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Strain measurements through fiber Bragg grating sensors embedded in composite structure: from curing to blast loading
No full textInternational audienceThis paper deals with in-situ measurement in a composite part with metric dimensions during its life cycle, from curing to blast loading. To achieve this objective, a single fiber with three Bragg gratings is used. Two different curing processes are involved, respectively in autoclave and in a curing oven, and strain measurements are performed during both polymerization cycles, especially during the cooling phase where residual strain is building up. Because blast loading is highly dynamic (loading duration typically tens to hundreds of μs), an adapted measurement system is required. The system used for the blast loading application is based on pulsed illumination of the Bragg gratings and wavelength to time conversion is used to measure the strain of the composite at a frequency of 100 MHz. The residual and dynamic strain measurements during blast loading are compared to finite element models
Monitoring the water saturation and damage in reinforced concrete subjected to alkali-silica reaction gradient by non-destructive testing
No full textInternational audienceAlkali silica reaction (ASR) is a harmful swelling pathology in concrete influenced by various factors such as moisture levels and stress conditions. In order to evaluate ASR-damaged structures, it is essential to characterize in situ water saturation gradients and to quantify the influence of reinforcement on ASR-induced damage. This research aims to assess the capability of different non-destructive testing (NDT) methods to evaluate moisture gradients in the context of ASR as well as the damage arising from expansion gradients in presence of reinforcement. Plain and reinforced concrete specimens were cast using both reactive and control composition. After curing, the specimens were fully, half-immersed and quarter-immersed in water at 38 °C to monitor the effects of ASR under varying water saturation conditions. The physicochemical conditions were characterized using permittivity tests. Expansion and damage were monitored through two-dimensional length change measurements, linear vibration analysis, acoustic emission (AE) and crack observations. This paper proposes an original method involving AE monitoring during mechanical loading following expansion. The linear vibration method was found to correlate the extent of damage with average expansion, regardless of the moisture gradient or presence of reinforcement. The acoustic emission during mechanical loading, along with crack observation techniques, effectively localized damage resulting from moisture gradients and reinforcement effects. The discussion highlights the complementary nature of these two techniques for monitoring the damage in both plain and reinforced concrete subjected to ASR
Tous les chemins mènent à DROP : une évaluation de la sécurité d'un mécanisme de routage du Bluetooth Mesh
No full textNational audienceLe Bluetooth Mesh est un protocole sans fil récent basé sur le Bluetooth Low Energy, offrant une communication de type many-to-many. Disposer d'une sécurité robuste et assurer la confidentialité des communications sont des enjeux de plus en plus centraux dans le contexte de l'Internet des Objets. Ces contraintes ont ainsi guidé la conception du Bluetooth Mesh, faisant de celui-ci un protocole particulièrement intéressant à étudier du point de vue de la recherche en sécurité. En particulier, le Bluetooth Mesh dispose à l'heure actuelle de deux mécanismes de relais destinés à assurer la transmission des messages d'un équipement à l'autre. Le premier de ces mécanismes, appelé Managed Flooding et reposant sur une retransmission systématique des communications par les nœuds relais, garantit la transmission des messages mais implique un coût élevé en termes d'utilisation du réseau. La fonctionnalité de Directed Forwarding, récemment introduite dans la version 1.1 du protocole, constitue quant à elle une nouvelle méthode de relais, reposant sur des chemins dédiés au sein du réseau et permettant la communication entre des nœuds distants, sans surcharger le réseau de messages inutiles. Dans cet article, nous présentons la première évaluation de la sécurité de la fonctionnalité de Directed Forwarding du Bluetooth Mesh. Nous montrons qu'un attaquant présent au sein du réseau peut perturber de manière significative le bon fonctionnement de ce mécanisme de routage. Cette analyse nous a notamment permis d'identifier plusieurs vulnérabilités du protocole, exploitant les caractéristiques de ce mécanisme de routage. Enfin, nous démontrons théoriquement et expérimentalement l'existence de différentes attaques par déni de service (DoS), impactant significativement la disponibilité et les propriétés de sécurité assurées par un réseau Bluetooth Mesh
Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring
