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Dislocation evolution and redistribution during stress relaxation in single crystal AD730TM after prior plastic strain at 700 °C
No full textInternational audienceUnderstanding dislocation evolution during viscoplastic deformation is essential to accurately predict high-temperature superalloy behavior. While creep and monotonic deformation are well studied, the evolution of the dislocation substructures during stress relaxation, particularly following prior plastic straining, remains unclear. This study investigates a single crystal γ/γ' AD730TM superalloy deformed at 700 °C, focusing on dislocation evolution and redistribution. Transmission electron microscopy reveals that stacking faults formed during plastic straining progressively disappear during relaxation, suggesting thermally activated mechanisms such as dislocation re-association and recovery. After re-association, perfect dislocations spread into the matrix, forming a homogeneous dislocation network. These microstructural evolutions are believed to underlie the macroscopic stress relaxation behavior, which exhibits two regimes in the Norton diagram: an initial high-stress exponent regime linked to rapid dislocation rearrangement, followed by a low-stress exponent regime associated with broader dislocation activity
Bridging Design Science research and formal design theories: leveraging C-K theory for impactful research
No full textInternational audienceThis paper proposes a novel integration of formal design theories-specifically C-K theory-into design science research (DSR) to better address complex and ill-defined problems. Traditional DSR methods often assume well-framed problem spaces, limiting their generative potential in tackling grand societal challenges. By embedding C-K theory into DSR, we outline a methodology that enhances generativity through iterative co-expansion of concept and knowledge spaces. An empirical case in the healthcare sector illustrates this approach, showing how C-K theory fostered the discovery of anomalies, expanded the knowledge base, and led to new artefacts and business model innovations. Our findings demonstrate the relevance of formal design theories in supporting both knowledge exploration and generative artefact development. We conclude that such integration enables more generative, reflexive, and impactful research processes.</div
Extension of Sequence of Physical Processes framework relating the second Piola-Kirchhoff stress tensor to the Green-Lagrange strain tensor
No full textAn extension of the Sequence of Physical Processes using geometrical corrections of the Piola-Kirchhoff stress tensor and the Green-Lagrange strain tensor is addressed. More precisely, the usual Sequence of Physical Processes omits some geometrical non linearities that appear when the deformation becomes large. With this extension, geometrical corrections are added and let the opportunity to study rheological non linearities. Application on two famous classical viscoelastic models, namely the linear Maxwell model and the linear Kelvin-Voigt model, helps to understand how some complex behaviours may be rationalised to better understand the behaviours after some corrections
Rapport de prospections thématiques : Evolution environnementale des vallées affluentes de la Seine
No full textReconstructing multi-decadal daily river water temperature in the Seine RiverBasin (France) with a bidirectional LSTM and basin-location embeddings
No full textInternational audienceLong-term hydrological time series are essential for planning effective water-resource management strategies that balance competing water and energy uses and preserve ecosystem functioning. In particular, long-term large-scale surface water temperature (SWT) time series are crucial for enhancing understanding of climate change impacts and for quantifying uncertainties in the occurrence of critical periods affecting water and energy uses, as well as ecosystem balance. However, these datasets inevitably contain missing observations, and long-term data series with large spatial coverage remain scarce. Modeling approaches provide valuable tools for estimating surface water temperature dynamics when observations are missing. Owing to their low data requirements and fast computation times, statistically based approaches are well suited to large spatial scales, where physically based approaches often become impractical to apply. Among statistically based methods, recurrent neural networks, such as Long Short-Term Memory (LSTM) models, have recently shown considerable potential for time series imputation (Cao et al., 2018 https://doi.org/10.48550/arXiv.1805.10572; Che et al., 2018 https://doi.org/10.1038/s41598-018-24271-9) and for simulating hydrological variables, including SWT (e.g. Saadi et al., 2025 https://doi.org/10.5194/egusphere-2025-3393). The aim of the present work