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Simulation de la fissuration par champ de phase : Analyse technique de l’initialisation et la propagation de fissures
No full textInternational audienceThis study examines numerical biases in phase-field fracture simulations. More specifically, it investigates the influence of meshing on crack path trajectories in phase-field fracture simulations. A reference problem based on a Pure Shear test with an eccentric initial crack is used to evaluate the impact of spatial discretization on predicted crack trajectories. Phase-field simulations are compared to a reference solution from Linear Elastic Fracture Mechanics, towards which they theoretically converge. Results show that structured meshes introduce artificial anisotropy that forces the crack to follow a mesh-dependent trajectory. Conversely, unstructured meshes enable a more accurate prediction of the expected exponential trajectory. Indeed, the disordered nature of the mesh significantly reduces the artificial anisotropy effects induced by discretization. These observations highlight the importance of mesh selection in crack propagation simulations. Practical recommendations are finally proposed to minimize numerical biases in phase-field simulations.Cette étude propose d’étudier les bias numériques dans les simulations de la fissuration par champ de phase. Plus spécifiquement, elle examine l’influence du maillage sur la trajectoire de fissure. Un problème de référence basé sur un essai de Pure Shear avec fissure initiale excentrée est utilisé pour évaluer l’impact de la discrétisation spatiale sur la trajectoire de fissure prédite. Les simulations par champ de phase sont comparées à une solution de référence issue de la mécanique linéaire élastique de la rupture, vers laquelle elles convergent théoriquement. Les résultats montrent que les maillages structurés introduisent une anisotropie artificielle qui contraint la fissure à suivre une trajectoire dépen- dante du maillage. En revanche, les maillages non structurés permettent une prédiction plus fidèle de la trajectoire exponentielle attendue. En effet, le caractère désordonné du maillage réduit les effets d’ani- sotropie artificiels induits par la discrétisation. Ces observations mettent en évidence l’importance du choix du maillage dans les simulations de propagation de fissures. Des recommandations pratiques sont finalement proposées pour minimiser les biais numériques dans les simulations par champ de phase
Uncovering the mechanisms of heartwood formation and wood resistance to fungal degradation in the tropical Lauraceae tree Sextonia rubra (Mez.) van der Werff
No full textInternational audienceHeartwood formation is a complex process that contributes to ensuring the integrity of trunks and the longevity of trees. We examined this mechanism in the tropical angiosperm Sextonia rubra in relation to the spatial distribution of specialised metabolites and their functional role at the scale of a mature individual. Heartwood formation was analysed starting from the examination of one of its properties, namely the decay resistance, of the different S. rubra wood tissues (sapwood, heartwood, and pith) using soil bed tests. Annotation and identification of the metabolites present in ethyl acetate extracts were carried out by reversephase liquid chromatography coupled to a tandem mass spectrometer (RPLC-ESI-MS/MS) and molecular networks. Following the application of supervised statistical analyses and the use of Glutathione S-transferases enzymatic assays, the specialised metabolites of interest were quantified radially and longitudinally in the different tissues using RPLC-ESI-HRMS system. Heartwood and pith were shown to resist degradation after a ten-months exposure to forest soil, with no effect of the heartwood cambial age. Molecular diversity was specific to each tissue type, with flavonoids and butanolides detected in bark and sapwood, while alkaloids and butyrolactones were identified in heartwood and pith. Supervised analyses and enzyme assays suggested that alkaloids and butyrolactones play a role in the resistance of internal tissues to degradation. Butyrolactone concentrations peaked in the middle heartwood but remained homogeneous longitudinally, while alkaloid concentrations were uniform longitudinally and radially in the heartwood. In conclusion, the resistance of heartwood and pith to fungal degradation was correlated with the accumulation of lactones and alkaloids. While butanolide 3 precursors of butyrolactones have been detected in the sapwood, alkaloids appear to be directly biosynthesised in the heartwood. This suggests that the biosynthesis and accumulation of specialised metabolites during heartwood formation is specific to each molecular family
Contrasting Trends in Phytoplankton Diversity, Size Structure, and Carbon Burial Efficiency in the Mediterranean Sea Under Shifting Environmental Conditions
