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Accelerating Next-Gen Materials Discovery for Photovoltaic Applications Using AI-Driven Synthesis and Characterization
No full textInternational audienceTo effectively combat climate change, we must accelerate the pace of scientific breakthroughs. Fortunately, the power of artificial intelligence (AI) has opened up new possibilities, allowing us to streamline the entire process of developing new materials, from conceptual design and synthesis to in-depth characterization and analysis. In this work, we present an automated platform that leverages AI and physics modeling for high-throughput perovskite thin-film deposition, characterization, and performance and degradation analysis. Currently under development at IPVF within the project MATCH-UP, this platform is designed to control fabrication steps, material composition, and characterization workflows, aiming to generate reproducible high-quality data for national and international collaborations. Our effort also incorporates implementing SCORE, a novel algorithm that outperforms classical optimization techniques. Typically, automated discovery relies on Bayesian optimization, which faces challenges due to the curse of dimensionality and the need for significant computational resources in high-dimensional spaces. Herein, we demonstrate how SCORE not only addresses these limitations but also excels in several solar energy challenges, offering a solution that researchers can use without needing heavy computational resources
Effects of Direct-Quadrature Rotor Current Sensors’ Faults in Wind Energy Conversion System based on Doubly Fed Induction Generator
No full textInternational audienceThis paper investigates the dynamic performance of a grid-connected wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) in the presence of additive and multiplicative faults in directquadrature rotor current sensors. The system architecture includes direct current-link voltage regulation, via the grid side converter and active/reactive power control, through the rotor side converter. Unlike prior studies that mainly consider symmetrical grid faults, this work systematically introduces various sensor faults specifically in stator current, stator voltage, and rotor current measurements into the rotor current control loop. The impact of these faults is assessed by examining key electrical parameters such as current, voltage, active, and reactive power across the grid, stator, and rotor. Simulation results reveal that rotor current sensor faults significantly degrade system stability and power regulation. These findings highlight the importance of developing robust fault detection and isolation and fault-tolerant control strategies and provide a foundation for enhancing the reliability of DFIG-based WECS under sensor fault conditions.Cite this article as: N. Chouider, K. Beddek, R. Haddouche, M. Zerrougui and O. Remdani, "Effects of direct-quadrature rotor current sensors’ faults in wind energy conversion system based on doubly fed induction generator," Electrica, 25, 0042, 2025. doi: 10.5152/electrica.2025.25042
Overview of the R&D activities on earthquake engineering and seismic risk assessment within the joint framework CEA-EDF-Framatome-IRSN
No full textInternational audienceThe French nuclear community is highly active in the field of earthquake engineering, especially regarding seismic risk assessment. Among the existing safetyoriented frameworks, there is one which involves not only nuclear operators such as Electricité de France (EDF), the French Sustainable Energies and Atomic Energy Commission (CEA) and FRAMATOME, but also the French technical safety organization (TSO) which is the Institute for Radiation Protection and Nuclear Safety (IRSN) that has become the French Authority of Nuclear Safety and Radiation protection since the 1st of January 2025. Within this framework, research activities are conducted to enhance the scientific knowledge and improve nuclear safety of either existing or new nuclear facilities. These activities deal with innovative topics and challenging issues that are crucial in the seismic risk assessment process. More precisely, the aim of the collaborative work is to better assess the seismic margins when available, to better understand the beyond-design behavior of nuclear buildings or related equipment and to improve the necessary knowledge to produce robust seismic risk assessment studies. This paper provides an overview of the past, ongoing and future joint activities carried out by CEA, EDF, FRAMATOME and IRSN and highlights the main findings and future challenges and opportunities in the field of seismic risk assessment of nuclear facilities
A Stationary Mean-Field Equilibrium Model of Irreversible Investment in a Two-Regime Economy
