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Pierre Magnico. Wall morphology dependence of rare gas Knudsen diffusion in silica and graphite slit nanopore: A molecular dynamics study. Vacuum, 2025, 242, pp.114756. ⟨10.1016/j.vacuum.2025.114756⟩. ⟨hal-05273657⟩ Plus de détails...
Gas/wall collision mechanisms play a key role in Knudsen diffusion process. In particular, the channel wall structure has a major influence in mass transfer. So, we investigate the influence of the wall roughness, anisotropy and porosity on the self-diffusion of helium and neon in nanochannels. Three materials are proposed: graphite and β-cristobalite and amorphous silica. The study makes it possible to analyze, in function of temperature, the correlation between 1/the ballistic/diffusion transition regime of the surface gas transfer, 2/the transition of the bouncing process to a linear increase of the bounce number with time and 3/the shape of the surface residence time distribution characterized by a Fréchet like distribution at short time and an exponential decay at long time. As concerns the amorphous SiO 2 , the bounce must be redefined owing to the transfer inside the material which is dominated by a cage effect. The anisotropy effect on collision process and Knudsen diffusion is analyzed by means of a tensorial computation of the tangential momentum accommodation coefficient and of the mean square displacement. Using the Langevin at the channel scale and the Arya model, the ballistic/diffusion transition time of the mean square displacement is related to the collision frequency and the collision number required for the velocity to be uncorrelated. A stochastic model confirms the molecular dynamics results with β-SiO 2 channel: The behavior of the Knudsen diffusion coefficient according to the Arrhenius law and the influence of collision frequency on transition time.
Pierre Magnico. Wall morphology dependence of rare gas Knudsen diffusion in silica and graphite slit nanopore: A molecular dynamics study. Vacuum, 2025, 242, pp.114756. ⟨10.1016/j.vacuum.2025.114756⟩. ⟨hal-05273657⟩
Stacy Ragueneau, Camille Benard-Pardell, Clémence Cordier, Adeline Lange, Magalie Claeys-Bruno, et al.. Influence of seawater treatment by ultrafiltration and culture conditions on the biochemical composition of the diatom Odontella aurita. Algal Research - Biomass, Biofuels and Bioproducts, 2025, 91, pp.104207. ⟨10.1016/j.algal.2025.104207⟩. ⟨hal-05296601⟩ Plus de détails...
Stacy Ragueneau, Camille Benard-Pardell, Clémence Cordier, Adeline Lange, Magalie Claeys-Bruno, et al.. Influence of seawater treatment by ultrafiltration and culture conditions on the biochemical composition of the diatom Odontella aurita. Algal Research - Biomass, Biofuels and Bioproducts, 2025, 91, pp.104207. ⟨10.1016/j.algal.2025.104207⟩. ⟨hal-05296601⟩
Journal: Algal Research - Biomass, Biofuels and Bioproducts
Transferable adversarial images raise critical security concerns for computer vision systems in real-world, blackbox attack scenarios. Although many transfer attacks have been proposed, existing research lacks a systematic and comprehensive evaluation. In this paper, we systemize transfer attacks into five categories around the general machine learning pipeline and provide the first comprehensive evaluation, with 23 representative attacks against 11 representative defenses, including the recent, transfer-oriented defense and the real-world Google Cloud Vision. In particular, we identify two main problems of existing evaluations: (1) for attack transferability, lack of intra-category analyses with fair hyperparameter settings, and (2) for attack stealthiness, lack of diverse measures. Our evaluation results validate that these problems have indeed caused misleading conclusions and missing points, and addressing them leads to new, consensuschallenging insights, such as (1) an early attack, DI, even outperforms all similar follow-up ones, (2) the state-of-the-art (whitebox) defense, DiffPure, is even vulnerable to (black-box) transfer attacks, and (3) even under the same Lp constraint, different attacks yield dramatically different stealthiness results regarding diverse imperceptibility metrics, finer-grained measures, and a user study. We hope that our analyses will serve as guidance on properly evaluating transferable adversarial images and advance the design of attacks and defenses.
Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre, Laurent Amsaleg, et al.. Revisiting Transferable Adversarial Images: Systemization, Evaluation, and New Insights. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, pp.1-16. ⟨10.1109/TPAMI.2025.3610085⟩. ⟨hal-05267252⟩
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Andres Bustos, D. Zarzoso, Alvaro Cappa, Teresa Estrada, Enrique Ascasibar. AI session leader assistant prototype for the TJ-II device. Plasma Physics and Controlled Fusion, 2025, 67 (9), pp.095014. ⟨10.1088/1361-6587/adfd80⟩. ⟨hal-04856163⟩ Plus de détails...
The advent of artificial intelligence [AI] has a deep impact on numerous scientific and industrial fields, particularly in magnetic confinement fusion. This work explores the application of AI techniques to help scientists with the design of future fusion experiments based on previous experimental campaigns. Traditional ways of interpreting and designing fusion discharges often require extensive computational resources, time, and research experience (including trial and error procedure). By leveraging AI, it is shown the possibility to partially overcome these constraints. As an example, the explored AI techniques are applied to the TJ-II stellarator. The major goal of this work is the development of an AI system that is able to estimate the operation parameters given a desired plasma scenario. The latter will be determined by the magnetic fluctuations measured by a Mirnov coil and the produced operation parameters are the plasma fueling and heating configurations. The results indicate that AI can approximate fusion experiments and assist scientists for the design of new ones, offering a faster and cost-effective alternative to conventional approaches. This study paves the way for more efficient research and development processes in fusion experiments, with AI serving as a tool for innovation and discovery.
