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Oxygen defect configurations in single-phase UO
No full textInternational audienceHyperstoichiometric UO was characterized by neutron total scattering at high temperature in the single-phase UO region of the U/O phase diagram. The diffraction data confirmed a single-phase fluorite structure at high temperature. Analysis of the short-range data showed that the same structural model does not fit the pair distribution functions well. Instead, structural models containing specific configurations of oxygen defect clusters best represent the local atomic arrangement. Prevalent defect clusters previously proposed were fit to the experimental data, and moderately distorted oxygen cuboctahedra hypothesized by recent molecular dynamics simulations fit the data most accurately
Harmonic analysis of a repositioning plan optimization problem
No full textInternational audienceThe placement of the stops for refueling is an important challenge for the economic aspect in nuclearenergy production industry. Thus in this paper, we intend to study for the pressurized water reactorof 1300 MW so called PWR1300, how economical criteria as the mean core burn-up and safetycriterion as hot power factor, often not correlated, are repositioning sensitive. Given the hugenumber of possible combinations and the very expensive cpu cost of a steady cycle simulation, wehave set a methodology based on a surrogate model using Fourier expansion of the criterion functionon the symmetric group S푛. In this paper, we address this problem in an efficient way takinginto account the limited computational resources: thus we restrict our analysis to the permutationoperating only on the Uox Gadolinium assemblies as UGd. The criteria functions are evaluatedby using the Minos Solver of APOLLO3® neutronics simulation code along the last convergeddepletion cycle. As a result we establish a design of experiments, from which derive an expansionFourier surrogate model of first and second degree with a very good accuracy. After that, we studythe sensitivity of the criteria from the substitution model and we present as a result an intensity mapwhich attests to the importance of specific repositioning of the UGd. These first results show thatthe criteria are repositioning sensitive and call to follow further investigation to the application onmore complex permutation including Uox and UGd ones simultaneously
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
No full textInternational audienceUncertainty in the dynamics of the Amazon rainforest poses a critical challenge for accurately modeling the global carbon cycle. Current dynamic global vegetation models (DGVMs), which use one or two plant functional types for tropical rainforests, fail to capture observed biomass and mortality gradients in this region, raising concerns about their ability to predict forest responses to global change drivers. Here we assess the importance of spatially varying parameters to resolve ecosystem spatial heterogeneity in the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) DGVM. Using satellite observations of tree aboveground biomass (AGB), gross primary productivity (GPP), and biomass mortality rates, we optimized two key parameters: the alpha self-thinning (α), which controls tree mortality induced by light competition, and the nitrogen use efficiency of photosynthesis (η), which regulates GPP. The model incorporating spatially optimized α and η parameters successfully reproduces the spatial variability of AGB (R2 = 0.82), GPP (R2 = 0.79), and biomass mortality rates (R2 = 0.73) when compared to remote sensing observations in intact Amazon rainforests, whereas the model using spatially constant parameters has R2 values lower than 0.04 for all observations. Furthermore, the relationships between the optimized parameters and ecosystem traits, as well as climate variables, were evaluated using random forest regression. We found that wood density emerges as the most important determinant of α, which is in line with existing theory, while water deficit conditions significantly impact η. This study presents an efficient and accurate approach to enhancing the simulation of Amazonian carbon pools and fluxes in DGVMs by assimilating existing observational data, offering valuable insights for future model development and parameterization
A circularly polarized low-frequency radio burst from the exoplanetary system HD 189733
No full textInternational audienceAims. We aim to detect low-frequency radio emission from exoplanetary systems to gain insights into planetary magnetic fields, star–planet interactions, stellar activity, and exo-space weather. The HD 189733 system, hosting a well-studied hot Jupiter, is a prime target for such searches.Methods. We conducted NenuFAR imaging observations in the 15–62 MHz range to cover the entire orbital phase of HD 189733 b. Dynamic spectra were generated for the target and other sources in the field, followed by a transient search in the time-frequency plane. The data processing pipeline incorporated direction-dependent calibration and noise characterization to improve sensitivity. We also searched for periodic signals using a Lomb–Scargle analysis.Results. A highly circularly polarized radio burst was detected at 50 MHz, with a flux density of 1.5 Jy and a significance of 6σ at the position of HD 189733. No counterpart was found in Stokes I, likely because the emission is embedded in confusion noise and remains below the detection threshold. The estimated minimum fractional circular polarization of 38% suggests a coherent emission process. A periodicity search revealed no weaker signals linked to the planet’s orbital period, the star’s rotational period, or the synodic period and harmonic period between them. The burst’s properties are consistent with cyclotron maser instability (CMI) emission, however, the origin remains ambiguous. A comparison with theoretical models suggests star–planet interaction or stellar activity as potential origins. Alternative explanations such as contamination from other sources along the line of sight (e.g. the companion M dwarf) or noise fluctuation are plausible
Advanced Analysis of Selectively Grown GaN PN Diodes Using Multi-Technique Approach