No full textWe study firms size distribution in a mean-field model of Cournot competition in a commodity market, where price follows an inverse power demand function. Firms face irreversible investment decisions and constant depreciation of production capacity. Output is affected by Gaussian productivity shocks, whose volatility and the price function can shift due to rare macroeconomic events modeled by a two-state Markov chain. Firms aim to maximize expected discounted profits, net of investment and operating costs, based on the long-run stationary price. We establish existence and uniqueness of a stationary mean-field equilibrium and characterize it through a barrier-type investment strategy with endogenous thresholds for each economic regime. A quasi-closed form for the stationary distribution of firms' states is provided. The model generates Pareto-distributed firm sizes, consistent with empirical industry data. It also shows that downturns raise market concentration and that firm performance depends on depreciation rates and the persistence of economic fluctuations. We consider a mean-field model of firms competing à la Cournot on a commodity market, where the commodity price is given in terms of a power inverse demand function of the industry-aggregate production. Investment is irreversible and production capacity depreciates at a constant rate. Production is subject to Gaussian productivity shocks, whereas large nonanticipated macroeconomic events driven by a two-state continuous-time Markov chain can change the volatility of the shocks, as well as the price function. Firms wish to maximize expected discounted revenues of production, net of investment, and operational costs. Investment decisions are based on the long-run stationary price of the commodity. We prove existence, uniqueness, and characterization of the stationary mean-field equilibrium of the model. The equilibrium investment strategy is of barrier type, and it is triggered by a couple of endogenously determined investment thresholds, one per state of the economy. We provide a quasi-closed form expression of the stationary density of the state, and we show that our model can produce Pareto distribution of firms' size. This is a feature that is consistent both with observations at the aggregate level of industries and at the level of a particular industry. We provide evidence that persistent periods of economic downturn increase market concentration. We demonstrate that firms with slowly depreciating production capacities fare better in a stable, average economy, whereas firms with quickly depreciating assets can benefit from sequences of boom and bust. Funding: This work was supported by the Agence Nationale de la Recherche
Tendances d'évolution des effectifs de truite à proximité des aménagements hydroélectriques
No full textInternational audienc
Adaptive confidence intervals for extreme quantiles from heavy-tailed distributions
No full textThe celebrated Weissman estimator provides a simple way to compute extreme quantiles, lying outside the observation range, from heavy-tailed distributions. Asymptotic confidence intervals can also be built basing on its asymptotic normality, but they may suffer from poor coverage properties in practice. We propose several higher order approximations of the Weissman estimator asymptotic distribution together with a data-driven procedure to automatically select the most appropriate one. The usefulness of the associated adaptive confidence interval is illustrated on an intensive simulation study as well as on two climatic and financial data sets
Symbiont diversity and light-organ morphology in Sepiola affinis
No full textInternational audienceAbstract The squid-vibrio symbiosis has illuminated fundamental mechanisms of beneficial animal-microbe associations, yet the interactions within sepiolid squid in the Mediterranean Sea remain underexplored. Here we characterize the Sepiola affinis squid-vibrio symbiosis by combining whole-genome sequencing of light-organ isolates, confocal microscopy, and temperature-dependent growth assays. Comparative genomic analyses (ANI, phylogenomics, and functional analyses) revealed two previously undescribed Vibrio species associated with the S. affinis light organ. One species clusters more distantly from other Vibrio while the other species is closer to established Vibrio clades, with the second species exhibiting an expanded repertoire of mobile elements and Type VI secretion components, suggesting heightened capacity for genetic exchange and interbacterial interaction. Confocal microscopy of juvenile squid established that the S. affinis light organ comprises twelve crypts connected by pores and ducts, expanding the number of symbiotic niches relative to other sepiolid squid. In addition, fluorescently labeled isolates from the two Vibrio species colonized juveniles in both mono- and co-colonization patterns within crypts. Finally, growth assays across 16–24°C identified species-specific temperature differences, indicating temperature preference that may align with seasonal variability in the Mediterranean Sea. Together, these findings position S. affinis as a tractable model for studying how symbiont diversity, organ architecture, and interbacterial interactions contribute to the stability of a mutualistic symbiosis