Andres Bustos, D. Zarzoso, Alvaro Cappa, Teresa Estrada, Enrique Ascasibar. AI session leader assistant prototype for the TJ-II device. Plasma Physics and Controlled Fusion, 2025, 67 (9), pp.095014. ⟨10.1088/1361-6587/adfd80⟩. ⟨hal-04856163⟩
Homam Betar, Daniele Del Sarto. Microscopic Current Sheets and Fast Tearing Modes in Plasma Turbulence. The Astrophysical Journal, 2025, 990 (1), pp.28. ⟨10.3847/1538-4357/adea47⟩. ⟨hal-05219245v1⟩ Plus de détails...
Since the seminal work by W. H. Matthaeus & S. L. Lamkin, a large amount of evidence has been collected over the years that magnetic reconnection can disrupt current sheets formed in turbulence. The details about how this happens, however, are not clear, yet. The observation of plasmoids suggests that tearing-type modes are involved, but their nature of spontaneous linear instabilities developing on a static (or at most steady) magnetic equilibrium poses strong constraints on their growth rate versus the timescale of the current sheet evolution. None of the tearing-based scenarios, which to date are most credited in literature, seems to fulfill both this constraint and other consistency requirements on the equilibrium profile. In revising them and the main hypotheses, which any tearing-based theory for 2D turbulent reconnection must satisfy, we propose a possible explanation—supported by numerical calculations—for why tearing modes may be relevant. This explanation is grounded on the microscopic thickness that current sheets attain in turbulence, which makes the growth rates of tearing modes large enough for the instability to possibly develop. At the same time, this implies that theoretical growth rates obtained from a boundary layer analysis cannot be applied in this case. We discuss a few implications of these elements in solar wind turbulence and in comparison with alternative models for tearing-based turbulent reconnection that are available in literature.
Homam Betar, Daniele Del Sarto. Microscopic Current Sheets and Fast Tearing Modes in Plasma Turbulence. The Astrophysical Journal, 2025, 990 (1), pp.28. ⟨10.3847/1538-4357/adea47⟩. ⟨hal-05219245v1⟩
Jingqi Zhang, Mitra Fouladirad, Nikolaos Limnios, Pierre Magnico. Stochastic modeling of movement for Helium particles in a graphite channel. Physica A: Statistical Mechanics and its Applications, 2025, 675, pp.130818. ⟨10.1016/j.physa.2025.130818⟩. ⟨hal-05246509⟩ Plus de détails...
In this article, we present a stochastic model for the movement of Helium particles within a graphite channel, focusing on Knudsen diffusion. We develop a semi-Markov model to describe the movement of the particle, derive the stationary distribution of its mean position, and analyze the model's asymptotic properties. To validate the model, we compare its theoretical outcomes with Monte Carlo simulations. As temperature significantly influences on the movement of particles, two situations are studied for high and low temperature. In both cases, theoretical and simulation results by Monte Carlo coincide. Furthermore, we propose estimation methods for the local parameters of the model and demonstrate its application using data from Molecular Dynamics simulations.
Jingqi Zhang, Mitra Fouladirad, Nikolaos Limnios, Pierre Magnico. Stochastic modeling of movement for Helium particles in a graphite channel. Physica A: Statistical Mechanics and its Applications, 2025, 675, pp.130818. ⟨10.1016/j.physa.2025.130818⟩. ⟨hal-05246509⟩
Journal: Physica A: Statistical Mechanics and its Applications
Adrien Magne, Emilie Carretier, Lilivet Ubiera Ruiz, Thomas Clair, Morgane Le Hir, et al.. Membrane separation between homogeneous palladium-based catalysts and industrial active pharmaceutical ingredients from a complex organic solvent matrix: First approach using ceramic membranes. Separation and Purification Technology, 2025, 359, pp.130442. ⟨10.1016/j.seppur.2024.130442⟩. ⟨hal-05042327⟩ Plus de détails...
Palladium-based homogeneous catalysts are indispensable in the pharmaceutical field due to the high reaction yields and high selectivity they can reach. However, they are toxic and sensitive to oxidation. Isolating these complexes from pharmaceutical molecules at the end of the synthesis without degrading both compounds could therefore lead to major environmental and economic gains. This study focuses on the separation between a real pharmaceutical intermediate at around 600 g mol -1 and a palladium catalyst PdCl 2 (PPh 3 ) 2 at 701.9 g mol -1 using ceramic membranes in organic solvent phase. For improving this separation, substitute catalysts with higher molecular weights and/or higher steric hindrances had been selected, and MWCO of 1000, 750, and 250 Da had been evaluated. The interest of catalyst enlargement had been confirmed with Pd retentions from 13 % (reference) to 18 % (heavier complex), but this approach was limited by economic aspect which restricted the choice of potential substitutes. Nanofiltration membranes with lower cut-off points have led to slightly higher retentions, but membrane characterization concluded that experimental MWCO were similar between all membranes, therefore raising questions about the definition of MWCO for different manufacturers.