No full textInternational audiencePower electronic devices are fundamental in electrical energy conversion, with gallium nitride (GaN) emerging as a key material for next-generation power devices due to its high breakdown field and superior performance over silicon[1][2], [3]. However, challenges such as lattice mismatch, thermal expansion differences, and stress-induced cracking hinder efficient device fabrication. Selective Area Growth (SAG) has proven to be an effective method for mitigating these challenges, enabling the growth of thick GaN layers with reduced stress and defect density[4].In this work, GaN-based quasi-vertical p-n diodes were fabricated on 200 mm Si(111) wafers using metal-organic vapor-phase epitaxy (MOVPE). The structure included AlN and AlGaN buffer layers, an undoped GaN layer, and three Si-doped GaN layers with doping concentrations ranging from 5 × 10¹⁶ cm⁻³ to 5 × 10¹⁸ cm⁻³. A 50 nm-thick Al₂O₃ mask, deposited via atomic layer deposition (ALD), facilitated the SAG process.To assess the doping distribution and uniformity in the grown layers, cathodoluminescence (CL) mapping was employed as a high-resolution, non-destructive technique. By correlating near-band-edge (NBE) linewidth broadening with doping concentration, local carrier densities were mapped at the nanoscale. The results revealed a net doping concentration of ~2 × 10¹⁶ cm⁻³ in the nominally undoped (NID) GaN layer and ~1 × 10¹⁷ cm⁻³ at the sidewalls. Mg doping uniformity was also examined, showing the presence of Mg in the top and in the sidewalls of the mesa. Complementary scanning spreading resistance microscopy (SSRM) and scanning capacitance microscopy (SCM) was used the doping distribution. Additionally, capacitance-voltage (C-V) measurements corroborated the net doping concentration in the drift layer.Electrical characterization demonstrated a high breakdown voltage (BV) under reverse bias at about 800 V, confirming the robustness of the fabricated devices. These findings emphasize the potential of SAG for advancing high-power GaN-based electronics by improving material quality and electrical performance
Digital plane detection in a point set: application to the interactive extraction of charcoal platforms from airborne LiDAR
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Observation of the anomalous Nernst effect in altermagnetic candidate Mn5Si3
No full textInternational audienceThe anomalous Nernst effect generates transverse voltage to the applied thermal gradient in magnetically ordered systems. The effect was previously considered excluded in compensated magnetic materials with collinear ordering. However, in the recently identified class of compensated magnetic materials, dubbed altermagnets, time-reversal symmetry breaking in the electronic band structure makes the presence of the anomalous Nernst effect possible despite the collinear spin arrangement. In this work, we investigate epitaxial Mn5Si3 thin films known to be an altermagnetic candidate. We show that the material manifests a sizable anomalous Nernst coefficient despite the small net magnetization of the films. The measured magnitudes of the anomalous Nernst coefficient reach a scale of microVolts per Kelvin. We support our magneto-thermoelectric measurements by density-functional theory calculations of the material's spin-split electronic structure, which allows for the finite Berry curvature in the reciprocal space. Furthermore, we present our calculations of the intrinsic Berry-curvature Nernst conductivity, which agree with our experimental observations
Simulation of the influence of initial voids on the mechanical behaviour of steel-concrete-steel structures
No full textInternational audienceSteel-concrete-structures, which are composed of a concrete core related to outer steel plates through steel dowels, are of increasing interest due to their modularity, performance and durability. However, the presence of two outer steel plates makes difficult any visual monitoring of the quality of concrete pouring and the detection of potential defects. It thus requires specific methodologies, based either on experimental approaches for detection (nondestructive methods) or on numerical strategies to evaluate the potential consequences of concrete defects on the mechanical behavior.In this contribution, the structural consequences of a concrete defect are investigated through its impact on the dowel-concrete interaction at a local scale. Classical push-out tests are first simulated, using a refined approach, specifically developed for this scope [1] and based on damage mechanics for concrete. It includes “energetic” regularization in both tension and compression for concrete and the introduction of joint elements at the junction between the dowel and the steel plate. Validation is obtained by comparison to experimental results.Voids are then introduced into the mesh to represent defects in concrete. The impact in terms of stiffness and resistance is especially studied. A generic sensitivity analysis is proposed to identify key parameters, using surface methodology. The most critical defect type and its severity is particularly discussed
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
Causal Attribution of the Interannual Variability in Flood Peaks Through Bayesian Networks
No full textInternational audienceClassical regression models, due to the limited computational expense and good performance, can be used for the attribution of interannual variability in flood peaks. However, these models capture the relation between predictand (i.e., flood peaks) and predictors (i.e., climate variables), suffering from the disconnect between correlation and causation. Here, we utilize a causal Bayesian Network model to establish causal relationships between flood peaks and basin-and season-averaged precipitation and temperature, which were found to be useful predictors in previous regression-based attribution studies. We develop these models for seasonal flood peaks for 3,884 gauges across the conterminous Unites States, achieving a median Spearman's rank correlation above 0.7. By performing do-calculus intervention on the predictors, we found a strong causal relationship between seasonal maximum daily discharge and both concurrent and lagged season-precipitation and temperature, consistent with underlying physical processes across different basins. The Bayesian Network model effectively predicts the interannual variability in seasonal and annual peak discharges and establishes a causal link between them. The model identifies key drivers across different seasons and regions in CONUS and highlights that antecedent catchment wetness is particularly relevant for high magnitude flows, while precipitation is the dominant driver of medium flows. This study significantly expands our current knowledge on causal flood drivers and presents a novel approach to flood prediction and attribution.</div