No full textInternational audienceShifts in the phytoplankton assemblage induced by environmental changes have significant implications for carbon cycling and marine food webs, but remain poorly constrained across spatiotemporal scales. Here, we investigate the effects of rising sea surface temperatures and increased stratification on the phytoplankton composition and size in the northwestern Mediterranean Sea (2010–2019) using two sediment trap series: one in the oligotrophic Ligurian Sea and the other in the deep convection zone of the Gulf of Lion. We apply deep learning image analysis to quantify phytoplankton particle fluxes, size distributions, and relative assemblages, focusing on coccolithophores, diatoms, and silicoflagellates. Our results show a general decline of phytoplankton fluxes to the seafloor, mirroring the decrease in vertical mixing in the water column. Both sites show a shift toward phytoplankton species adapted to stratified and nutrient‐depleted conditions, although with contrasting patterns. In the Ligurian Sea, deep‐dwelling coccolithophore species become dominant, while in the Gulf of Lion, summer‐associated siliceous species, including large diatoms and silicoflagellates, show an increase. These contrasted trends, which likely result from differences in nutrient inputs and pH changes in the surface between the two sites, have implications for the efficiency of carbon export pathways at depth. Specifically, the increasing dominance of smaller phytoplankton in the Ligurian Sea leads to a reduction in carbon burial efficiency, while in the Gulf of Lion, the enhanced contribution of larger diatoms may sustain higher export and burial rates in the future
Quantification of CO<sub>2</sub> hotspot emissions from OCO-3 SAM CO<sub>2</sub> satellite images using deep learning methods
No full textInternational audienceThis paper presents the development and application of a deep-learning-based method for inverting CO 2 atmospheric plumes from power plants using satellite imagery of the CO 2 total column mixing ratios (XCO 2 ). We present an end-to-end convolutional neural network (CNN) approach, processing the satellite XCO 2 images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume owing to the limited extent of the satellite swath.The CNN is trained and validated exclusively on CO 2 simulations from eight power plants in Germany in 2015. The evaluation on this synthetic dataset shows an excellent CNN performance with relative errors close to 20 %, which is only significantly affected by substantial cloud cover. The method is then applied to 39 images of the XCO 2 plumes from nine power plants, acquired by the Orbiting Carbon Observatory-3 Snapshot Area Maps (OCO3 SAMs), and the predictions are compared to average annual reported emissions. The results are very promising, showing a relative difference in the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. Furthermore, analysis of the area of the images in which the CNN-based inversion extracts the information for the quantification of the emissions, based on integratedgradient techniques, demonstrates that the CNN effectively identifies the location of the plumes in the OCO-3 SAM images. This study demonstrates the feasibility of applying neural networks that have been trained on synthetic datasets for the inversion of atmospheric plumes in real satellite imagery from XCO 2 and provides the tools for future applications
Assessing the benefits of an agent-based approach for occupant behavior modeling in power demand simulation of dwelling stock
No full textInternational audienceThe European Union aims for carbon neutrality by 2050, necessitating the gradual phase-out of carbonbased energy sources. Key elements for decarbonizing the residential sector and addressing the energy crisis include electrifying end-uses, reducing the demand through energy conservation measures, adopting renewable energy sources, and integrating flexibility measures to address the intermittent nature of these sources. Quantifying current and future power demand is crucial to balance energy supply and demand and to support the development of more effective policies and strategies for achieving carbon neutrality in the residential sector. This study aims to assess the impact of stochasticity and heterogeneity in input data on residential electric load curve modeling using a techno-explicit model, generated through an Agent Based Model (ABM). Existing models often assume deterministic occupants' behaviors, which limits their ability to capture the variability, diversity, and complexity of human behaviors and interactions with their environment. This can lead to an underestimation of its impact on building energy consumption during simulations and limits the ability to analyze the