Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building materials

The actual European energy context highlights the building sector as one of the largest sectors of energy consumption. Consequently, the “Energy Performance of Buildings Directive”, adopted in 2002 and focusing on energy use in buildings, requires all the EU members to enhance their building regulations and to introduce energy certification schemes, with the aim of both reducing energy consumption and improving energy efficiency. That is why carrying out an energy performance diagnosis is mandatory, notably when buying or selling properties. Indeed, invisible defaults, like, for example, non-emerging cracks or delaminations, could have a detrimental effect on insulating qualities. Esimaing in situ thermo-physical properties allowing locating these defaults, the present work focuses on proposing new and efficient approaches based on the use of both artificial intelligence tools (artificial neural networks and neuro-fuzzy systems) and inverse methods for characterizing building materials i.e. for estimating their thermal diffusivity using thermograms obtained thanks to a non-destructive photothermal method.     

Local pressure stresses on lateral pipe-nozzle with various angles of intersection

This paper reports the variations of local pressure stress factors (local pressure stress/internal pressure) at the juncture of a pipe-nozzle when the angle of the pipe-nozzle intersection varies from the standard 90° to the 30° lateral. These stress factors in both the circumferential and longitudinal directions at four symmetric points around the pipe-nozzle juncture are plotted as functions of the angle. The ALGOR finite element software was employed to model the true pipe-nozzle geometry. The numerical stress results come from a pipe-nozzle with geometric parameters, beta, β, (nozzle mean radius/pipe mean radius) of 0.5, and gamma, γ, (pipe thickness/pipe mean radius) of 50.0. This study shows that the 90° intersection, a standard pipe-nozzle, exhibits relatively less severe local stresses. These stresses are increasing as the angle of intersection is decreasing from 90° and become more severe when the angle of intersection is further decreasing from 45°. The circumferential stress at the inside crotch point exhibits the worst stress value. The stress factor results in pipe circumferential direction at the inside crotch points are in good agreement with the stress factor equation from the ASME boiler and pressure vessel code.     

Training strategies for neural network soft classification of remotely-sensed imagery

Recently the 'soft' classification approach has gained in popularity against the discrete (hard) classification of land cover from remotely-sensed imagery. An empirical study is presented to test training procedures with neural networks for soft (mixture) classification. The results show thatland cover mixtures are best recognized following training with two-component mixed pixels, and that linearly re-scaled or binned target vector representations are equally satisfactory. Interestingly, dominant classes within pixels are also better recognized by training with wider varieties of class mixtures.     

Balanced Parametrizations of Stable SISO All-Pass Systems in Discrete Time

A balanced canonical form for discrete-time stable SISO all-pass systems is obtained by requiring the realization to be balanced and such that the reachability matrix is upper triangular with positive diagonal entries, in analogy to the continuous-time balanced canonical form of Ober [O1]. It is shown that the resulting balanced canonical form can be parametrized by Schur parameters. The relation with the Schur parameters for stable AR systems is established. Using the structure of the canonical form it is shown that, for the space of stable all-pass systems of order less than or equal to a fixed number n, the topology of pointwise convergence and the topology induced by H ₂ coincide. The topological space thus obtained has the structure of a hypersphere. Model reduction procedures based on truncation, which respect the canonical form, are discussed. Date received: April 1, 1997. Date revised: April 9, 1999.     

Optimization of the synthesis of polyhydroxyurethanes using dynamic rheometry

Polyhydroxyurethanes (PHUs) produced by the reaction between dicyclocarbonate and diamine groups are often presented as possible candidates to substitute for classical polyurethanes based on isocyanate precursors. In the literature, the synthesis of this class of polymers is often performed according to arbitrary conditions of time and temperature without any scientific justification. As such, the real potential of PHUs is probably not fully known. Numerous contradictions in previously published results seem to support this hypothesis. Our paper proposes two methodologies based on dynamic rheometry to determine optimized conditions for the synthesis of PHUs. The case of a PHU formed by the reaction between 1,10‐diaminodecane and a dicyclocarbonate bearing a central aromatic group is described more precisely. The first approach consists of conducting various rheological experiments (kinetics, thermomechanical analyses) in situ on the reaction mixture. The second one retains the same technique to qualify the viscoelastic properties of PHUs synthesized according to various conditions. In this latter case, all samples show thermomechanical behaviour of amorphous thermoplastic polymers. But discrepancies are observed with regard to the value of the glass transition temperature and the existence or not of a rubbery zone. Comparison of these data with size exclusion chromatography results shows that these differences are direct consequences of the polymer molecular weight that can be predicted using macromolecular theory. The properties of the PHUs obtained after optimization of the polymerization reaction were compared with literature data in order to complete the evaluation of the efficiency of the rheological methodology. Copyright © 2012 Society of Chemical Industry