Adrien Magne, Emilie Carretier, Lilivet Ubiera Ruiz, Thomas Clair, Morgane Le Hir, et al.. Membrane separation between homogeneous palladium-based catalysts and industrial active pharmaceutical ingredients from a complex organic solvent matrix: First approach using ceramic membranes. Separation and Purification Technology, 2025, 359, pp.130442. ⟨10.1016/j.seppur.2024.130442⟩. ⟨hal-05042327⟩
Teddy Gresse, Julie Soriano, Auline Rodler, Jean-Claude Krapez, Jean Pierro, et al.. Qualification of microclimate models and simulation tools: An academic benchmark. Building and Environment, 2025, 278, pp.112913. ⟨10.1016/j.buildenv.2025.112913⟩. ⟨hal-05073994⟩ Plus de détails...
In recent decades, numerous urban microclimate models have been developed to address various applications, such as diagnosing urban overheating and evaluating heat mitigation strategies using green or grey solutions. These models account for complex physical interactions; however, their qualification and validation remain significant challenges due to their complexity and the lack of a standardized framework and comprehensive reference datasets. This paper presents the first step of a comprehensive qualification and validation methodology through the definition of an academic benchmark and its application to four urban microclimate models. The proposed methodology follows an incremental phenomenological approach, systematically analysing heat transfer processes within an idealized street canyon with well-defined conditions across four cases: shortwave radiation, longwave radiation, aeraulics, and their coupling with heat conduction and storage in walls and ground. The benchmark aims to analyse the behaviour of different microclimate models, quantify deviations between simulation results, and identify their underlying sources within the physical models. This is achieved through the intercomparison of simulation results, incorporating reference data with a known standard deviation where available. The results show good agreement between models for solar radiation, infrared radiation, and heat conduction but reveal significant deviations in surface convection, stressing the need for further research into convection modelling and its influence on coupled processes. Additionally, the results confirm the suitability of the proposed methodology in identifying the sources of deviations between models. This benchmark provides a robust framework for model qualification and is expected to be widely adopted in future studies.
Teddy Gresse, Julie Soriano, Auline Rodler, Jean-Claude Krapez, Jean Pierro, et al.. Qualification of microclimate models and simulation tools: An academic benchmark. Building and Environment, 2025, 278, pp.112913. ⟨10.1016/j.buildenv.2025.112913⟩. ⟨hal-05073994⟩
Jinhua Lu, Song Zhao, Pierre Boivin. A lattice-Boltzmann inspired finite volume solver for compressible flows. Computers and Mathematics with Applications, 2025, 187, pp.50-71. ⟨10.1016/j.camwa.2025.03.007⟩. ⟨hal-05086335v1⟩ Plus de détails...
The lattice Boltzmann method (LBM) for compressible flow is characterized by good numerical stability and low dissipation, while the conventional finite volume solvers have intrinsic conversation and flexibility in using unstructured meshes for complex geometries. This paper proposes a strategy to combine the advantages of the two kinds of solvers by designing a finite volume solver to mimic the LBM algorithm. It assumes an ideal LBM that can recover all desired higher-order moments. Time-discretized moment equations with second-order temporal accuracy and physically consistent dissipation terms are derived from the ideal LBM. By solving the recovered moment equations, a finite volume solver that can be applied to nonuniform meshes naturally, enabling body-fitted mass-conserving simulations, is proposed. Numerical tests show that the proposed solver can achieve good numerical stability from subsonic to hypersonic flows, and low dissipation for a long-distance entropy spot convection. For the challenging direct simulations of acoustic waves, its dissipation can be significantly reduced compared with the Lax-Wendroff solver of the same second-order spatial and temporal accuracy, while only remaining higher than that of the LBM on coarse meshes. The analysis implies that approximations of third-order temporal accuracy are required to recover the low dissipation of LBM further.
Jinhua Lu, Song Zhao, Pierre Boivin. A lattice-Boltzmann inspired finite volume solver for compressible flows. Computers and Mathematics with Applications, 2025, 187, pp.50-71. ⟨10.1016/j.camwa.2025.03.007⟩. ⟨hal-05086335v1⟩
Journal: Computers and Mathematics with Applications
B Clavier, D Zarzoso, D Del-Castillo-Negrete, E Frénod. A Generative Artificial Intelligence framework for long-time plasma turbulence simulations. Physics of Plasmas, In press, ⟨10.1063/5.0255386⟩. ⟨hal-05085168⟩ Plus de détails...
Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatio-temporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa-Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatio-temporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo-Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and in the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyper-parameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of AI-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.
B Clavier, D Zarzoso, D Del-Castillo-Negrete, E Frénod. A Generative Artificial Intelligence framework for long-time plasma turbulence simulations. Physics of Plasmas, In press, ⟨10.1063/5.0255386⟩. ⟨hal-05085168⟩
