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Advanced Segmentation and Geometric Analysis of Geological Samples
No full textInternational audienceThe identification of lithology from drill cuttings is a crucial step in reservoir characterization, providing di- rect insights into the drilled rock formations; neverthe- less, traditional cuttings analysis is labor- intensive, re- quires expert interpretation, and often suffers from in- consistency due to its subjective nature. To address these challenges, we propose an automatic, real-time method for drill cuttings characterization. We propound a cut- ting instance segmentation workflow for analyzing 2D images of densely clustered cuttings. Traditional seg- mentation methods struggle to deliver satisfactory results in such complex scenarios. To overcome these limita- tions, we combine marker-based segmentation with a su- pervised deep learning model; nonetheless, training such a model depends on the quality of the reference dataset, and annotation is typically time-consuming and costly. The novelty of our approach lies in its fully automated workflow, eliminating the need for manual annotation or refinement. We validate our approach on a large, diverse dataset of real drill cuttings, demonstrating high performance across various lithologies and cuttings’ sizes. Index Terms— Instance segmentation, Geometric analysis, drill cuttings, convolutional neural networks (CNN)
Long-term impacts of the EU ban on the sale of new thermal cars on critical material needs and energy transition
No full textInternational audienceIn the European Union (EU), in 2019, about a quarter of total CO2 emissions were coming from the transport sector, with 71.7% generated by road transportation. Having set an objective of reducing its transport sector GHG emissions by 90% in 2050 compared with 1990, the EU has adopted a ban on the sale of new ICEVs within its borders from 2035. Transport demand management and the adoption of new technologies such as electromobility are needed solutions to achieve this objective. Indeed, when charged with low-carbon electricity, battery electric vehicles (BEVs) emit less GHG than internal combustion engine vehicles (ICEVs) during their lifecycle. However, disseminating BEVs will increase battery demand, implying a possible sharp increase in the transport sector’s material requirements, especially minerals. Electric vehicles (EVs) use approximately six times more minerals than ICEVs. Copper, cobalt, lithium, manganese, nickel, graphite and rare-earth elements (REEs) can be cited among them. This growing dependency to mineral flows to achieve the energy transition has sparked a lot of discussion for the past years, fuelling the field of material criticality studies. The aforementioned minerals are all deemed critical by the European Commission, meaning they are essential for strategic industrial sectors, as well as at risk of potential shortages. Given the economic, geopolitical, technological and environmental implications of these growing needs for materials, how will the ban on the sale of new ICEVs affect critical material demand? This study aims to assess future critical materials needs entailed by the electrification of the transport sector and to place them in the context of EV supply chains to highlight the challenges the EU will face. Long-term modelling is used to this end
Phase Equilibria and Solid Precipitation in CO₂-NH₃ Mixtures
No full textInternational audienceThis study investigates the thermodynamic behavior of Carbon Dioxide (CO2) – Ammonia (NH3) mixtures under enough low-temperature conditions to consider the formation of Ammonium Carbamate (AC), a solid precipitate that risks pipeline obstruction and pressure fluctuations during liquid CO2 transportation. As part of the Joint Industry Project (JIP) CO2’s first work package on thermodynamics, the research aims to refine legacy models [1][2] through experimental validation, ensuring reliability for crucial phases such as ship loading/unloading and geological CO2 storage in the North Sea.The work, conducted in the CTP experimental platform, first defines the experimental protocol to explore the phase behavior and chemical interactions in CO2-rich and NH3-rich environments. The approach allows at the same time to characterize the thermodynamical behavior of the CO2 – NH3 system and to thresholds AC precipitation and analyze its influence on mixture stability.The findings directly contribute to the JIP’s dataset, enhancing predictive accuracy for impurities like NH3 in CO2 streams. This advancement supports the feasibility assessment of large-scale CO2 transportation and geological storage in the North Sea, aligning with stringent safety and technical standards. The updated models provide a framework for mitigating risks associated with reactive impurities, informing guidelines for liquefaction processes, infrastructure design, and regulatory protocols.This work bridges gaps between theoretical models and real-world conditions, reinforcing JIP’s goal of enabling safe, efficient decarbonization through carbon capture and storage