Modelling drainage-surface exchanges with TELEMAC-SWMM at the scale of a neighbourhood
No full textInternational audienceUrban drainage system failures are becoming more and more common due to the increase in magnitude and frequency of extreme rainfall events and pose significant safety and sanitary risks for urban populations during urban flooding. Practitioners involved in urban planning need comprehensive numerical tools to accurately describe the interplay between flows in the drainage network and floodwaters in the street network during extreme rainfall. EDF R&D, France, and Yuansuan, China, have recently coupled TELEMAC with the stormwater management software SWMM, from the US-EPA, under the git development branch called "swordtail". This branch is available to developers for testing and validation. The coupled system is initially tested against experimental data from a 2×2 street network only (no sewer network), investigated by Mejia-Morales [2021] where numerical parameters are calibrated for the experimental platform, with errors below 3% for flow rates and 5% for flow depths. Building on this calibration, a second case study investigates a surface-todrainage scenario based on experimental observations of a 3×3 street network with 12 drains by Camazzola et al. [2025]. The model is able to reproduce flow rates through the drains, discharge in the main street, and qualitative flow structures such as swirling eddies
A tree-based Polynomial Chaos expansion for surrogate modeling and sensitivity analysis of complex numerical models
No full textThis paper introduces Tree-based Polynomial Chaos Expansion (Tree-PCE), a novel surrogate modeling technique designed to efficiently approximate complex numerical models exhibiting nonlinearities and discontinuities. Tree-PCE combines the expressive power of Polynomial Chaos Expansion (PCE) with an adaptive partitioning strategy inspired by regression trees. By recursively dividing the input space into hyperrectangular subdomains and fitting localized PCEs, Tree-PCE constructs a piecewise polynomial surrogate that improves both accuracy and computational efficiency. The method is particularly well-suited for global sensitivity analysis, enabling direct computation of Sobol' indices from local expansion coefficients and introducing a new class of sensitivity indices derived from the tree structure itself. Numerical experiments on synthetic and real-world models, including a 2D morphodynamic case, demonstrate that Tree-PCE offers a favorable balance between accuracy and complexity, especially in the presence of discontinuities. While its performance depends on the compromise between the number of subdomains and the degree of local polynomials, this trade-off can be explored using automated hyperparameter optimization frameworks. This opens promising perspectives for systematically identifying optimal configurations and enhancing the robustness of surrogate modeling in complex systems
Des essais virtuels à la réalité - essais de compression diagonale sur des panneaux en maçonnerie
No full textInternational audienceLors de séismes, les murs de remplissage en maçonnerie sont sollicités en cisaillement dans le plan, ce qui se traduit par des contraintes de traction et de compression le long de leurs diagonales. L'essai de compression diagonale, normalisé par l'ASTM, reproduit ces sollicitations sur des panneaux en maçonnerie afin d'analyser les mécanismes de rupture et de déterminer leur résistance. Traditionnellement, les déformations sont mesurées avec des capteurs LVDT, mais cette méthode locale reste limitée pour des matériaux hétérogènes comme la maçonnerie. La corrélation d'images numériques (CIN), méthode sans contact, s'impose comme une alternative permettant d'étudier les champs de déplacement et de quantifier la propagation des fissures de manière plus précise. Lors des essais, les fissures s'amorcent typiquement le long des joints de mortier, formant un motif en escalier. La taille de l'éprouvette nécessite une configuration multicaméra : deux caméras captent la cinématique 3D globale, tandis que trois autres se concentrent sur des zones spécifiques, au niveau des conditions aux limites et dans la région centrale où les fissures s'amorcent. Cette approche multiéchelle améliore la résolution des mesures. En parallèle, des simulations numériques sont réalisées avec le code Cast3M, utilisant un modèle basé sur une description détaillée des blocs et joints. Une étape clé consiste à identifier les paramètres des modèles en intégrant les données expérimentales. Pour cela, des expériences virtuelles sont simulées au préalable dans Blender, un logiciel d'animation, permettant d'optimiser le positionnement des caméras, de mener des analyses de sensibilité pour valider a priori l'identifiabilité des paramètres ainsi que les incertitudes de mesure avant de réaliser les essais proprement dits