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Le refus de se déporter ne méconnaît pas en lui-même le principe d'impartialité lorsque l'autorité hiérarchique n'est pas personnellement mise en cause
No full textInternational audienceNote sous CAA Lyon, 20 juin 2024, no 22LY02345 (C+
Les dark patterns et le droit des pratiques commerciales trompeuses et agressives. La manipulation par l’esthétisme sanctionnable
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Generative-machine-learning surrogate model of plasma turbulence
No full textInternational audienceGenerative artificial intelligence methods are employed for the first time to construct a surrogate model for plasma turbulence that enables long-time transport simulations. The proposed GAIT (Generative Artificial Intelligence Turbulence) model is based on the coupling of a convolutional variational autoencoder that encodes precomputed turbulence data into a reduced latent space, and a recurrent neural network and decoder that generate new turbulence states 400 times faster than the direct numerical integration. The model is applied to the Hasegawa-Wakatani (HW) plasma turbulence model, which is closely related to the quasigeostrophic model used in geophysical fluid dynamics. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, and the flow topology characterized by the Okubo-Weiss decomposition. The GAIT model also reproduces Lagrangian transport including the probability distribution function of particle displacements and the effective turbulent diffusivity
Non intrusive interpolation and low rank approximation methods for nonlinear parametric models. Application to mechanical engineering.
No full textInternational audienceWe propose real-time approximation methods for multi-parametric static nonlinear mechanical problems. Data assimilation is based on high-fidelity computations provided by a solver in a black-box routine. We suppose that there exists an approximation that accurately captures the data in the parameter space and that it has low rank intrinsic structure. In [1] the proposed Sparse Subspace Learning (SSL) method is a non-intrusive hierarchical collocation framework coupled with an incremental low-rank approximation (iRSVD). But, the Lagrange interpolation and the constrained collocation coupled to the hierarchical algorithm lead to oscillations and error issues. Therefore, two different approaches are proposed in order to circumvent those. Firstly, iRSVD is coupled to a regression based on Sobolev norm (MSN). This makes it possible to remove Runge oscillation when too many samples and parameters are used. Secondly, we couple rank revealing randomized SVD [2] and the sparsity-promoting regression framework SINDy [3]. We seek to build a library of multidimensional basis functions and then identify by hard thresholding the most pertinent coefficients in the regression. Our method generalizes the construction of the basis library in an incremental manner using Smolyak’s rule. The aim is to mitigate the curse of dimensionality we are subjected to when building the library in high dimensions. In both methods, sampling is considered fully random in the parametric domain. Specific strategies, and incremental procedures make it possible to reduce the number of high fidelity computations and ensure a global tolerance for the reduced order models. Hence we obtain a real time interactive software. Studies of academic and realistic models are proposed with material, process or load parameter variations. REFERENCES [1] Borzacchiello, D., Aguado, J.V. & Chinesta, F. Non-intrusive Sparse Subspace Learning for Parametrized Problems. Arch Computat Methods Eng, 26, 303–326, 2019. [2] A Rank Revealing Randomized Singular Value Decomposition (R3SVD) Algorithm for Low-rank Matrix Approximations, H. Ji, W. Yu, Y. Li, arXiv:1605.08134 [cs.NA], 2016. [3] Brunton SL, Proctor JL, Kutz JN. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. 113, 3932–3937, 2016
Influence of void presence on the elastic behavior of carbon nanotube-reinforced polymer biocomposites
No full textInternational audienceThe presence of voids substantially influences the elastic response of carbon nanotube (CNT)-reinforced polymer biocomposites, crucial for developing sustainable materials for environmental applications. This research examines the effect of voids on the mechanical properties of alfa-based composites, particularly relevant for Mediterranean regions, where alfa fibers are readily available. Employing a computational approach, we analyze the elastic modulus of biocomposites with varying void content. Our findings reveal that voids diminish the effective load-bearing capacity of the polymer matrix, consequently weakening the reinforcement efficiency of the embedded CNTs. Furthermore, the distribution and the size of voids are demonstrated to significantly impact the mechanical performance of the composites. Comprehending these effects is vital for optimizing the design and manufacturing processes of CNT-reinforced polymer biocomposites, ensuring their reliability and effectiveness in Mediterranean environmental conditions
Caractérisation rapide des propriétés en fatigue d’un composite stratifié sous sollicitations hors plan et de compression à partir d’essais d’auto-échauffement
No full textDue to their technological maturity, laminated composites now enable the design of complex and optimized parts, some of which require fatigue justification with the aim of achieving long service lives. However, this poses a major challenge for the design of composite parts, as the visco-elasto(plastic) nature of organic matrices limits loading frequencies to around ten Hz. In this context, the objective of the study is to propose a methodology based on self-heating to rapidly characterize the fatigue properties of a unidirectional carbon/epoxy laminate subjected to in-plane compression and out-of-plane shear loading. To address this issue, experimental campaigns were conducted to characterize the failure scenarios of laminates under targeted cyclic loadings. The configurations of interest were also subjected to thermomechanical analyses, primarily based on infrared measurements. The analysis strategies developed for processing self-heating tests allow access to the source term associated with energy dissipation. This characterization is complemented by a modeling of the resin’s role in dissipation, thereby establishing