Multi-grid graph neural networks with self-attention for computational mechanics
No full textInternational audienceCombining computational mechanics with machine learning has recently attracted a lot of attention, especially in computational mechanics, where researchers aim to improve the accuracy and computational cost of the simulations. While convolutional neural networks have been used to turn mesh data into images, newer methods now use graph neural networks to work directly with mesh structures. In this work, we introduce a new multigrid graph neural network framework, designed for time-dependent three-dimensional computational fluid dynamics simulations. The proposed method uses self-attention within the message-passing steps to better understand the solution behavior across both space and time. It also uses attention scores to dynamically prune and refine meshes, making it possible to handle large-scale simulations efficiently without losing accuracy. Inspired by Bidirectional Encoder (BERT), we also add a self-supervised training method tailored for graphs, which helps the model generalize better. Tests on benchmark datasets show that the method performs better than current state-of-the-art approaches, even on larger and more complex meshes. Codes and datasets are available at https://github.com/DonsetPG/graph-physics
Utilisation de données géonumériques pour la thermique du bâtiment et l'analyse de cycle de vie : prise en compte des masques solaires
No full textInternational audienceLes impacts environnementaux de projets urbains peuvent être évalués par la méthode de l'analyse de cycle de vie (ACV) dans un objectif d'écoconception. Il ressort d'études ACV ayant pour périmètre un quartier que les bâtiments -et, en particulier, les consommations énergétiques (chauffage, climatisation) durant leur phase d'usage -sont d'importants contributeurs aux impacts du quartier. Pour évaluer plus précisément ces consommations, que ce soit à l'échelle du bâtiment, du quartier, voire de la ville, de nombreuses données géonumériques sont disponibles sur l'ensemble du territoire. Elles peuvent nous apporter des informations susceptibles d'améliorer notamment les simulations thermiques dynamiques (géométrie du relief, de la végétation, du bâti, réseaux, matériaux constituant les différentes facettes du quartier, etc.). Ici, nous analysons spécifiquement l'effet des masques solaires (bâti et végétation) sur les consommations énergétiques des bâtiments, le confort thermique à l'intérieur de ceux-ci et en conséquence sur leur évaluation environnementale. Plusieurs méthodes de génération des masques, à partir de différentes bases de données de l'IGN (BD TOPO, LIDAR HD, MNS corrélés), de résolutions variées, sont alors comparées, afin d'éclairer les compromis à faire entre automatisation des études, temps de calcul et précision souhaitée des résultats
Retrospective of Prospective Exercises: A Chronicle of Long-Term Modelling and Energy Policymaking in France
No full textInternational audienceThe French prospective aims to influence the shaping of the future by exploring different scenarios and evolution, so that long-term challenges are considered in current decision-making. In practice realizing this linkage between modelers and policy makers is itself a process and comes with its own challenges. This chapter reflects on modelling to inform various energy policy assessments in France aligned with SDGs 7 (energy), 8 (economic growth), 9 (industry), 11 (cities) and 13 (climate), across several exercises around the reduction of greenhouse gas emissions, the nuclear phase-out, the implementation of a 100% renewable power system with its reliability issues and the assessment of carbon value. It is based on our practical experience at the Centre for Applied Mathematics (MINES Paris—Université PSL) where TIMES has been used as a bottom-up optimization approach to offer insights, within extended committees, to French policymakers. The model we developed is the TIMES-FR model where FR stands for France. Our experience reveals that policymakers do not fully harness the wealth of technical insights provided by researchers, as they often prioritize short or mid-term challenges. While policy perspective does not seem to need the precision and comprehensiveness of technical modelling, we show that the insights tend to penetrate at some point in the public debate and policy-making sphere, which emphasizes the relevance of using prospective and energy system models to address climate change
Strategic Confusopoly: Evidence from the UK Mobile Telecommunications Market