No full textModel Predictive Control (MPC) is widely used for torque-controlled robots, but classical formulations often neglect real-time force feedback and struggle with contact-rich industrial tasks under collision constraints. Deburring in particular requires precise tool insertion, stable force regulation, and collision-free circular motions in challenging configurations, which exceeds the capability of standard MPC pipelines. We propose a framework that integrates force-feedback MPC with diffusion-based motion priors to address these challenges. The diffusion model serves as a memory of motion strategies, providing robust initialization and adaptation across multiple task instances, while MPC ensures safe execution with explicit force tracking, torque feasibility, and collision avoidance. We validate our approach on a torque-controlled manipulator performing industrial deburring tasks. Experiments demonstrate reliable tool insertion, accurate normal force tracking, and circular deburring motions even in hard-to-reach configurations and under obstacle constraints. To our knowledge, this is the first integration of diffusion motion priors with force-feedback MPC for collision-aware, contact-rich industrial tasks
Metabolic Responses of Oxygenic Photogranules to Carbon and Light Fluctuations: Insights from Longitudinal Metabolomics
No full textInternational audienceOxygenic photogranules (OPGs) are biological aggregates primarily composed of cyanobacteria, eukaryotic algae, and heterotrophic bacteria interacting syntrophically [1]. Recognized for their potential in wastewater treatment, OPGs cultivated in sequencing batch reactors (SBR) are subjected to frequent fluctuations in light and carbon availability, potentially impacting their metabolic activity and syntrophic relationships. However, the metabolic responses of OPGs to such environmental changes and their consequences on community function and interactions, remain poorly understood. A major experimental challenge lies in disentangling the metabolic effects induced by environmental fluctuations from those caused by the spatial heterogeneity inherent to the three-dimensional structure of the biomass [2]. To address this challenge, we developed a dedicated experimental system, a co-extraction protocol for metabolome and metatranscriptome analyses, and the integration of longitudinal multi-omics approaches. OPGs were grown in a SBR with synthetic wastewater, and a controlled number of small, homogeneous granules (0.6–1 mm) was selected to reduce internal gradients. Under these conditions, we assumed that all OPGs experienced comparable environmental exposure. A sacrificial sampling strategy was employed, with three carbon availability conditions tested: early addition (at t0), late addition (after 90 minutes), and no carbon addition. Photogranules were exposed to 70 µmol·m⁻²·s⁻¹ of photosynthetically active radiation from 0 to 180 minutes, followed by darkness from 180 to 240 minutes. Eight replicates were collected per condition and time point. Metabolites and RNA were co-extracted according to the protocol described in [3]. NMR-based metabolomic analyses enabled metabolite identification, semi-quantification, and the identification of KEGG metabolic pathways statistically differentiated according to environmental conditions [4,5]. The metatransciptome is currently analyzed.Under carbon-limited conditions, eight KEGG pathways were altered, notably those related to C5-branched dibasic acids, phosphate and phosphinate, taurine and hypotaurine and amino sugar and nucleotide sugar metabolisms. Metabolites such as N-acetylneuraminate, a known precursor of extracellular polymeric substances (EPS) [6], and the amino-acids L-alanine and hypotaurine decreased in the absence of acetate, suggesting mobilization of internal carbon reserves, as previously reported [7]. In contrast, acetate supply sustained both heterotrophic and phototrophic activities. Our results further indicate that the history of carbon availability influenced the metabolic response of OPGs to light changes. When the light was turned off, distinct metabolomic signatures emerged between the early and late carbon addition series, particularly within the “carbon fixation by Calvin cycle” pathway. Differential dynamics of 3-phosphoglycerate and L-alanine suggest that the prior carbon supply modulated the activity of phototrophs. Moreover, other metabolic pathways were affected. Distinct responses of “alanine, aspartate and glutamate metabolism” and especially L-argininosuccinate, a metabolite linked to light response and implicated in biofilm development and cohesion [8], was observed between experimental series. Fatty acid metabolism also varied, with specific changes in polyunsaturated fatty acids, key components of cell membranes and sensitive markers of oxidative stress [9].Overall, these findings highlight how light and carbon availability, shape photogranule metabolism [10]. The integration of ongoing metatranscriptomic and metagenomic analyses will further elucidate the syntrophic relationships between cyanobacteria (notably Leptolyngbya in our system) and heterotrophic bacteria and their contributions to these dynamic processes. Future work will aim to determine whether environmental changes exert lasting effects on OPG functions and activity, particularly in relation to oxidative stress responses, the regulation of EPS and amino acid biosynthesis, and their impact on biofilm formation, structural cohesion, and cell-to-cell communication via quorum sensing