the link between its nonlinear viscoelastic behavior and the fatigue response.Grâce à leur maturité technologique, les composites stratifiés permettent aujourd’hui de concevoir des pièces complexes et optimisées, dont certaines nécessitent une justification en fatigue, avec la volonté de viser des durées de vie importantes. Cependant, cela représente un défi majeur pour le dimensionnement des pièces composites, car la nature visco-élasto(-plastique) des matrices organiques limite les fréquences de sollicitation à une dizaine de Hz. Dans ce contexte, l’objectif de l’étude est de proposer une méthodologie basée sur l’auto-échauffement pour caractériser rapidement les propriétés de fatigue d'un stratifié de plis unidirectionnels carbone-époxy soumis à des sollicitations de compression dans le plan et cisaillement hors-plan. Pour répondre à cette problématique, des campagnes expérimentales ont été menées afin de caractériser scénario de ruine des stratifiés soumis aux sollicitations cycliques ciblées. Les configurations d’intérêt ont également fait l’objet d’analyses thermomécaniques, reposant principalement sur des mesures infrarouges. Les stratégies d’analyse développées pour l’exploitation des essais d’auto-échauffement permettent d’accéder au terme source associé à la dissipation énergétique. Cette caractérisation est complétée par une modélisation du rôle de la résine dans la dissipation, établissant ainsi le lien entre son comportement viscoélastique non-linéaire et la réponse en fatigue. L’ensemble de ces travaux permet de proposer des méthodologies de dimensionnement en fatigue à partir d’essais d’auto-échauffement
Mass Spectrometry for In-Depth Study and Discovery of Marine Bioactive Metabolites
No full textInternational audienceThis chapter examines the use of mass spectrometry as an effective tool for the detailed study and discovery of bioactive metabolites of marine origin. The seas and oceans are full of organisms that generate a significant variety of natural compounds with pharmacological, cosmetic, and nutraceutical effects. Thanks to its high sensitivity and analytical resolution, mass spectrometry is able to identify, characterize, and quantify these molecules, even when they are found in complex matrices. This chapter discusses the main categories of marine metabolites, methods frequently used in mass spectrometry, and approaches for discovering new bioactive substances. It also highlights contemporary issues and future directions concerning the sustainable use of these natural resources in research and industry
Design and development of a Low-cost Sensor Network for indoor air quality and thermal insulation assessment
No full textInternational audienceThis study presents the development of a cost-effective sensor network (LCSN) for indoor environmental monitoring, with a focus on air quality and thermal insulation assessment. The system utilizes a Raspberry Pi equipped with sensors to measure volatile organic compounds (VOCs), particulate matter, temperature, humidity, and heat flux. The prototype integrates hardware components, including VOC and particulate matter sensors, along with heat flux sensors connected to an analog-to-digital converter. The software implementation employes C++ programming, cloud-based data storage using InfluxDB on Amazon Web Services, and real-time data visualization through Grafana. The primary objective is to evaluate the performance of biobased insulation materials and their impact on indoor air quality under real-world conditions. Key features of the system include low-cost components, open-source design, real-time data acquisition, and cloud-based storage, enabling long-term environmental monitoring in buildings. The study identifies challenges related to the accuracy of heat flux measurements in biobased materials but also outlines potential improvements and highlights the system's suitability for detailed evaluation of indoor environmental conditions
Convergence analysis of crack features extraction using conjugate work integral
No full textBackground: The extraction of Williams higher-order terms (n>1) is crucial, since these terms control the crack stability and propagation, among other phenomena. Nevertheless, current numerical methods strongly depend on the projection domain and/or are unstable, and thus not suited to analysing experimental data.Purpose: This paper aims to investigate the method based on the Bueckner-Chen conjugate work integral, which provides a bilinear form in which Williams series terms are orthogonal, to retrieve all Williams higher-order coefficients. We reformulate the original path-independent integral into an Equivalent Domain Integral (EDI), more robust when dealing with experimental data or finite element discretisation, and compare the convergence of both approaches.Methods: Williams fields are imposed on a meshed body with decreasing element sizes. The different error sources, i.e. numerical integration, stress interpolation and Finite Element (FE) discretisation, are decoupled by applying relevant boundary conditions to the linear elastic problem.Results: Predictably, the error due to FE discretisation is higher than the other types of errors. Bueckner-Chen integral converges markedly faster than the J-integral on the same domain and provides a better approximation of the Stress Intensity Factor (SIF). As expected, the EDI formulation is more accurate and efficient to retrieve Williams higher-order terms.Conclusion: This work presents a convergence analysis on a robust and efficient method based on the Bueckner-Chen integral to retrieve Williams higher-order coefficients
Rendre attractives les formations réglementaires
No full textInternational audienceImposées dans de nombreuses entreprises, les formations réglementaires jouent un rôle clé dans le respect de la conformité et permettent de maintenir les compétences des collaborateurs à jour. Cependant, celles-ci ne sont pas toujours plébiscitées par les salariés, qui les considèrent souvent comme une contrainte obligatoire. Cette recherche menée au sein d’un réseau bancaire français, s’intéresse aux différents leviers susceptibles de motiver et d’engager les salariés pour les formations réglementaires. Ces travaux ont permis de mettre en lumière la nécessité d’une communication personnalisée, l’importance du rôle des managers, mais également un nécessaire renforcement de l’attractivité des contenus proposés