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Optimizing Thermal Design of 75kW LLC Resonant Converters: Leveraging Variable Inductance for Electric Vehicle Converter Applications by Finite Element Analysis
No full textInternational audienceThis paper presents a comprehensive analysis of a 75kW LLC resonant converter, focusing on magnetic design and efficiency. The work introduces a novel method for predicting temperature profiles, reducing the need for extensive testing, and saving time and costs
Nanoparticle growth control in One-Step In-Situ Synthesis of Iron Oxide-Polyisobutylene Nanocomposites
No full textInternational audienceLarge-scale manufacturing processes of polymer nanocomposites often involve the incorporation of pre-synthesized nanoparticles (in solution or powder) into a molten or pre-dissolved solution of the polymer matrix. The synthesis of IONPs can be achieved through the thermal decomposition of organometallic precursors, a method known for its ability to produce monodisperse nanocrystals with precise size control 1,2. In this process, the iron oleate precursor was dissolved and decomposed in a high-boiling-point organic solvent using oleic acid as a surfactant. The solution was then heated to temperatures above 250°C and maintained for at least 30 min, during which IONPs were formed. The process of nanoparticle formation through this bottom-up approach is primarily understood using the LaMer diagram 3, which consists of three key phases: supersaturation, nucleation, and growth Initially, the iron oleate precursors were dissolved in a heated organic solvent under reflux, leading to iron supersaturation. This is followed by nucleation, in which small nanoparticle clusters are formed and stabilized by a surfactant such as oleic acid. Finally, in the growth phase, these clustersincrease in size as more precursor material is deposited onto them, until the precursor concentration drops below the nucleation threshold. Various factors influence this growth process, including precursor concentration, solution temperature, surfactant ratio, and reaction time4,5. Once the nanoparticles were formed, the solution containing them was mixed with the polymer to create a nanocomposite material. This process aims to incorporate nanoparticles uniformly throughout the polymer matrix. However, achieving homogeneous dispersion of nanoparticles within a polymer is not always guaranteed. Indeed, it can be influenced by various factors, including the affinity between the coated nanoparticles and the polymer, mixing method employed, and processing conditions. We recently readapted the conventional nanoparticle synthesis method to create PIB-iron oxide nanocomposites by directly dissolving the iron oleate precursor in a polymer solution instead of only an organic solvent. To control the nanoparticle size and distribution, we varied two key parameters: the PIB dilution ratio and molecular weight of PIB. By varying these two parameters, we highlight how the size of the nanoparticles depends on these factors and how they are dispersed within the PIB matrix.Références1. Meftah, S. et al. Synthesis and Magnetic Properties of Spherical Maghemite Nanoparticles with Tunable Size and SurfaceChemistry. Langmuir (2024) doi:10.1021/acs.langmuir.4c02495.2. Park, J. et al. Ultra-large-scale syntheses of monodisperse nanocrystals. Nature Mater 3, 891–895 (2004).3. LaMer, V. K. & Dinegar, R. H. Theory, Production and Mechanism of Formation of Monodispersed Hydrosols. J. Am.Chem. Soc. 72, 4847–4854 (1950).4. Meftah, S. et al. Synthesis and magnetic properties of spherical maghemite nanoparticles with tunable size and surfacechemistry. Accepted Langmuir, ACS (2024).5. Meftah, S. et al. Striking effect of the iron stearate purity on the shape and size of maghemite nanoparticles. Colloidsand Surfaces A: Physicochemical and Engineering Aspects 680, 132689 (2024
Context-aware and Reliable Long-term Decision-Making for Safe Intelligent Vehicles: A survey
No full textInternational audienceIn complex environments, decision-making for an Intelligent Vehicle (IV) requires reliability to ensure safe and efficient navigation. During vehicle operation, context and vehicle's capabilities influence the feasibility of its possible decisions and actions functions. Context-awareness allows to enhance the reliability of the decision-making process, ensuring that the vehicle has the capabilities to effectively perform the desired action. To warrant the safe operation of a vehicle, the Operational Design Domain (ODD) concept has been introduced. In the literature, it defines the conditions under which the vehicle is designed to operate safely. In this survey the ODD concept serves as a formalism to describe the context according to an established taxonomy. This survey focuses on how the operational context and the vehicle's capabilities determine the manner of how decisions are taken to ensure driving safety, comfort, and reliability. This is a multidimensional problem as the vehicle's capabilities, the road, the road users, and other elements of the context need to be considered. The different approaches and methods used in decision-making for IVs which take into account contextual information are identified as well as the research gaps that still need to be addressed in order to ensure reliable decision-making. Further, recent approaches that consider the ODD framework are presented to highlight the importance of this formalism. Conclusions underscore the importance of this integration for IVs and offer key insights for future research, emphasizing the crucial synergy between reliable long-term decision-making and the ODD as a contextual-awareness formalism
On exploring age difference using HD-sEMG signals during STS exercise
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Rapid fatigue limit estimation of smart polymer-matrix composite under self-heating bending tests using an innovative automatic approach: Knee method
No full textInternational audienceIn recent years, Polymer-Matrix Composites (PMCs) have gained increasing attention across various sectors. With this growing interest and usage, accurately determining their mechanical properties, including the fatigue properties, has become crucial. Traditional methods for these evaluations are both time-consuming and costly, prompting the development of easier and more cost-effective methods for rapidly estimating the fatigue limitsof materials. Among these methods, the self-heating test has emerged as notable. The first innovation of this study lies in determining the fatigue limit through the capacitance measurements of in-situ piezoceramic transducers during the four-point self-heating bending test. This determination was validated using the classical temperature measurement methods. Additionally, a novel method called the ‘‘knee method’’ was developpedand employed, representing the second originality of this study, and it has shown very promising results
