Non-destructive inspection of polymer sample using a periodically moving heating source

Introduction The development of innovative materials with specific properties requires the design of non-destructive testing methods. The proposed study is focused on the localization of a possible defect in a polymer sample. In such an aim, a frequency analysis based on a periodic heating can reveal the defect location (see authors previous works). However this approach is usually time-consuming and this feature could reduce the method attractiveness in an industrial context. In the proposed communication, a new protocol has been developed in order to reduce the inspection duration. Methods Let us consider that the material to be studied is a polymer plate. On the upper face of this plate, a radiative heater is considered. Its power supply is kept constant. Its spatial distribution is limited to a disc of small radius r. Moreover, this source moves circularly so as to heat the plate. Once the steady state established, the temperature at each point of the sample is periodic. The frequency of the oscillations is related to the angular velocity of the source. Two observable characteristics of these "thermal waves" can then be taken into account at each point of the surface: the modulus and the phase shift of the thermograms. It has been shown that modulus is more relevant for defect location. Results and Discussion An example of thermograms is shown on figure I. The contrast distribution (difference of two cartographies of modulus with and without defect) is presented on figure II. Considering this numerical example, the whole plate inspection is performed and methods feasibility is exposed. Several concrete results based on our experimental device will be exposed during the conference

An original non destructive technique for fibre rate measurement

This study presents an original method for estimating the rate of resin content in orthotropic layeredcomposite materials while avoiding their destruction. The composite materials considered, commonly used in aeronautic, result of the impregnation of fibre reinforced by epoxy resin. Thermal or mechanical analyses using numerical tools are essential to optimize their implementation, and to better understand their behaviour. The prediction quality of these tools requires the knowledge of input parameters with enough accuracy. Usually dedicated to the identification of the thermal diffusivity, the proposed approach consists in comparing the responses obtained on thermal resin and these obtained on pre-impregnated fibres under periodic thermal waves. An experimental device has been developed and a test campaign has been conducted. The modelling of heat transfer through the complex temperatures method has also been implemented

Étude de revêtements intumescents sous flux solaire concentré et modélisation numérique

La protection des matériels militaires contre les agressions thermiques est uneproblématique capitale dans le domaine de la défense. Dans ce contexte, les revêtements intumescents offrent une solution efficace et aisée à mettre en oeuvre : lorsqu’ils atteignent une température seuil, ces revêtements gonflent pour former une couche isolante performante. Cette étude présente une série d’essais expérimentaux réalisés au moyen d’un four solaire de 45 kW et propose la méthode de la transformation de Landau pour faciliter la modélisation des phénomènes de gonflement

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

Citation: Rutkoski, J., . . . Singh, R. (2016). Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat. G3-Genes Genomes Genetics, 6(9), 2799-2808. https://doi.org/10.1534/g3.116.032888Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots