Publications

Biomass pyrolysis is a thermochemical process used for renewable products and energies. However, there are still issues that need to be addressed for process modeling and optimization. This study focuses on the relationship between heating rate, shrinkage, and products from flax fibers using thermogravimetric analysis (TGA), microscopic observation, and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). TGA confirms sequential evaporation of water then decomposition of hemicellulose, cellulose, and lignin. Observations from the micro-reactor show that flax fibers undergo shrinkage within the temperature range of 335 to 370 °C, depending on the heating rate. Pyrolysis products were analyzed using Py-GC/MS at four different final temperatures from 350 to 500 °C, revealing the presence of anhydrosugars, furans, ketones, phenols, esters, alcohols, aldehydes, and acids. The results indicate a correlation between temperature and the increase in furans and ketones. The analysis suggests that furans and ketones are associated with shrinkage.

This work presents an innovative non destructive control process in order to guide for the end of life (re-use or recycling) of permanents magnets (PM) rotors of electric motors. The process is based on the measurement of the external field produced by the PM rotors. A Finite Element model of the rotor and its environment has been used to simulate a process of classification of the geometry of PM rotors, crucial information for the disassembling of the PM. Firstly, using a finite element model, we were able to investigate the field distribution outside the rotor of the magnetic flux density for different PM rotors designs. From this information, we can set up a classification methodology, based on the Central Voronoi Tessellation (CVT) method, which can help to identify how the PM are inserted within the rotor. This information can be very useful to choose PM disassembling process that should be applied.

L'augmentation des produits en fin de vie et des déchets de produits de consommation pose des défis majeurs en termes de gestion des ressources, notamment des matériaux critiques et des composants stratégiques. Dans le contexte de la transition électrique, ces enjeux sont exacerbés, mettant en lumière la nécessité d'une gestion efficace des ressources et d'une économie circulaire. Cette proposition avance une vision du développement de solutions adaptées de l’industrie 4.0, dans le but de promouvoir une Industrie 4.0 Circulaire. L’objectif est de présenter les technologies et leurs enjeux dans le cadre d’une Économie Circulaire 4.0, afin de favoriser la réparation ou la réutilisation de parties (modules ou composants) encore fonctionnelles à l’étape de fin de vie des produits (souvent en fin d’usage), dans le but de préserver les ressources abiotiques, de réduire les impacts environnementaux et, de favoriser le basculement des activités économiques vers des filières régénératives locales.

We present an adaptive physics-informed deep homogenization neural network (DHN) approach to formulate a full-field micromechanics model for elastic and thermoelastic periodic arrays with different microstructures. The unit cell solution is approximated by fully connected multilayers via minimizing a loss function formulated in terms of the sum of residuals from the stress equilibrium and heat conduction partial differential equations (PDEs), together with interfacial traction-free or adiabatic boundary conditions. In comparison, periodicity boundary conditions are directly satisfied by introducing a network layer with sinusoidal functions. Fully trainable weights are applied on all collocation points, which are simultaneously trained alongside the network weights. Hence, the network automatically assigns higher weights to the collocation points in the vicinity of the interface (particularly challenging regions of the unit cell solution) in the loss function. This compels the neural networks to enhance their performance at these specific points. The accuracy of adaptive DHN is verified against the finite element and the elasticity solution respectively for elliptical and circular cylindrical pores/fibers. The advantage of the adaptive DHN over the original DHN technique is justified by considering locally irregular porous architecture where pore–pore interaction makes training the network particularly slow and hard to optimize.

Short Fiber Reinfored Thermoplastics (SFRTs) like PA66GF composites are highly heterogeneous materials with a rather complex microstructure such that the characterization and the prediction of their mechanical behavior remain quite challenging. So far, most of the research efforts have employed phenomenologigal and mean-field multi-scale models to deal with SFRTs. The present contribution rather focuses on a full-field multi-scale approach that incorporates advanced techniques in terms of microstructural representation and material modeling, allowing a deep insight of the dominating deformation mechanisms occurring in PA66GF composites. The proposed approach is based on an automatic periodic mesh generation algorithm for matrix-inclusion Representative Volume Elements (RVE) with randomly positioned fibers that follow a given Orientation Distribution Function (ODF). At the microscopic scale, while the fibers are assumed to be elastic, the behavior of the thermoplastic matrix is described by a phenomenological multi-mechanism constitutive model accounting for viscoelasticity, viscoplasticity and ductile damage. It results an advanced multi-scale model that enables to visualize the local deformation and degradation mechanisms occurring at the microscopic scale while simultaneously analyzing their influence on the macroscopic response of the composite upon monotonic, persistent and cyclic loading. The potential of the proposed approach is further evaluated by comparing the predicted responses against experimental data.