was to develop and assess an approach for reconstructing long-term SWT time series at the scale of a large river basin using an LSTM model. The study was conducted at the scale of the Seine River Basin, including nearly 80 monitoring stations providing daily SWT observations, and relied on continuous meteorological data from 1958 to 2025 derived from the SAFRAN system (Vidal et al., 2010 10.1002/joc.2003). The developed model was designed to simulate a one-year daily SWT sequence, considering both dynamic and static inputs. Dynamic inputs include one-year sequences of meteorological data and the daily SWT time series to be reconstructed, as well as masks used to identify missing values in the SWT input (Quian et al., 2024 arXiv:2405.17508v1). Static inputs include features characterizing the monitoring stations, such as hydrological (mean and low-flow discharges), geographical and meteorological features. The model architecture is composed of two sequential modules: (i) a bidirectional LSTM that encodes basin-scale temporal dynamics from dynamic inputs, and (ii) a multilayer perceptron that combines the LSTM’s final hidden states with a learned embedding representing the target monitoring station to generate the full annual SWT sequence. This approach enables the reconstruction of daily SWT across the basin over multiple decades, handling a wide range of missing-data situations - from sporadic gaps to entirely missing time series - by leveraging covariates and influential drivers, primarily meteorological factors
LmPT: Conditional Point Transformer for Anatomical Landmark Detection on 3D Point Clouds
No full textInternational audienceThis paper has been accepted at International Symposium on Biomedical Imaging (ISBI) 202
CO₂ Injection in Opalinus Clay at the Mont Terri CL-Experiment: Insights from Laboratory Experiments and Hydraulic-Geochemical Coupled Modeling
No full textInternational audienceCarbon capture and storage (CCS) projects raise fundamental questions beyond technical performance, including how injected CO₂ behaves in the subsurface over long-time scales, how reliable model predictions are, and how experimental observations and simulations can be meaningfully combined. Addressing these questions requires not only process-based physical understanding, but also transparent modeling workflows, experimental validation, and effective collaboration across disciplines and institutions. In this contribution, we use the ongoing CO₂ Long-term Periodic Injection Experiment (CL-Experiment) at the Mont Terri Rock Laboratory in Switzerland as a central case study to illustrate how such integrated understanding can be developed. The core of the work is a numerical benchmark modeling study of CO₂ injection into the Opalinus Clay formation, using a two-dimensional axisymmetric representation of the injection system to investigate hydraulic propagation and coupled geochemical processes over a 20-year period. The simulations assume a fully water-saturated domain and single-phase injection at 3 MPa, using artificial porewater containing dissolved CO₂ corresponding to a partial pressure of 2 MPa. As part of a benchmark study, international teams use different numerical codes. Evaluation of the results enables a transparent assessment of model assumptions, sensitivities, and limitations, as well as model verification.To gain insights into CO₂–water–rock interactions, laboratory experiments were conducted using crushed Opalinus Clay from the in-situ sandy facies field site in an open system under controlled CO₂ conditions. Differences and consistencies between laboratory observations and numerical simulations are explicitly examined, highlighting key parameters and controlling processes that influence both model behavior and experimental responses.This study integrates numerical benchmarking, laboratory experiments, and interdisciplinary collaboration as a learning process to improve understanding of CO₂ storage in clay formations. Continuum-scale modeling shows that the CO₂ plume remains confined within approximately 1 m of the injection zone over 20 years (based on a cutoff concentration of 10 mmol/L), while CO₂-induced carbonate dissolution causes localized porosity increases within about 5 cm of the injection zone. At the laboratory scale, modeling indicates that carbonate reactions are the dominant factor on the pH evolution. However, strong spatial mineralogical heterogeneity observed in the in-situ samples limits the applicability of homogeneous batch-scale representations. For the international benchmark exercise, effective coordination relied on a hierarchical benchmarking strategy in which model complexity was increased stepwise by progressively introducing key variables and parameters. Together, the results of this study demonstrate the strength of coordinated benchmarking initiatives, and continuous exchange across disciplines, tools, and teams
