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Optimisation multi-critère d'une exploitation d'uranium par In Situ Recovery
No full textWorldwide, In situ recovery (ISR) is the most widely used uranium mining technique. Uranium ISR consists in dissolving the ore minerals using an acidic leaching solution directly within the deposit through a series of injection and extraction wells. The U-enriched solution is then pumped to the surface in order to separate the dissolved uranium from the acid solution. It is by far the most cost effective extraction technique. However, an ISR exploitation impacts the groundwater quality by increasing the concentration of dissolved elements and decreasing the pH. Subsequently, groundwater impacted by acid ISR mining is typically remediated using various rehabilitation strategies. This study aims to forecast uranium production and predict the long-term environmental footprint of such exploitation using a reactive transport modeling approach.To do so, we have used HYTEC, an investigative tool to assess both production and the environmental footprint of an ISR mining site. The model is applied to an uranium deposit of the KATCO mine and the SaUran mine in Kazakhstan. This model can simulate the evolution of the aquifer geochemistry during and after the production phase. The environmental impact model is calibrated based on long-term water quality monitoring data.We have studied how operating parameters impact both the uranium production and the environmental footprint of the ISR exploitation.The environmental footprint can be described in terms of distance and time. The distance is generally controlled by the migration of sulfate ions resulting from the injection of sulfuric acid, which have low reactivity and hence an important mobility. Acidity or pH is the parameter which influences the duration of the impact, as protons has a very important reactivity and can also be stored locally by adsorption on clay mineral surfaces.The geochemical model, previously developed in this study, suggests that cationic sorption on clay surfaces and the precipitation of secondary minerals like gypsum regulate the behavior of elements over extended durations and distances.À l'échelle mondiale, la récupération in situ (ISR) est la technique d'extraction d'uranium la plus utilisée au monde. Cette méthode consiste à injecter une solution acide à travers des puits dans des couches géologiques perméables pour dissoudre les phases minérales contenant l'uranium. La solution enrichie en uranium est ensuite pompée à la surface, où l'uranium dissous est extrait à l'aide de résines échangeuses d'ions. La solution de lixiviation est ensuite recyclée et réinjectée dans le gisement.Bien que l'ISR soit très rentable, elle affecte la qualité des eaux souterraines en augmentant les concentrations en éléments dissous et en diminuant le pH, nécessitant ainsi des stratégies de réhabilitation.Cette thèse a pour objectif de prévoir la production d'uranium et d'anticiper l'empreinte environnementale à long terme d'une telle exploitation, en utilisant une approche de modélisation du transport réactif. Pour ce faire, nous avons utilisé HYTEC, un outil d'investigation permettant d'évaluer à la fois la production et l'empreinte environnementale d'un site ISR. Le modèle a été appliqué aux gisements d'uranium des mines de KATCO et de SaUran au Kazakhstan. Il permet de simuler l'évolution géochimique de l'aquifère, pendant et après la phase de production. L'impact environnemental du modèle a été calibré sur la base de données à long terme provenant de piézomètres de surveillance.Nous avons étudié l'influence de plusieurs paramètres opérationnels sur la production d'uranium et l'empreinte environnementale de l'exploitation ISR.Cette empreinte environnementale peut être analysée en termes de distance et de temps. La distance est principalement contrôlée par la migration des ions sulfate, issus de l'injection d'acide sulfurique, qui sont faiblement réactifs et donc très mobiles. L'acidité (pH) influe sur la durée de l'impact, car les protons peuvent être stockés localement par adsorption sur les surfaces des minéraux argileux.Le modèle géochimique, développé dans cette étude, suggère que l'adsorption cationique sur les surfaces argileuses, ainsi que la précipitation de minéraux secondaires tels que le gypse, régulent la dispersion des éléments sur de longues distances et périodes
The (Market) Value of State Honors
No full textDocuments de travail du Centre d’Économie de la Sorbonne 2025.25 - ISSN : 1955-611X - eISSN : 2968-6687State awards to civilians are a widespread social phenomenon across space and time. This paper provides a quantification of the impact of State awards given to Directors on the stock value of their firms. We link a comprehensive dataset of recipients of the Légion d'honneur-the most prestigious official award in France-over the 1995-2019 period to Board positions in French listed firms. We document positive abnormal returns in the stocks of recipients' firms at the date of the award. This finding does not apply to those previously identified as politically connected through shared education in elite graduate schools; rather, it is driven by recipients for whom the awards newly signal a valuable access to policy-makers, establishing State awards as a new indicator of political connections