No full textInternational audienceCan entire markets strategically confuse consumers to raise market prices? Using a detailed data set covering virtually all mobile phone tariffs and their handsets in the United Kingdom between January 2010 and September 2012, we study the evolution of quality-adjusted prices and find that they increased until December 2010, even though the industry was mature, technologically homogeneous, and competitive. Upon exploring the role of several salient factors, such as differentiation and product proliferation by firms that may have affected this evolution, we argue that the primary driver is the implementation of obfuscation strategies by firms. The observed price increase is significantly correlated with the rate at which operators implemented dominated tariffs (i.e., tariffs for which there is a cheaper alternative from the same operator), indicating that firms use obfuscation strategies to reduce product transparency, thereby elevating overall prices. Importantly, the presence of dominated tariffs raises not only the prices of these contracts but also, those of efficient ones, distinguishing our findings from a behavioral price discrimination strategy that would only affect inattentive consumers. Our exploratory study is one of the first to offer suggestive evidence of obfuscation as an industry-wide supply-side phenomenon. Funding: A. Elsas-Nicolle acknowledges funding from the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under a Marie Sklodowska-Curie Grant Agreement [Grant 754388], LMU Munich's Institutional Strategy LMUexcellent within the framework of the German Excellence Initiative [Grant ZUK22] and LMU Collaboration Funds (2021-2022). C. Genakos acknowledges financial support from the Cambridge Judge Business School [Small Grants Scheme]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2024.0285
Mechanisms and numerical modelling of the failure of a three layers of polymers by embedded 4-point bending
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Une Technique d’Homogénéisation pour les matériaux à microstructure périodique lacunaire fractale
No full textInternational audienceThis paper proposes an original homogenization approach for materials characterized by a periodic lacunar microstructure with fractal geometry. The geometrical complexity of these materials creates major difficulties for the application of conventional homogenization methods to evaluate their effective properties. As the fractal level increases, the number of degrees of freedom in the numerical models grows sharply, making the computations very expensive and, beyond a certain level, inaccessible. To address this challenge, an indirect homogenization technique was developed on the basis of a classical direct homogenization technique (periodic homogenization by the average method), by embedding into this homogenization process the iterative process of generating fractal geometries following the iterated function systems principle. The two techniques were applied to two-dimensional extruded geometries and to a fully three-dimensional case. For iteration levels where both techniques could be compared, their results were very close. In addition, the indirect technique is substantially more efficient, requiring lower computational cost in terms of time and memory, and enabling the study of iteration levels that cannot be reached with the direct one. Finally, when the iteration level tends to infinity the theoretical and numerical convergence values of the homogenized elasticity tensors are equal. This provides direct proof of the quality of the method.Cet article propose une nouvelle technique d’homogénéisation pour des matériaux caractérisés par une microstructure périodique lacunaire à géométrie fractale. La complexité géométrique de ces matériaux pose des problèmes importants pour l’application des techniques d’homogénéisation traditionnelles afin d’évaluer leurs propriétés effectives. En effet, l’augmentation du niveau fractal entraîne une forte croissance du nombre de degrés de liberté dans les modèles numériques, ce qui rend les calculs très coûteux et, au-delà d’un certain seuil, inaccessibles. Pour surmonter ce défi, une technique d’homogénéisation indirecte a été développée à partir d’une technique d’homogénéisation directe connue (l’homogénéisation périodique par la technique de la moyenne), en y intégrant le processus itératif de génération des géométries fractales par les systèmes de fonctions itérées. Les deux techniques ont été appliquées à des géométries fractales bidimensionnelles extrudées ainsi qu’à un cas tridimensionnel. Pour les niveaux d’itération où les deux techniques pouvaient être comparées, leurs résultats se sont révélés très proches. De plus, la technique indirecte s’est montrée nettement plus efficace, nécessitant un coût de calcul réduit, tant en temps qu’en mémoire, et permettant d’étudier des niveaux d’itération inaccessibles avec la technique directe. Enfin, lorsque le niveau d’itération tend vers l’infini, le tenseur de rigidité à convergence théorique et celui numérique sont égaux. Ce résultat apporte une preuve directe de la qualité de la méthode