Circular Economy initiatives have introduced a solution demand for the treatment of End of Life (EoL) products, switching from shredding and material recycling to the recovery of modules/components that remain functional. The process needs product recovery and diagnostic, disassembly, and requalification. These tasks are today carried out by human labour due to their complexity and uncertainties concerning the working conditions of the received product. But it implies time and costs that hardly balance the economical balance in developed countries context. Human – Cobot collaborative disassembly are promising solution to overcome the uncertainties without compromising the flexibility and the rate of disassembly operation. The Smart Disassembly Cell for Circularity (SDC2) project aims to provide a semi–automated disassembly solution for EoL electronic products. This paper portrays the requirements for the implementation of a Human-Cobot collaborative cell for electronic products. The goal of this project is to implement a shared workplace where the robot and the worker can work in parallel or share a common task with safe interaction. A literature study on available solutions was made. From there, the study and implementation of the decision system, database and service platform started. This document describes the function of the database and decision system as well as the service platform to establish global communication. MySQL and Python with the FLASK package were used to frame the database and service platform, Genetic algorithm was implemented for providing the robotic disassembly planning solution. A case study using a personal computer and a Power inverter was conducted to verify the development in each stage. The systems are under development with an aim to provide a global disassembly cell which will be able to disassemble various products rather than focusing on a specific product.

With the rising cost of energy and the advancement of corporate social responsibility, there is a growing interest in addressing the challenge of recovering and storing high-temperature waste heat. Sensible heat storage in packed beds stands out as a cost-effective and seemingly straightforward solution for high-temperature Thermal Energy Storage (TES). Engineering models developed to design low-temperature TES systems were tentatively used to design this new generation of high-temperature systems. Delving into the physics of coupled heat and mass transfer reveals a lack of validation of this approach. This study seeks to establish a comprehensive bottom-up methodology - from the particle scale up to the system level - to provide informed and validated engineering models for the design of high-temperature TES systems. To achieve this goal, we developed a multi-scale numerical model to explore the physics of heat and momentum transfer in packed-bed TES systems. At the microscopic scale (pore/particle), we consider the flow of a compressible high-temperature gas between the particles, coupled to transient heat conduction within the particles, with particular attention given to incorporating accurate temperature-dependent viscosity for the gas phase and thermal conductivity and density for both solid and gas phases. At the macroscopic scale (engineering), we propose a high-temperature extension of state-of-the-art two-equation TES models. The governing equations considered are the volume-average conservation laws for gas-mass, gas-momentum and energy of both phases. The multi-scale strategy is applied to a randomly packed bed of spherical particles generated with the discrete element method (DEM) software LIGGGHTS. Numerical models for both scales were implemented in the Porous material Analysis Toolbox based on OpenFoam (PATO), which is made available in Open Source by NASA. Microscopic scale simulations were used to infer the effective parameters needed to inform the macroscopic model, namely, permeability, Forchheimer coefficient, effective thermal conductivities, and the heat transfer coefficient. The informed macroscopic model reproduces with excellent accuracy the average temperature fields of the physics-based microscopic model. Pore-scale analysis shows highly three-dimensional flow characterized by reverse flow and strong cross-flow in the packed bed system. Moreover, it indicates the coupling between temperature and velocity fields, where a nonuniform velocity field results in uneven temperature distributions across the fluid and the solid spheres within the packed bed, subsequently affecting the macroscopic heat transfer coefficient. The overall strategy is validated by comparison to available experimental data. This bottom-up methodology contributes to the understanding and opens new perspectives for a more precise design and monitoring of high-temperature TES systems.