Etude expérimentale et numérique des dommages induits par le phénomène d'arc électrique
No full textInternational audienceHigh-voltage electrical networks are being integrated into a generation of aircraft including electric propulsion systems. This implies the prevention of faults linked to their use, such as electric arcs. The energy dissipated by an arc is at the origin of various phenomena representing a threat to the structureof the aircraft in the vicinity of the arc, such as: thermal effects or the effects of strong pressure variations. In order to guarantee the integrity of the aircraft structure, it is essential to characterize the damage to materials generated by these phenomena. This work proposes a numerical approach based on a finite element method. To achieve this, the first objective is to precisely define the model input data, and particularly the equivalent loadings for the two phenomena described above.Des réseaux électriques haute tension sont intégrés dans une génération d’avions incluant des systèmes propulsifs électriques. Ceci implique la prévention de défauts liés à leur utilisation tels que les arcs électriques. L’énergie dissipée par un arc est à l’origine de différents phénomènes représentant unemenace pour la structure de l’avion à proximité de l’arc tels que des effets thermiques ou des effets de forte variation de pression. Afin de garantir l’intégrité de la structure de l’avion, il est essentiel de caractériser l’endommagement généré par ces phénomènes sur les matériaux. Ce travail propose uneapproche numérique basée sur une méthode par éléments finis. Pour y parvenir, le premier objectif est de définir précisément les données d’entrée du modèle, et en particulier les chargements équivalents des deux phénomènes énoncés précédemment
Accurate VLE predictions via COSMO-RS-Guided deep learning models: solubility and selectivity in physical solvent systems for carbon capture
No full textInternational audienceCarbon capture through physical solvents reduces energy consumption and lowers environmental impact compared to conventional chemical absorption methods. Typical properties for solvent screening are solubility and selectivity. However, they require accurate prediction of vapor-liquid equilibrium (VLE) that remains a critical challenge due to the lack of enough available experimental data. This could be supplemented by in silico data prediction provided that current predictions models are improved as this paper intends. When modeling physical solvents, a challenge arises due to the dominant role of non-bonding interactions and molecular geometry. For this purpose, a machine learning pipeline is developed using VLE results obtained from the quantum chemical-based thermodynamic model COnductor like Screening MOdel for Real Solvents (COSMO-RS) and experimental data. A Direct-Message Passing Neural Network (D-MPNN) architecture is employed, leveraging molecular representations, additional features and transfer learning to refine predictions. Two models, solubility and selectivity, are pre-trained over 30000 COSMO-RS simulated data points and fine-tuned with experimental VLE datasets for CO₂ and common gas impurities (H₂S, CH₄, N₂, H₂) respectively. The models’ accuracy is significantly improved over COSMO alone by correcting bias in total pressure predictions. Experimental trends are successfully reproduced in the test data, confirming the physical consistency of the models. Sensitivity analysis confirms that molecular features have the highest impact on estimations, while the scaling effect of additional features is essential for accuracy. These results demonstrate the potential of the proposed methodology to systematically screen and optimize an extensive range of physical solvents on the basis of their chemical structure for carbon capture applications, reducing reliance on costly and time-consuming experimental measurements
Hierarchical Modelling of the Impact of Obsolescence on System Availability
No full textInternational audienceTechnological advances, changing needs and market dynamics generate a multitude of obsolescence issues in almost all sectors of activity. This represents serious challenges for companies, particularly in terms of quality, availability and maintainability. These challenges are particularly acute for complex systems with a long lifespan such as trains or planes, which must maintain acceptable levels of performance for many years. The obsolescence of components, documentation, tools and personnel skills are all industrial risks that must be controlled. In this context, this research presents models to represent the impact of obsolescence on the availability of complex systems. The objective is to show the mechanisms of these impacts. These models, built using Petri nets, then make it possible to estimate the possible degradation of availability following the occurrence of obsolescence, an aspect not addressed in this article. The modelling is based on a principle of classifying components into four classes, allowing to model the multilevel architecture of a system. By associating a set of Petri models with each class, and by synchronising the models between them, the impact of obsolescence on availability is clearly described. The article ends with a number of conclusions and in particular with a presentation of the research carried out to predict the availability of systems in the presence of obsolescence of components, documentation, personnel or tools
