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Modélisation semi-analytique et numérique de la conduction thermique au sein d’un transistor MOSFET MOSFET晶体管内导热的半解析和数值模拟
Entropie = Pub Date : 2023-01-01 DOI: 10.21494/iste.op.2023.1016
Ali El Arabi, Nicolas Blet, Benjamin Rémy, Denis Maillet
{"title":"Modélisation semi-analytique et numérique de la conduction thermique au sein d’un transistor MOSFET","authors":"Ali El Arabi, Nicolas Blet, Benjamin Rémy, Denis Maillet","doi":"10.21494/iste.op.2023.1016","DOIUrl":"https://doi.org/10.21494/iste.op.2023.1016","url":null,"abstract":"Résumé : Un modèle thermique semi-analytique d’un transistor MOSFET en régime instationnaire est présenté. Il permet le calcul de la température de la face supérieure du composant à partir de celle sur la face inférieure et du flux de chaleur sur la face supérieure. La méthode des quadripôles thermiques est employée et une conversion de spectre est utilisée pour gérer les interfaces entre les différentes couches du composant. Pour une géométrie bidimensionnelle, la comparaison des résultats du modèle semi-analytique à des résultats numériques (sous COMSOL Multiphysics) montre un écart maximal inférieur à 0.1 K et permet une inter-validation des modèles","PeriodicalId":483187,"journal":{"name":"Entropie =","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Caractérisation et performances thermiques des fibres de Furcraea Foetida et de broyat d’ananas en tant que matériau isolant en vrac Furcraea Foetida纤维和菠萝碎纤维作为散装绝缘材料的热特性和性能
Entropie = Pub Date : 2023-01-01 DOI: 10.21494/iste.op.2023.1017
Hélène Caillet, Julie Bascaules, Youssoufou Mahaman, Patrick Rousset, Jean-François Martin, Laetitia Adelard, Olivier Marc
{"title":"Caractérisation et performances thermiques des fibres de Furcraea Foetida et de broyat d’ananas en tant que matériau isolant en vrac","authors":"Hélène Caillet, Julie Bascaules, Youssoufou Mahaman, Patrick Rousset, Jean-François Martin, Laetitia Adelard, Olivier Marc","doi":"10.21494/iste.op.2023.1017","DOIUrl":"https://doi.org/10.21494/iste.op.2023.1017","url":null,"abstract":"RÉSUMÉ. A La Réunion, les déchets végétaux issus de la culture de l’ananas ainsi que le Furcraea Foetida (peste végétale aussi connue sous le nom de choka) ne sont actuellement pas valorisés. Dans le domaine de la construction, les isolants thermiques non biosourcés génèrent des impacts environnementaux importants, bien qu’ils soient essentiels pour la réduction des consommations énergétiques des bâtiments. Nous nous intéressons dans cette étude à la valorisation de ces déchets via l’extraction des fibres pour l’élaboration d’isolants thermiques en vrac. Les objectifs","PeriodicalId":483187,"journal":{"name":"Entropie =","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Earth-Air Heat Exchangers (EAHE): Energetic and Exergetic Analysis 地球-空气热交换器(EAHE):能量与火用分析
Entropie = Pub Date : 2023-01-01 DOI: 10.21494/iste.op.2023.1014
Wael Zeitoun, Jian Lin, Monica Siroux
{"title":"Earth-Air Heat Exchangers (EAHE): Energetic and Exergetic Analysis","authors":"Wael Zeitoun, Jian Lin, Monica Siroux","doi":"10.21494/iste.op.2023.1014","DOIUrl":"https://doi.org/10.21494/iste.op.2023.1014","url":null,"abstract":"- EAHE is an air-soil exchanger buried under the ground that permits the use of shallow ground temperatures to decrease building’s heating and cooling demands. Exergy analysis, which results from combining both the first and second law of thermodynamics, helps to analyze the performance of the EAHE at its reversible limit and to estimate the departure from this limit. An exergetic analysis will be carried out on the experimental EAHE installed at Illkirch-Graffenstaden campus of University of Strasbourg. The objective is to assess the system and identify the parts that dissipates energy the most to optimize the system. The experimental EAHE and the measurements taken are presented in the analysis and finally the derived results are analyzed","PeriodicalId":483187,"journal":{"name":"Entropie =","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A priori reconstruction of Thermal-Large Eddy Simulation (T-LES) by Deep Learning Reconstruction a priori de champs de Simulations des Grandes Echelles Thermiques par Apprentissage Profond 基于深度学习的热大涡模拟(T-LES)先验重建
Entropie = Pub Date : 2023-01-01 DOI: 10.21494/iste.op.2023.1015
Yanis Zatout, Adrien Toutant, Onofrio Semeraro, Lionel Mathelin, Françoise Bataille
{"title":"A priori reconstruction of Thermal-Large Eddy Simulation (T-LES) by Deep Learning Reconstruction a priori de champs de Simulations des Grandes Echelles Thermiques par Apprentissage Profond","authors":"Yanis Zatout, Adrien Toutant, Onofrio Semeraro, Lionel Mathelin, Françoise Bataille","doi":"10.21494/iste.op.2023.1015","DOIUrl":"https://doi.org/10.21494/iste.op.2023.1015","url":null,"abstract":". In this paper, we examine a machine learning-based method aimed at improving the accuracy of T-LES fields in the context of highly anisothermal flows. We compare this method with an already existing super-resolution method. We train our convolutional neural network by filtering Direct Numerical Simulation (DNS) snapshots into T-LES ones, and optimize our network to reconstruct DNS small scales from T-LES snapshots. Our results show that the neural network outperforms the classical reconstruction method in terms of the quality of the reconstructed coherent structures, but ends up increasing the Root Mean Square (RMS) values over the DNS ones","PeriodicalId":483187,"journal":{"name":"Entropie =","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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