In order to better constrain the alteration history of the Ryugu parent body, we performed a multi-analytical study combining scanning electron microscopy, transmission electron microscopy and infrared spectroscopy on sections extracted from the three fragments A0064-FO019, A0064-FO021 and C0002-FO019 returned from Ryugu by the Hayabusa2 space mission. The three sections show large differences in terms of structure, mineralogy and infrared signature. Section A0064-FO019 resembles the major Ryugu lithology with the presence of both fine-grained phyllosilicates (fg-phyllos) with embedded nanosulfides and coarse-grained phyllosilicates (cg-phyllos), whereas section C0002-FO019 belongs to the group of the less altered lithologies with the presence of anhydrous minerals embedded in a partially amorphous matrix. Section A0064-FO021 also belongs to this group but shows two different lithologies, a compact amorphous one and a more porous and very fractured one showing the presence of Na-rich phosphate, calcite and olivine. The two less altered lithologies (sections A0064-FO021 and C0002-FO019) show the presence of numerous mineralogical features similar to those observed in cometary interplanetary dust particles, ultra-carbonaceous Antarctic micrometeorites or in the CM Paris meteorite, i.e. amorphous and partially crystallized matrix with GEMS-like ghosts objects, whisker olivine, phosphide, or FeNi metal. This supports an outer solar system origin common with that of cometary material for the Ryugu parent body. Combined with the results of Nakamura et al. (2022b) reporting the presence of a lithology showing the presence of GEMS-like objects, we propose that section C0002-FO019 represents the onset of aqueous alteration of such primitive materials. The cg-phyllos and fg-phyllos of section A0064-FO019, i.e. of the major Ryugu lithology, representing the advanced stage of alteration, exhibit distinctive IR signatures with a higher abundance of oxygen-rich functional groups in the organic matter (OM) from the cg-phyllos. We thus suggest the following chronology of formation and evolution for Ryugu: (1) accretion of highly porous aggregate of GEMS-like units with fine-grained high-temperature anhydrous silicates, (2) onset of alteration with the dissolution of primary nanosulfides and development of amorphous/partially crystallized material in the pores, (3) crystallization of fg-phyllos with a second generation of sulfides, (4) later formation of cg-phyllos devoid of nanosulfides and their associated oxygen-rich OM in a more water-rich environment.

Progress in hydrogen fuel powered systems has been propelled by the implementation of secure, reliable, and cost-effective hydrogen storage and transportation technologies. The fourth category, distinguished by a polymer liner serving as a hydrogen diffusion barrier, fully encapsulated within a fiber-reinforced composite to bestow structural integrity, has garnered substantial attention from the automotive industry due to its lightweight nature and rational manufacturing process. The method of rotomolding has sparked interest among manufacturers due to its capability to directly bond the metallic component to the polymer substrate, specifically the liner, thus negating the need for welding and its attendant imperfections. In fact, a pivotal facet of fourth-generation hydrogen storage systems revolves around the interface connection between the polymer liner and the metallic boss, posing as a structural Achilles' heel. For the study's purposes, a scaled-down demonstrator was fabricated using rotomolding in which a nozzle-liner interface mimics the boss-liner interface of the actual system. This demonstrator was designed to facilitate the mechanical characterization of the interface under quasi-static and fatigue loading. The thermal cycling phases of rotational molding and the surface treatments undertaken have been optimized in order to enhance direct adhesion within the metal-polymer interface. This study commences by assessing the efficacy of two treatments (sandblasting and flaming) applied to the aluminum nozzle surface. Subsequently, we explore the adhesion microstructural and mechanical characteristics of the treated nozzle onto a medium-density polyethylene polymer (liner). Lastly, we delve into an exploration of the damage and fatigue behaviors endemic to the metal-polymer interface region. The obtained Wöhler curves disclose a linear trend for the metal-polymer interface. Moreover, the metal-polymer interface evinces heightened resilience against damage and fracture for sandblasted interfaces. This inquiry underscores the potency of innovative polymer-metal interfaces treatment in amplifying the reliability and robustness of hydrogen storage technology.

Nowadays, Deep Learning (DL) techniques are increasingly employed in industrial applications. This paper investigate the development of data-driven models for two use cases: Additive Manufacturing-driven Topology Optimization and Structural Health Monitoring (SHM). We first propose an original data-driven generative method that integrates the mechanical and geometrical constraints concurrently at the same conceptual level and generates a 2D design accordingly. In this way, it adapts the geometry of the design to the manufacturing criteria, allowing the designer better interpretation and avoiding being stuck in a timeconsuming loop of drawing the CAD and testing its performance. On the other hand, SHM technique is dedicated to the continuous and non-invasive monitoring of structures integrity, ensuring safety and optimal performances through on-site real-time measurements. We propose in this work new ways of structuring data that increase the accuracy of data driven SHM algorithms and that are based on the physical knowledge related with the structure to be inspected. We focus our study on the damage classification step within the aeronautic context, where the primary objective is to distinguish between different damage types in composite.