Inductive, Capacitive and Resistive Aspects Modeling of Planar Windings Applied to a 1 MHz LLC Converter
No full textInternational audiencePlanar transformers and inductors are subject to parasitic capacitances that can alter the operation of the converter using them. This article integrates capacitive aspects into conventional inductive and resistive modeling developed by Dowell [1]. This leads to a relatively simple software program that enables fast simulation of planar components' frequency behavior, predicts conductors' current distribution, and deducts the resulting losses. Finally, an application example is proposed consisting of optimizing the design of a transformer by adjusting its windings, the nature of its materials, and the position of an air gap, to minimize current oscillations in the converter that could result from excessive parasitic capacitors
Comparison of the electrical and thermal method for determining the power losses of a QDPAK-MOSFET
No full textInternational audienceDue to increasingly higher switching speeds, the electrical determination of power losses in power electronics is more challenging. In this paper, a double-pulse test is performed to determine the switching losses of a MOSFET. Additionally, it presents an approach for the thermal determination of power losses in semiconductors, which significantly reduces measurement duration from minutes to seconds. The accuracy of the thermal method is demonstrated, with a deviation of 0.16% between the measurement results and those obtained with a power analyzer. Beyond its high accuracy, the thermal method offers high resolution, enabling the detection of even the smallest differences in power losses. Furthermore, a comparison between electrical and thermal power loss determination is provided
Comparison of experimental and simulated behaviour of Solid-liquid expression using predictive model
No full textInternational audienceThis study compares the experimental and simulated behaviour of solid–liquid expression using a nonlinear predictive model. The model is based on power-law constitutive equations for the local filter cake parameters (permeability, specific resistance, compressibility modulus, and consolidation coefficient), and describes the compressive pressure distributions and consolidation ratio under different pressures. Experimental data from kaolin and bentonite suspensions are compared to model simulations. The experiments involve the formation of semi-solid and pre-compressed by filtration cakes, which are then subjected to solid–liquid expression. The study highlights the influence of initial cake structure on consolidation behaviour and evaluates the applicability of conventional filtration-consolidation theory to non-uniform cakes formed by filtration. The results demonstrate that the predictive model generally aligns with the experimental data, although deviations may occur due to unmodeled effects and experimental inaccuracies
AI and Digital Humanities in the Arabian Gulf: Interdisciplinary Perspectives on Infrastructure, Cultural Heritage, and Community Building: Towards Inclusive and Ethical AI for Cultural Heritage in the Gulf Region
No full textInternational audienceThis article examines the integration of artificial intelligence (AI) in digital humanities and cultural heritage preservation across the Arabian Gulf region. It highlights the ethical, legal, and community-centered challenges raised by AI in archives, museums, and libraries, while showcasing local initiatives that adopt inclusive and culturally grounded approaches. The paper calls for an interdisciplinary governance of AI, anchored in shared infrastructures, context-sensitive regulation, and active community participation.Ce texte explore les usages de l’intelligence artificielle (IA) dans les humanités numériques et la préservation du patrimoine culturel dans la région du Golfe arabo-persique. Il met en lumière les enjeux éthiques, juridiques et communautaires soulevés par l’usage de l’IA dans les archives, les musées, et les bibliothèques, tout en soulignant les initiatives locales qui intègrent des approches inclusives et respectueuses des contextes culturels. L’article plaide pour une gouvernance interdisciplinaire de l’IA, ancrée dans des infrastructures partagées, une régulation contextualisée et la participation active des communautés concernées
Skew-probabilistic neural networks for learning from imbalanced data
No full textInternational audienceReal-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown a bias towards the majority class, impeding accurate predictions for the minority class. This paper introduces an imbalanced data-oriented classifier using probabilistic neural networks (PNN) with a skew-normal kernel function to address this major challenge. PNN is known for providing probabilistic outputs, enabling quantification of prediction confidence, interpretability, and the ability to handle limited data. By leveraging the skew-normal distribution, which offers increased flexibility, particularly for imbalanced and non-symmetric data, our proposed Skew-Probabilistic Neural Networks (SkewPNN) can better represent underlying class densities. Hyperparameter fine-tuning is imperative to optimize the performance of the proposed approach on imbalanced datasets. To this end, we employ a population-based heuristic algorithm, the Bat optimization algorithm, to explore the hyperparameter space effectively. We also prove the statistical consistency of the density estimates, suggesting that the true distribution will be approached smoothly as the sample size increases. Theoretical analysis of the computational complexity of the proposed SkewPNN and BA-SkewPNN is also provided. Numerical simulations have been conducted on different synthetic datasets, comparing various benchmark-imbalanced learners. Real-data analysis on several datasets shows that SkewPNN and BA-SkewPNN substantially outperform most state-of-the-art machine-learning methods for both balanced and imbalanced datasets (binary and multi-class categories) in most experimental settings