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A Godunov-type scheme and a relaxation scheme for second-order turbulence-moment models
No full textInternational audienceA Godunov-type scheme and a relaxation scheme are presented to approximate discrete solutions of the convective subsystem arising from a second-order turbulence model in the incompressible framework. An analytical representation is proposed for the case of the occurrence of a laminar zone. Numerical tests of the two schemes and a comparison with the Rusanov scheme complete the paper
Effet des conditions de trempe et de revenu d'un acier faiblement allié pour réacteur sous pression (A508 Gr.3) sur l'amorçage de la rupture fragile et sur sa ténacité
No full textThe fracture toughness and the brittle fracture initiation of a quenched/tempered A508 Gr.3 low-alloy steel were studied to reveal the effects of heat treatment conditions. The results show better fracture toughness properties (i.e. lower fracture toughness reference temperature T0 (according to ASTM E1921 standard)) at higher quenching rate and lower tempering parameter, as expected. Three types of brittle fracture initiation mechanisms have been observed at the microscopic scale: at grain boundary carbides; on inclusions; at grain boundaries with no detectable particle. The proportion of occurrences of crack initiations cases observed at inclusions was higher when the reference temperature T0 is lower, either with a smaller tempering parameter or a higher quenching rate. Overall, the mechanism of crack initiation on a carbide as observed in this study was predominant
Is tropicalization of European estuarine fish communities a matter of latitude?
No full textEach of the last ten years -from 2015 to 2024- has been the warmest on record since 1850, and there is growing evidence that global warming is reshaping biodiversity across ecological scales. At the community level, climate change drives local extinctions, species invasions, and shifts in species relative abundances in the communities —typically resulting in species assemblages dominated by warm-affinity species. This pattern has long been attributed to a poleward expansion of tropical and subtropical species. However, thermal changes in communities arise from four distinct processes: tropicalization (increase in the abundance of warm-affinity species), deborealization (decline in cold-affinity species), borealization (increase in cold-affinity species), and detropicalization (decline in warm-affinity species). While tropicalization is often emphasized, deborealization, especially in coastal and estuarine systems, may be underappreciated.We analysed long-term fish abundance time series from 10 temperate estuarine systems across northeastern Atlantic Europe (Portugal to the UK) applying the Community Temperature Index (CTI). Our results revealed a broad reorganization of estuarine fish communities toward higher predominance of warm-affinity species, with a latitudinal gradient in intensity: mid-latitude estuaries being the most affected. Tropicalization dominates overall trends. However, this trajectory appears to unfold in distinct phases that vary with latitude and ecoregion. Initially, the abundance of species cold-water affinities would decrease drastically (northern estuaries). Signs of deborealization may thus represent an early stage of climate-driven community reshaping. Then, driven by increases in estuarine resident, marine estuarine opportunist, and subtropical species, a striking tropicalization could occur (mid latitude estuaries of the Bay of Biscay, except the Gironde) until the community becomes relatively stable (Portuguese estuaries). Our findings underscore the complex and spatially heterogeneous responses of estuarine fish communities to climate change, emphasizing the need to consider multiple underlying processes—not just tropicalization—when assessing ecological impacts in coastal ecosystems
On the relation between microstructure and impact toughness of 17-4 PH stainless steel produced by powder bed fusion laser beam (PBF-LB)
No full textInternational audienceThis work investigated the effects of aging heat treatments on the microstructure and, consequently, the quasi-static (tensile properties) and dynamic (impact toughness) mechanical behaviour of a 17-4 PH stainless steel produced by powder bed fusion laser beam (PBF-LB). Multiscale microstructural characterization, using X-ray diffraction, scanning and transmission electron microscopy and electron backscatter diffraction, was conducted to establish quantitative correlations between microstructural evolution and mechanical performance, providing insight into the mechanisms governing plasticity and fracture. The PBF-LB specimens exhibited tensile strengths comparable to or exceeding those of conventionally manufactured counterparts but consistently showed significantly lower impact toughness, regardless of heat treatment conditions. Within the complex microstructure, strain-induced transformation of reversed austenite was found to enhance ductility and impact toughness. SiO2 inclusions, originating from the starting powder, were identified as nucleation sites for micro-cavities and proved detrimental to impact toughness. Meanwhile, the distribution of Cu-rich nanoprecipitates could be tailored to favour either tensile strength through Orowan strengthening or impact toughness by enhancing local plasticity, but not both simultaneously. This work highlights the pronounced strength-toughness trade-off inherent in PBF-LB-produced 17-4 PH alloys and reveals the interplay between strength, ductility, and toughness. These findings underscore the need for further research into dynamic loading mechanical properties, especially for demanding applications such as those in the nuclear sector
Fast-Tracking Solar Cell Materials Discovery Using Automation, Physics Modeling, and Bayesian Machine Learning