Additive-free 3D-printed nanostructured carboxymethyl cellulose aerogels
No full textInternational audience3D printing of polysaccharide solutions is widely recognized as a highly promising method in the biomedical field for achieving complex customized shapes. One of the main challenges is in selecting conditions, in particular, the rheological properties of the system, to retain the printed shape. For the first time, the direct ink writing (DIW) is successfully applied to neat carboxymethyl cellulose (CMC) solutions without any additives or crosslinking, only by adjusting solutions' rheological properties. The influence of CMC molecular weight, degree of substitution and polymer concentration on solutions' viscoelastic properties is investigated. Extrusion velocity at various pressures and pressure calibration curves are determined to optimize printing parameters. Lightweight and nanostructured materials, aerogels, are then made from the printed structures through drying with supercritical CO 2 . 3D printed aerogels with high shape stability are of density (solid part) around 0.1 g/cm 3 and specific surface area up to 140 m 2 /g, density being twice lower and surface area twice higher than those of the "bulk" (or moulded) counterparts. Customized aerogels with high specific surface area hold significant potential in biomedical applications, such as tissue engineering, wound dressings, drug delivery, etc
A physics-informed 3D surrogate model for elastic fields in polycrystals
No full textInternational audienceWe develop a physics-informed neural network pipeline for solving linear elastic micromechanics in three dimensions, on a statistical volume element (SVE) of a polycrystalline material with periodic geometry. The presented approach combines a convolutional neural network containing residual connections with physics-informed non-trainable layers. The latter are introduced to enforce the strain field admissibility and the constitutive law in a way consistent with so-called fast Fourier transform (FFT) algorithms. More precisely, differential operators are discretized by finite differences in accordance with the Green operator used in FFT computations and treated as convolutions with fixed kernels. The deterministic relationship between crystalline orientations and stiffness tensors is transferred to the network by an additional non-trainable layer. A loss function dependent on the divergence of the predicted stress field allows for updating the neural network’s parameters without further supervision from ground truth data. The surrogate model is trained on untextured synthetic polycrystalline SVEs with periodic boundary conditions, realized from a stochastic 3D microstructure model based on random tessellations. Once trained, the network is able to predict the periodic part of the displacement field from the crystalline orientation field (represented as unit quaternions) of an SVE. The proposed self-supervised pipeline is compared to a similar one trained with a data-driven loss function instead. Further, the accuracy of both models is analyzed by applying them to microstructures larger than the training inputs, as well as to SVEs generated by the stochastic 3D microstructure model, utilizing various different parameters. We find that the self-supervised pipeline yields more accurate predictions than the data-driven one, at the expense of a longer training. Finally, we discuss how the trained surrogate model can be used to solve certain inverse problems on polycrystalline domains by gradient descent
Return period of non-concurrent climate compound events: a non parametric bivariate Generalized Pareto approach
No full textInternational audienceCompound events (CEs), commonly defined as the "combination of multiple drivers and/or hazards that contributes to societal or environmental risk", often result in amplified impacts compared to individual hazards. In order to estimate the return period of bivariate CEs, a novel non-parametric approach employing bivariate Generalized Pareto distributions (bi-GPD) is proposed and compared to a copula-based approach. Special attention is given to account for temporal dependencies and non-concurrent compound events. The latter are defined as excess of variables over a threshold at a relatively close time. The return period of such bivariate events is carefully defined and closed-form expressions are obtained for both approaches. Simulations reveal the bi-GPD approach is effective in case of positive asymptotic dependence and should be avoided in case of asymptotic independence. The novel approach is then applied to ERA5 reanalysis data to analyze two types of compound events: a spatial CE with simultaneous floods due to accumulated precipitations across two large watersheds in France and a preconditioned CE consisting of devastating floods triggered by antecedent precipitatio
Cold spray on short carbon fiber reinforced PEEK substrates