Optimisation des systèmes de contrôle complexes avec des simulateurs différentiables : une approche hybride de l'apprentissage par renforcement et de la planification de trajectoire
No full textInternational audienceDeep reinforcement learning (RL) often relies on simulators as abstract oracles to model interactions within complex environments. While differentiable simulators have recently emerged for multi-body robotic systems, they remain underutilized, despite their potential to provide richer information. This underutilization, coupled with the high computational cost of exploration-exploitation in high-dimensional state spaces, limits the practical application of RL in the real-world. We propose a method that integrates learning with differentiable simulators to enhance the efficiency of exploration-exploitation. Our approach learns value functions, state trajectories, and control policies from locally optimal runs of a model-based trajectory optimizer. The learned value function acts as a proxy to shorten the preview horizon, while approximated state and control policies guide the trajectory optimization. We benchmark our algorithm on three classical control problems and a torque-controlled 7 degree-of-freedom robot manipulator arm, demonstrating faster convergence and a more efficient symbiotic relationship between learning and simulation for end-to-end training of complex, poly-articulated systems.L'apprentissage par renforcement profond (RL) s'appuie souvent sur des simulateurs comme oracles abstraits pour modéliser les interactions au sein d'environnements complexes. Bien que des simulateurs différentiables aient récemment émergé pour les systèmes robotiques multi-corps, ils restent sous-utilisés, malgré leur potentiel à fournir des informations plus riches. Cette sous-utilisation, conjuguée au coût de calcul élevé de l'exploration-exploitation dans des espaces d'état de grande dimension, limite l'application pratique de l'RL en situation réelle. Nous proposons une méthode intégrant l'apprentissage à des simulateurs différentiables afin d'améliorer l'efficacité de l'exploration-exploitation. Notre approche apprend des fonctions de valeur, des trajectoires d'état et des politiques de contrôle à partir d'exécutions localement optimales d'un optimiseur de trajectoire basé sur un modèle. La fonction de valeur apprise agit comme un proxy pour raccourcir l'horizon de prévisualisation, tandis que les politiques d'état et de contrôle approximatives guident l'optimisation de la trajectoire. Nous comparons notre algorithme à trois problèmes de contrôle classiques et à un bras manipulateur robotique à 7 degrés de liberté contrôlé par couple, démontrant une convergence plus rapide et une relation symbiotique plus efficace entre apprentissage et simulation pour l'apprentissage complet de systèmes complexes et polyarticulés
High-temperature superspin glass and low-temperature glassy exchange bias in passivated FeCo nanoparticles
No full textInternational audienceConventional powders, dense systems of magnetic nanoparticles, often combine intra- and inter-particle magnetically glassy properties, which may complicate their interpretation. To shed light on this matter, we have studied 9 nm FeCo particles synthesized by thermal co-decomposition of metal amides after a passivation layer around 2 nm thick has formed in ambient conditions. The saturation magnetization, 117 emu/g, is consistent with the above metallic core/ferrite shell picture. The high magnetic moment and concentration of the particles yield, via strong interparticle interactions, a remarkable room temperature superspin glass-like phase (with freezing temperature above 350 K) for such small particles, as confirmed by the de Almeida-Thouless analysis. Additionally, we detect a spin glass-like freezing at the atomic scale (within the particles). Its corresponding feature, a small hump under small fields in the temperature dependence of the magnetization, closely agrees with the onset of the exchange bias effect (∼ 60 K) measured, unlike it is customary, with repeated field-coolings. The spin-disordered nature of the core/shell interface is further proved by a strong training effect of the exchange bias field, among others. This magnetic behavior offers an indirect proof of structural interface disorder even in fully passivated metallic particles