No full textInternational audienceCombating climate change hinges on our ability to accelerate scientific breakthroughs, particularly in the development of future energy solutions. Luckily, artificial intelligence (AI) is making this possible by transforming every stage of materials development from conceptual design and synthesis to characterization and performance evaluation. In photovoltaics, AI is improving the reliability of mature technologies such as silicon and driving innovation in emerging solutions like perovskites, opening the door to higher efficiencies and broader deployment scenarios. In this work, we demonstrate how combining Bayesian machine learning with physics-based modeling not only accelerates scientific discovery but also improves the interpretability of black-box optimization in automated fabrication platforms. First, we showcase how our probabilistic Bayesian framework (Chakar et al., 2024, 10.1016/j.solener.2024.112595), combined with drift-diffusion modeling, offers a physically grounded alternative to conventional techniques by linking performance and stability metrics to interpretable parameters such as charge carrier mobility and defect densities. We highlight how this approach – which has been successfully validated for identifying photovoltaic degradation pathways, linking indoor and outdoor aging, and assessing passivation effects – can be extended to provide actionable feedback for automated platforms for the synthesis and characterization of novel perovskite solar cells. Specifically, we show how connecting batch-to-batch variability and performance improvements to fundamental cell properties helps pinpoint the automation steps that need further refinement. The ability to capture prediction uncertainty makes this approach particularly well-suited for multi-solution problems such as identifying performance bottlenecks and optimizing processes, where multiple routes can yield comparable outcomes. However, as this requires extensive simulations, Bayesian optimization (BO) emerges as a practical solution for navigating the search space more intelligently. Nonetheless, BO has its own set of challenges, particularly due to the curse of dimensionality and high computational cost when exploring large parameter spaces. We therefore introduce SCORE (Chakar, 2024, 10.48550/arXiv.2406.12661), a technique that can overcome these limitations and tackle key problems in solar energy research and beyond, providing a resource-efficient and accessible solution for a broad range of researchers. Finally, we describe how these tools will be integrated into a high-throughput platform being established at IPVF to support automated perovskite thin-film fabrication, characterization, and stability testing. Bringing together diverse research efforts, the platform will enable precise control over processing and measurement workflows, generating reproducible, high-quality data to accelerate joint research efforts across the perovskite community
ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly and monthly sea surface salinity products from L-band, v5.5
No full textWeekly and monthly sea surface salinity fields derived from satellite observations (L-Band radiometry) - version 5.5The European Space Agency (ESA) Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2023 period. This dataset collection contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 week. It has been spatially sampled on a regular 0.25° grid and 1 day of time sampling. This product is also available on polar 25 km EASE-2 (Equal Area Scalable Earth) grid. A monthly product is also available, at a spatial resolution of 50 km and a time resolution of 1 month. It is spatially sampled on a 0.25° grid and 15 days of time sampling. This product is also available on polar 25km EASE-2 grid. In addition to salinity, information on uncertainties are provided. For more information, see the user guide and product documentation available on the Sea Surface Salinity CCI web pag
MODELLING SHORELINE DYNAMICS IN COMPLEX MACROTIDAL ENVIRONMENTS USING NEURAL NETWORKS
No full textInternational audienceThe aim of this work is to test an artificial intelligence approach with basic hydrodynamic and morphological variables, in order to assess the effectiveness of such methods in modelling complex beach dynamics. A simple feedforward neural network is used to evaluate the impact of selected variables on the prediction of the dynamics of specific shoreline isocontour proxies extracted from beach profiles, and to build a predictive model that could simulate the position of the proxies. The model was trained on datasets from two sites from the French coastal monitoring program DYNALIT, Porsmilin and Vougot beaches, and their profile measurements, water levels and wave measurements over 20 years. These two sites were selected due to differences in their morphology and hydrodynamics, as a means to assess the performance of neural networks over a larger variety of situations. A range of temporalities encompassing 3 days, 7 days, 14 days, and 30 days of selected hydrodynamic and morphological variables were used to study the impact time scales can have on modelled shoreline positions. The shoreline proxies used for Porsmilin and Vougot beaches correspond respectively to the berm and the contact between the dune toe and the upper beach, which can be assessed and followed along each beach profile. The shallow feedforward network included 1 hidden layer and 5 nodes, and was ran 50 times in order to assess the models' performance. The models were generally successful, with a blind shoreline prediction R of 0.88 in Porsmilin and 0.72 in Vougot. This artificial neural network (ANN) approach showed all-around better performance than previous beach equilibrium models, which is very encouraging regarding the prediction of future beach morphodynamics and the use of Machine Learning algorithms therein.</div
Simulation of clad ballooning during NSRR RIA tests with the fuel performance code ALCYONE
No full textInternational audienceThis paper presents a follow-up to a PLEIADES/ALCYONE [1] previous simulation exercise [2] of a selection of NSRR RIA integral experiments where following ranges were covered: 200 J/g – 800 J/g (fuel enthalpy increase), 293 K – 1200 K (clad outer surface peak temperature), 0 s – 2 s (film boiling duration), 2% – 25% (transient fission gas release), 0% – 25% (clad residual hoop strain). Relying on ALCYONE 2.1 RIA-related capabilities to model the post-Departure from Nucleate Boiling phase, oxide fuel grain boundary cracking-induced dominant fission gas release mechanism as well as axial gas flow induced by pressure gradients in the fuel rod free volumes [2][3][4], it had been shown that high clad residual hoop strains consistent with the ones measured at high enthalpy increase could be predicted provided fission gas release, consistent with the clad temperature elevation, is prescribed in the simulations. Investigations havebeen pursued to include a kinetic transient fission gas release approach. This paper aims at showing the current progress in that matter based on the latest simulations with ALCYONE 2.2. The consistency of the proposed approach is indirectly demonstrated by the improved prediction of clad ballooning during NSRR RIA tests
