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Analyse comparée de méthodes de résolution du couplage conduction-rayonnement dans des matériaux hétérogènes semi-transparents
No full textInternational audienceThis article compares numerical methods developed by seven french research teams attached to the GDR CNRS 2047 TAMARYS for coupled conduction-radiation for coupled conductionradiation heat transfer resolution in heterogeneous semi-transparent media. The teams work on a common configuration, analyzing net heat flux (total, conductive, radiative) and temperature profiles. The study highlights the benefits and limitations of each approach concerning the selected configuration, with each providing a unique perspective that enhances the others.Cet article compare les méthodes numériques de simulation développées par sept équipes de recherche françaises rattachées au GDR CNRS 2047 TAMARYS collaborant pour la résolution d’un problème couplé conduction-rayonnement dans un milieu hétérogène semi-transparent. Les équipes ont travaillé sur une configuration commune, analysant les profils de températures et de flux nets (totaux, conductifs et radiatifs). Cette étude met en évidence les points forts et limites de chaque méthode au regard de la configuration choisie, chacune offrant une perspective distincte et complémentaire aux autres
Measurement and Correlation of Vapor–Liquid Equilibrium for Difluoromethane ( R 32) + trans -1,1,1,4,4,4-Hexafluoro-2-butene (R1336mzz(E)) at Temperatures from 302.85 to 342.69 K
No full textInternational audienceHeat pump, organic Rankine cycle, or refrigeration cycle require the knowledge of phase equilibrium properties, particularly if multicomponent systems are considered. In this study, we present new experimental data of vapor–liquid equilibrium (VLE) for the binary systems of difluoromethane (R32) + trans-1,1,1,4,4,4-hexafluoro-2-butene (R1336mzz(E)) at temperatures from 302.85 to 342.69 K. The VLE data were measured by a “static-analytic”-type apparatus which was equipped with two electromagnetic capillary samplers (ROLSI, Armines’s patent). The experimental VLE data were correlated by the Peng–Robinson (PR) equation of state (EoS) associated with the Mathias and Copeman (MC) alpha function and van der Waals (vdW) mixing rules. The modeling results of the PRMC-vdW model were in good agreement with the measured data. The VLE data were compared with the prediction model, namely, PPR78, and it was found that the PPR78 model overestimated the bubble pressure of R32 + R1336mzz(E). Moreover, the binary interaction parameter (BIP) was also correlated considering a second-order polynomial expression between the BIP (kij) and acentric factor (ω) for R1336mzz(E)-contained binary mixtures
Problèmes de configuration de flottes hétérogènes pour la planification de tâches
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Using machine learning as a supportive tool to systematically study the tensile properties of a Ti-6Al-2Sn-4Zr-2Mo-Si alloy with various microstructures: Effects of texture, phase proportions and grain size
No full textInternational audienceThis study investigates the effects of microstructures on the tensile properties of a Ti-6Al-2Sn-4Zr-2Mo-Si alloy with a hot-rolled T-split texture. A statistical approach using machine learning is implemented to support the systematic study for a more quantitative analysis. The comparative study of the tensile properties of equiaxed (α + β), bimodal (α + β), and duplex (α + α′) microstructures shows that the duplex (α + α′) stands out with the best strength (UTS) – ductility and work hardening – ductility balances. Especially, the duplex (α + α′) reveals an outstanding work hardening ability and a great ductility as compared to the equiaxed (α + β) and bimodal (α + β). Through conventional means, the impacts of the different microstructural parameters were qualitatively investigated, and the parameters were ordered from most impactful to less impactful: the type of microstructure (equiaxed, bimodal, or duplex), followed by the orientation of the deformation axis (parallel or perpendicular to the rolling axis), the phase proportion (fraction of primary α), and finally the grain size. Due to the difficulties of accurately measuring such impacts, a machine learning method with a statistical approach to handle small datasets was implemented to understand more quantitatively the roles of the microstructural parameters. Thanks to the SHAP values, the feature importances of the models trained to predict the proof stress (Rp02), ultimate tensile strength (UTS), plastic elongation (Ap%), and work hardening exponent (n) were successfully computed. It turns out the different features were ranked in the same order than during the experimental study. Moreover, the interactions of the parameters with the tensile properties could also be examined. For example, the machine learning models understood that decreasing the grain size would increase the material strength (Hall-Petch effect), increasing the primary α fraction would increase the ductility etc. Additionally, the coefficient of determination (R2) and the mean absolute error (MAE) metrics were computed to measure the trustability of the machine learning results. It comes out this level of trust could be improved by optimizing the hyperparameters of the gradient boosting regression (GBR) algorithm we used in this study. Considering the size limitation of our small dataset (73 samples), a