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Multiparticle dispersion in rotating-stratified turbulent flows
No full textInternational audienceThe transport of matter by turbulent flows plays an important role, in particular in a geophysical context. Here, we study the relative movement of groups of two (pairs) and four (tetrahedra) Lagrangian particles using direct numerical simulations of the stably stratified Boussinsesq equations, with Brunt-Väisälä frequency N and Coriolis parameter f . We cover regimes close to homogeneous isotropic turbulence, to flows dominated by stratification and rotation, keeping fixed the ratio N / f = 5 . The flows studied are anisotropic, so the relative motion between two particles depends not only on the initial separation between the particles, but also on their orientation with respect to the vertical axis. In all cases considered, we demonstrate that the relative particle motion differs depending on whether dispersion is considered forward or backward in time, although the asymmetry becomes less pronounced when stratification and rotation increase. On the other hand, the strong fluctuations in the dispersion between two particles become more extreme when N and f increase. We also find evidence for the formation of shear layers, which become more pronounced as N and f become larger. Finally, we show that the irreversibility on the dispersion of a set of particles initially forming a regular tetrahedron becomes weaker when the influence of stratification and rotation increases, a property that we relate to that of the perceived rate-of-strain tensor
Physics-Informed Hybrid Modeling of Pneumatic Artificial Muscles
No full textInternational audiencePneumatic Artificial Muscles (PAMs) are complex nonlinear systems characterized by hysteresis, making them challenging to model with classical system identification methods. While deep learning has emerged as a powerful tool for modeling nonlinear systems from data, purely neural network-based models often lack interpretability and are prone to overfitting. To address these challenges, this study explores several hybrid approaches that combine analytical models withneural networks to model PAM behavior more effectively. The results demonstrate that hybrid models significantly outperform both purely analytical and black-box neural network models,particularly in terms of generalization and dynamic accuracy.Among the approaches, the Physics-Informed Neural Network (PINN) unsupervised model shows the most robust performance, capturing complex PAM dynamics while maintaining computational efficiency. These findings suggest that hybrid modeling is a promising and scalable solution for accurately representing the intricate behavior of PAMs
Wear behavior of 316L stainless steel obtained by additive manufacturing (laser metal deposition process)
No full textInternational audienc
Dual functionalization of carboxymethyl cellulose and alginate via Passerini three-component reaction to graft two hydrophobic moieties: Toward modular thin films
No full textInternational audiencePasserini reaction was advantageously exploited to hydrophobize carboxymethyl cellulose (CMC) and alginates (ALG) by employing various hydrophobic aldehydes and isocyanides. The Passerini reaction, carried out in ecofriendly conditions, allowed to design never described twofold hydrophobized polysaccharide derivatives via the covalent grafting of two hydrophobic moieties. The modified CMC and ALG products were in-depth characterized to guaranty the success of the modification and to calculate the degrees of substitution (DS). The impact of experimental parameters and especially the structure of the aliphatic reactants were thoroughly discussed. It appears that high conversions in carboxylic acid up to 70% can be reached. Finally, the Passerini CMC and ALG products were processed as thin films exhibiting modular wettability properties varying from a moderate to a significant hydrophobicity adjustable by the structure of the grafts and the DS values. The film formation of selected CMC and ALG samples was examined by QCM-D experiments completed by AFM analysis under humid environment. It appears that the functionalization i) increases the adsorbed mass by inducing a more packed deposition and ii) closely governs the energy dissipation of the films. This overall approach paves the way toward new bio-based multifunctional films with potential utilizations in coating fields
Identification of Tip-Leakage Vortex Wandering in Large-Eddy Simulation of ECL5/CATANA Transonic Fan Stage
