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DEMONSTRATIVE SET-UP FOR PROPULSION INDUCED BY HIGH POWER PULSED LASER IRRADIATION
No full textInternational audienc
2D dendritic thermal growth pulsations: diffusion field associated with the transport of heat for application in organic-based systems
No full textInternational audienceAbstract The crystallization of organic-based materials occurs under conditions far from equilibrium, leading to patterns that grow as propagating waves into the surrounding unstable fluid medium. This problem was reformulated considering the concept of thermal forces of preexisting 2D patterns. A new finite difference numerical scheme was tested. Dendritic islands were distributed in the liquid-viscous fluid. It induced a heterogeneous thermal distribution nearby the dendritic islands. The evolution of the isoline of the temperature versus time reveals fractal patterns within the inter-dendritic liquid. The physical origin of the fluctuations is guided by the proximity of the dendrites, i.e. lack of space. Moreover, the gradient of temperature is depicted by isocline pictures. Fractal patterns may be considered advantageous and be taken as a key point to the next step, i.e. the crystal growth in an environment subjected to rapid variations of temperature
Global outlook for the nuclear industry: the role of advanced nuclear technologies in the energy transition
No full textInternational audienc
A generic substation heating power forecasting approach using machine learning
No full textInternational audienceTo improve the energy performance and decarbonize the district heating (DH) systems, optimal control of systems is essential. A better energetic control of DH networks requires the forecast of the heating demand in substations. Among Machine Learning (ML) methods, regression techniques have found especially useful in short-term heat demand forecasting for DHNs, identifying patterns that assist in optimizing energy distribution.This study aims to provide a robust and generic approach to build statistical predictive models applicable to diverse building types. First, the selection of features is analyzed, and then various machine learning models are explored such as Artificial Neural Networks (ANNs), Linear Regression (LR), Ridge Regression (RR), Support Vector Regression (SVR), and Extremely Randomized Trees Regression (ETR). The approach is based on the analysis of 10 substations (SSTs) in 3 diPerent DHNs. Initially 13 features are used to forecast the heating power. The diPerent models are compared using MeanAbsolute Percentage Error (MAPE) and R2 values. In addition, this study investigated the influence of threshold values on model accuracy, finding minimal impact, and examined how reducing data availability aPected predictions.The performance metrics underscore the ANN and ETR’s abilities to capture complex, non-linear heating power demand patterns, particularly in standard building categories like residential and tertiary buildings with predictable heating demands. While large datasets over several years are typically required for accurate forecasting, more recent advancements in ML and deep learning have shown promise in achieving similar accuracy with less data. On the opposite, the irregular patterns of certain SSTs highlighted the need for further features and additional data to enhance predictive accuracy. Feature selection and data availability played critical roles in model performance specifically in these SSTs. While tertiary substations demonstrated resilience to reduced datasets, the swimming pool substation's performance notably deteriorated, emphasizing the importance of comprehensive datasets and robust feature engineerin
Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models
No full textGrain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size distributions during grain growth. Unlike full-field simulations, which are computationally demanding, the present work relies on mean-field statistical descriptors extracted from high-fidelity simulations. A dataset of 120 grain growth sequences was processed into normalized grain size distributions as a function of time. The models were trained to predict future distributions from a short temporal history using a recursive forecasting strategy. Among the tested models, the LSTM network achieved the highest accuracy (above 90\%) and the most stable performance, maintaining physically consistent predictions over extended horizons while reducing computation time from about 20 minutes per sequence to only a few seconds, whereas the other architectures tended to diverge when forecasting further in time. These results highlight the potential of low-dimensional descriptors and LSTM-based forecasting for efficient and accurate microstructure prediction, with direct implications for digital twin development and process optimization
Oedometric compression, or not, that is the question: experimental insights from in situ microtomography
No full textNational audienc
STARCH AND STARCH-HYDROXYAPATITE AEROGELS AND XEROGELS : TOWARDS BIO-BASED SUBSTITUTES FOR CANCELLOUS BONES
No full textInternational audienc
COMPOSITE HYDROGELS FOR DUAL DRUG DELIVERY
No full textInternational audienceIntroductionBiopolymers such as proteins and polysaccharides are widely used in scaffold design for biomedical applications due to their biocompatibility and ability to mimic the extracellular matrix. Hydrogels made of these polymers, are well-suited for the encapsulation of therapeutic agents, making them especially attractive for localized and sustained drug delivery. This approach offers significant potential in the treatment of cancer, chronic inflammation, and infections. However, conventional hydrogels often face challenges in simultaneously delivering both hydrophilic and hydrophobic drugs, despite the therapeutic interest in dual drug delivery strategies aimed at enhancing efficacy through synergistic effects. Composite hydrogels, consisting of a polymeric matrix combined with drug-loaded particles, represent a promising strategy to overcome these limitations. The objective of this work is to investigate how formulation parameters influence the structural and release properties of gelatin-dextran hydrogels, as a step toward the development of more sophisticated composite systems for dual drug delivery applications.Materials and methodsHydrogels were prepared by crosslinking gelatin and aldehyde-functionalized dextran via Schiff base formation in aqueous solution. Various formulations were prepared by adjusting the polymer concentrations to evaluate their effects on gelation time, swelling behavior, and drug release kinetics. Methylene blue was used as a model hydrophilic drug to study the release profile.Results and discussionPreliminary results show that increasing polymer concentrations significantly reduced gelation time and swelling degree, indicating a higher crosslinking density, which typically influences drug release kinetics. However, no notable differences in the release profile of methylene blue were observed across formulations, likely due to its small molecular size and high diffusivity in aqueous environments. These findings suggest that the release of small hydrophilic molecules may be less sensitive to variations in matrix structure.Conclusions and perspectivesThis study highlights the influence of formulation parameters on the structural and release properties of gelatin-dextran hydrogels, contributing to the development of advanced composite systems. The use of biopolymers such as gelatin and dextran offer key advantages for biomedical applications, including inherent biocompatibility, biodegradability, and origin from renewable resources. The next phase of this research will focus on investigating the release behavior of larger biomolecules, using fluorescent BSA as a model protein. This approach is particularly relevant due to the growing interest in therapeutic proteins for the treatment of complex diseases such as cancer, inflammation, and wound healing. Furthermore, the integration of PLGA microparticles will be explored to create composite hydrogels capable of dual drug delivery, simultaneously releasing both hydrophilic and hydrophobic agents, such as curcumin and ibuprofen