statistical approach consisting of a manual cross-validation process performed on many training/testing sets was implemented. Satisfactory R2 scores of 0.81, 0.67, and 0.77 were respectively found for the Rp02, Ap%, and n models, attesting the relevancy of their feature importances. However, the UTS models showed a smaller performance of 0.28, indicating a level of trust insufficient regarding its computed feature importances. Finally, it appears that the approach used in this study, which is not particularly suited for the prediction of new data due to our manual cross-validation process, is indeed relevant for the study of feature importances. Therefore, it is considered that machine learning is a powerful tool for helping us understanding better the roles of microstructures and their effects on the macroscopic properties
Data-Driven Approach for the Job Sequencing and Tool Switching Problem with Non-Identical Parallel Machines and Stochastic Processing Times
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Slip localization and grain boundary sliding analysis at sub-voxel resolution using phase contrast tomography
No full textInternational audienceMicroplasticity of a polycrystalline Ni-based superalloy was investigated using phase contrast tomography (PCT) and laser scanning confocal microscopy (LSCM). Incremental tensile testing was performed on three miniaturized specimens to investigate strain localization at low plastic deformation at room temperature and 650 ∘C. Microplasticity events, such as slip activity, deformation twinning, and grain boundary sliding, are free to emerge at the specimen surface and generate sub-micrometer topographic features. High resolution digital image correlation was conducted using LSCM to have a description of the in-plane and out-of-plane kinematics of the specimen surface. Despite slip amplitudes substantially smaller than the voxel size, PCT was capable to evidence the out-of-plane component of slip traces at the onset of plasticity. The technique was also used at 650 ∘C, a temperature at which grain boundary sliding occurs, but surface reactivity is severe enough not to allow for topographic measurements using LSCM. Therefore, PCT was found particularly adapted to evidence “surface” microplasticity events hidden by an extra surface oxidation layer
Role of oxidation in thermal fatigue damage mechanisms and life of X38CrMoV5 (AISI H11) hot work tool steel
No full textInternational audienceHot work tools steels, which are submitted to severe solicitations in service, are often damaged by thermal fatigue and corrosion. Interrupted Thermal Fatigue experiments were performed on X38CrMoV5 tool steel between 100 and 650 °C, in air and reduced oxygen partial pressure atmospheres after primary or secondary vacuum. A thermal and thermo-mechanical analysis by finite element method was carried out to estimate the strains and stresses undergone by the axisymmetric disc-shaped specimen during thermal cycling. The morphology, structure and phase composition of the oxide layer were analysed using scanning electron microscopy, energy dispersive spectroscopy and X-ray diffraction. It was shown that the thickness, homogeneity, structure, and compacity of the oxide multilayer formed on the specimen changed depending on test atmosphere. In air, crack initiation and propagation were strongly assisted by oxidation, leading to reduced fatigue life compared to inert atmospheres. Steel softening was highlighted in sub-surface, whatever the test atmosphere
Case study: The downside of using a worst‐case approach in occupational safety policy as an interpretation of the precautionary principle: Putting the uncertain UXO occupational safety risk into probabilistic perspective
No full textInternational audienceUnexploded ordnance (UXO) from the World Wars on the North Sea floor pose an uncertain occupational safety risk for dredging and cable installation. At present mitigation strategies are based on an interpretation of the precautionary principle that uses a worst‐case approach, that is, assuming that UXO will be encountered, will explode, and will harm people onboard. We propose a probabilistic framework to estimate the UXO risk. Using this probabilistic framework, we conclude that the UXO risk during cable installation meets the prevailing safety standard in the Netherlands. Furthermore, we demonstrate that the UXO risk is lower than the general maritime risk, that is, the occupational health risk caused by the mitigation is higher than the UXO risk itself. We conclude that even for uncertain occupational risks, such as the UXO risk in the North Sea, a probabilistic analysis can be more instrumental in the decision‐making process on accepting and mitigating risks than using worst‐case scenario thinking
Predictive Vision of Physics of Decision : Modelisation of Virus Propagation with Force Fields Paradigm
No full textInternational audienceThis paper introduces a novel method for simulating complex system behaviors using a specific geometric space and force fields within that space. The approach considers the system's performance as a physical trajectory defined by its performance indicators and environmental attributes, which can be deviated by force fields representing risks or opportunities within the system. The primary contribution of this work is the proposal of a method that uses multiple trajectories of a defined system to identify force fields that accurately represent the system