No full textInternational audienceThe tip-leakage flow in fan stages affects aerodynamic efficiency, stability and contributes to broadband noise. Its behavior is difficult to predict, especially in transonic configurations. This study focuses on the ECL5 fan/OGV stage, an open test case from École Centrale de Lyon. At nominal speed, the flow is transonic, with a relative Mach number at the inlet slightly above unity near the tip. A large-eddy simulation (LES) is performed on a periodic angular sector to simulate the unsteady flow phenomena. A first comparison with experimental measurements focuses on the validation of the LES. The analysis of the pressure fluctuation spectra reveals an unexpected low-frequency peak upstream of the rotor blades. A dynamic mode decomposition at the corresponding frequency shows an unsteadiness in the tip-leakage flow. Tracking the tip-leakage flow dynamically using conventional vortex identification methods such as Galilean invariants can be challenging, especially in complex flow conditions. A purely kinetic vortex tracking algorithm is developed and validated to determine the trajectory and size evolution of the tip-leakage vortex. In the mean flow, the vortex is detected up to two rotor chords downstream of the leading edge of the rotor blade. By using frequencyfiltered fields from the unsteady simulation, vortex wandering is investigated. The interaction with the surrounding flow features is outlined
Binaural speech intelligibility for combinations of noise, reverberation, and hearing-aid signal processing
No full textInternational audienceBinaural speech intelligibility in rooms is a complex process that is affected by many factors including room acoustics, hearing loss, and hearing aid (HA) signal processing. Intelligibility is evaluated in this paper for a simulated room combined with a simulated hearing aid. The test conditions comprise three spatial configurations of the speech and noise sources, simulated anechoic and concert hall acoustics, three amounts of multitalker babble interference, the hearing status of the listeners, and three degrees of simulated HA processing provided to compensate for the noise and/or hearing loss. The impact of these factors and their interactions is considered for normal-hearing (NH) and hearing-impaired (HI) listeners for sentence stimuli. Both listener groups showed a significant reduction in intelligibility as the signal-to-noise ratio (SNR) decreased, and showed a reduction in intelligibility in reverberation when compared to anechoic listening. There was no significant improvement in intelligibility for the NH group for the noise suppression algorithm used here, and no significant improvement in intelligibility for the HI group for more advanced HA processing algorithms as opposed to linear amplification in either of the two acoustic spaces or at any of the three SNRs
Instruct-to-SPARQL: A text-to-SPARQL dataset for training Wikidata Agents
No full textInternational audienceThe rapid adoption of Large Language Models (LLMs) for search engines and fact-checking platforms necessitates enhancing their output accuracy. Retrieval Augmented Generation (RAG) mitigates hallucinations but requires semantically rich repositories like Wikidata. However, there is a lack of high-quality data to fine-tune LLMs for querying such knowledge bases. To address this gap, we propose a curated dataset with 2,771 unique queries for fine-tuning LLMs to generate accurate and syntactically valid SPARQL queries from natural language instructions. This dataset, customized for interaction with Wikidata, also serves as a robust benchmark for text-to-SPARQL task evaluation. Key findings show that models generally perform better on queries with lower complexity
Numerical Analysis of a New Corona Ionic Wind Blower Used for Solar Panel Cleaning
No full textInternational audienceThe potential of solar energy as a sustainable power source remains hindered by various efficiency limitations. One of the major challenges is the accumulation of dust on photovoltaic (PV) panels, leading to the decrease of the efficiency and the need for regular cleaning. To address this issue, this work aims to explore the use of ionic wind, generated by corona discharge, as an innovative method for dust removal from PV panels. In line with this objective, this paper presents a numerical analysis of electro-hydrodynamic (EHD) air blowers with wire-to-rectangle configurations to harness the potential of ionic wind for cleaning PV panels. The study utilizes 2D models that have undergone experimental validation. The results highlight a parametric analysis, shedding light on how different geometrical parameters influence the performance of the ionic wind air blower and its energetic efficiency
Nonlinear partial differential equations in neuroscience: from modelling to mathematical theory
No full textMany systems of partial differential equations have been proposed as simplified representations of complex collective behaviours in large networks of neurons. In this survey, we briefly discuss their derivations and then review the mathematical methods developed to handle the unique features of these models, which are often nonlinear and non-local. The first part focuses on parabolic Fokker-Planck equations: the Nonlinear Noisy Leaky Integrate and Fire neuron model, stochastic neural fields in PDE form with applications to grid cells, and rate-based models for decision-making. The second part concerns hyperbolic transport equations, namely the model of the Time Elapsed since the last discharge and the jump-based Leaky Integrate and Fire model. The last part covers some kinetic mesoscopic models, with particular attention to the kinetic Voltage-Conductance model and FitzHugh-Nagumo kinetic Fokker-Planck systems