impacts of tariff changes, flexibility measures and energy conservation behaviors. To achieve our goal, two existing models are integrated through a coupling approach. The first model is an activity based multi-agent model, parametrized to represent the French population, that simulates the occupants' behaviors and the electricity consumption. The second model is a bottom-up technoexplicit model that evaluates the electrical load curve of the thermal end-uses of the French dwelling stock, with occupants represented using data-based deterministic inputs. A sequential coupling is implemented using these two models. The first model generates stochastic/agent-based data, which are then post-processed to prepare the needed granularities for running the second model. Three cases were analyzed: raw stochastic and heterogeneous data, daily averaged data retaining heterogeneity, and data averaged both daily and across household types. A comparison between these different cases and measured data was performed to evaluate the impact of stochasticity and heterogeneity based on the coincidence factor and the diversification speed of the CV(RMSE) and NMBE.</div
A thermodynamically consistent wear modeling approach based on damage accumulation
No full textInternational audienceDue to the diversity of mechanisms involved, wear is very complex to model. Wear models are mostly empirical, and they sometimes fail to accurately predict wear evolution. In this paper, a damage-based wear modeling approach is developed in the framework of continuum thermodynamics. The model is physically consistent and aims at accounting for the progressive accumulation of nearsurface degradation leading to material detachment. A thermodynamic driving force associated with wear is derived under the form of an energy release rate. Wear evolution is then driven by the accumulation of near-surface damage, and wear occurs when the surface damage value reaches a threshold. The damage evolution problem is treated using the thick level set approach, providing a non-local formulation for damage evolution. Numerical simulations are conducted on a fretting test case using the finite element method, and the results compared to those obtained with a classical friction energy wear law
Durability of internally pressurised plastic-pipes by plane-strain grooved tensile (PSGT) specimens
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L'hydrodynamique des vagues du large jusqu'à la côte : modélisation et impacts
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Incentivizing Electric Vehicle Charging Flexibility for Demand Response via Price Menu Approach
No full textInternational audienceThis study considers a Charging Station Operator (CSO) that participates in Demand Response (DR) services and, in return, receives remuneration from the Electric Vehicle Aggregator (EVA). To participate in DR, the CSO requires flexibility from Electric Vehicle (EV) users, which typically concerns charging deadlines or energy demands. In this model, EV users are flexible with their charging deadlines but have fixed energy demands. To incentivize this flexibility, the CSO offers EV users a menu of charging options, where each option specifies a charging deadline and its corresponding charging price. The price menu design problem is formulated as a Mixed Integer Quadratic Programming problem, where the CSO aims to maximize its profit and the user selects the option from the menu that maximizes its utility. The numerical results provide valuable insights into the optimal price menu design and the ideal remuneration from the EVA to the CSO
When Lions meets Krugman: A mean-field game theory of spatial dynamics
No full textWe propose a mean-field game (MFG) set-up to study the dynamics of spatial agglomeration in a continuous space-time framework where trade across locations may follow a broad class of static gravity models. Forward-looking intertemporal utility-maximizing agents work and migrate in a twodimensional geography and face idiosyncratic shocks. Equilibrium wages and prices depend on their common distribution and adjust statically according to the underlying trade model. We first prove existence and uniqueness of the static trade equilibrium. We then prove existence of dynamic equilibria. In the case of Krugman (1996)'s racetrack economy, we obtain closed-form solutions for small sinusoidal perturbations around the steady state, and we identify the sets of parameters that lead to agglomeration or dispersion. We exploit the MFG structure of the model to explicitly quantify how uncertainty and forward-looking expectations contribute to agglomeration and dispersion. In particular, we show that, regardless of the static trade model, forward-looking expectations always promote agglomeration, but cannot reverse the dominant pattern that would arise under myopic behavior