No full textInternational audienceAbstract This work focuses on the metallization of short carbon fiber reinforced PEEK composites by the cold spray technology. In this process, powder particles are accelerated to high speeds by a heated and pressurized gas. Particles maintain their solid state all along the process. The main challenges identified in previous studies on the topic concern the low adhesion of metal coatings onto polymer-based composites and the risk of damaging the composite during the cold spray process. The innovative strategy employed in this study consists in spraying a mixed Al-PEEK feedstock powder, with different PEEK proportions within the mixture. Both high-pressure and low-pressure cold spray systems have been tested for comparison. The principal outcomes of the study can be summarized as follows: coatings produced by low-pressure cold spray can achieve higher adhesion values; the higher the spraying temperature, the higher the adhesion; an increased PEEK content in the feedstock results in higher adhesion, at the expense of lower electrical conductivity. When PEEK content in the feedstock passes a certain threshold, located between 10 and 20 vol.%, the coatings completely lack electrical conductivity. A compromise thus has to be found, depending on the requirement of each application, to achieve a satisfying balance between the antagonist properties of adhesion and electrical conductivity. Finally, two mechanisms leading to coating creation and growth for mixed metal-polymer feedstock powders have been proposed
LED: Light Enhanced Depth Estimation at Night
No full textInternational audienceNighttime camera-based depth estimation is a highly challenging task, especially for autonomous driving applications, where accurate depth perception is essential for ensuring safe navigation. Models trained on daytime data often fail in the absence of precise but costly LiDAR. Even vision foundation models trained on large amounts of data are unreliable in low-light conditions. In this work, we aim to improve the reliability of perception systems at night time. To this end, we introduce Light Enhanced Depth (LED), a novel, cost-effective approach that significantly improves depth estimation in low-light environments by harnessing a pattern projected by high definition headlights available in modern vehicles. LED leads to significant performance boosts across multiple depth-estimation architectures (encoder-decoder, Adabins, DepthFormer, Depth Anything V2) both on synthetic and real datasets. Furthermore, increased performances beyond illuminated areas reveal a holistic enhancement in scene understanding. Finally, we release the Nighttime Synthetic Drive Dataset, a synthetic and photorealistic nighttime dataset, which comprises 49,990 comprehensively annotated images. To facilitate further research, both synthetic dataset and code are publicly available at https://simondemoreau.github.io/LED/. IntroductionAdverse conditions, such as harsh weather or nighttime, pose significant challenges to many computer vision applications. Despite impressive progress in perception systems for autonomous driving, enabled by powerful deep neural architectures and training techniques, the challenges of nighttime navigation persist. Accurate depth estimation is a crucial aspect</div
Sociologie des circuits financiers. Les infrastructures de l’argent et leurs politiques
No full textInternational audienceHow can the social sciences implement the injunction to 'follow the money' to study its political effects? How do financial circuits both enable and constrain European and national public policies? The Sociology of Financial Circuits proposes to combine a sociology inspired by the work of Viviana Zelizer with infrastructure studies in order to achieve a detailed understanding of the materiality of money circulation. A series of in-depth empirical case studies illustrates the fruitfulness of this approach. The book provides access to the worlds of the money of institutions, the social and political meanings embedded in technological, legal and accounting infrastructures, and the interdependencies between financial circuits and public policies. The chapters shed light on how the politics of money is transforming both the state and capitalism.Comment les sciences sociales peuvent-elles mettre en œuvre l’injonction à « suivre l’argent » ? Comment les circuits de l’argent rendent-ils possibles tout en les contraignant les politiques publiques européennes ou nationales ? La sociologie des circuits financiers propose de croiser une sociologie inspirée par les travaux de Viviana Zelizer et les infrastructures studies afin d’aboutir à une compréhension fine de la matérialité de la circulation de l’argent. Une série d’études de cas empiriquement fouillées illustrent la fécondité de la démarche. L’ouvrage donne accès aux mondes de l’argent institutionnel, aux significations sociales et politiques inscrites dans les infrastructures informatiques, juridiques et comptables, et aux interdépendances entre circuits financiers et politiques publiques. Les chapitres permettent d’éclairer comment les politiques de l’argent transforment à la fois l’État et le capitalisme
A Short Introduction to Metal Forming
No full textCAS - CERN Accelerator School: Mechanical & Materials Engineering for Particle Accelerators and Detectors, 2-15 June 2024, Sint-Michielsgestel, NetherlandsInternational audienceThe present paper is a short introduction to metal forming. The paper highlights the different disciplines that should be considered when analyzing metal forming. Hence, mechanical flow, heat flow, mechanical and thermal contact, and microstructure evolution are presented within the perspective of metal forming. For every aspect, well-established knowledge is presented in addition to scientific open questions on which active research is still done