that firms provided with information from market sources and from internal sources as well as firms involved in science-based collaboration for their product innovations are more likely to introduce new to the market innovations, whereas information coming from competitors seems to have a negative influence on the degree of novelty of innovation. © 2010 Elsevier Ltd. All rights reserved.

Agency: Cordis | Branch: FP7 | Program: CP | Phase: ENV.2012.6.3-1 | Award Amount: 3.85M | Year: 2012

The bio-electrochemically-assisted recovery of valuable resources from urine (ValueFromUrine) project will develop, optimize and evaluate an innovative bio-electrochemical system that allows for the recovery of phosphorus (P), ammonia (NH3) and electricity (E) or hydrogen from urine. The innovative principle is that biological oxidation of organics (present in urine) at a bio-anode drives both the transport of ammonium over a membrane (which allows the recovery of NH3) and the production of alkalinity (which can be utilized for the precipitation of P-salts). Toilets and urinals that collect urine separately from other wastewater streams, are increasingly being installed in newly constructed utility buildings or during renovation of old buildings. Unlike any state-of-the art technology, the ValueFromUrine technology not only has the potential to recover over 95% of the P and NH3 from urine, but also to produce chemicals (NaOH, KOH) and energy. The ValueFromUrine consortium is made up of complementary knowledge institutes, SMEs and industry partner, each of them leading in one or more relevant fields (electrochemistry, membrane technology, microbiology, micro-pollutants and decentralized wastewater treatment). Moreover, all commercial partners have experience in the validation of new technologies. The participating SMEs have a key function in the consortium, which is reflected by the fact that 41% of the requested funding will go to the SMEs for research and technology development.

Marvuglia A.,CRP Henri Tudor | Messineo A.,Kore University of Enna
Applied Energy | Year: 2012

The estimation of a wind farm's power curve, which links the wind speed to the power that is produced by the whole wind farm, is a challenging task because this relationship is nonlinear and bounded, in addition to being non-stationary due for example to changes in the site environment and seasonality. Even for a single wind turbine the measured power at different wind speeds is generally different than the rated power, since the operating conditions on site are generally different than the conditions under which the turbine was calibrated (the wind speed on site is not uniform horizontally across the face of the turbine; the vertical wind profile and the air density are different than during the calibration; the wind data available on site are not always measured at the height of the turbine's hub).The paper presents a data-driven approach for building an equivalent steady state model of a wind farm under normal operating conditions and shows its utilization for the creation of quality control charts at the aim of detecting anomalous functioning conditions of the wind farm. We use and compare three different machine learning models - viz. a self-supervised neural network called GMR (Generalized Mapping Regressor), a feed-forward Multi Layer Perceptron (MLP) and a General Regression Neural Network (GRNN) - to estimate the relationship between the wind speed and the generated power in a wind farm. GMR is a novel incremental self-supervised neural network which can approximate every multidimensional function or relation presenting any kind of discontinuity; MLPs are the most widely used state-of-the-art neural network models and GRNNs belong to the family of kernel neural networks. The methodology allows the creation of a non-parametric model of the power curve that can be used as a reference profile for on-line monitoring of the power generation process, as well as for power forecasts. The results obtained show that the non-parametric approach provides fair performances, provided that a suitable pre-processing of the input data is accomplished. © 2012 Elsevier Ltd.

Discover hidden collaborations