{"id":27654,"date":"2026-05-19T10:50:37","date_gmt":"2026-05-19T08:50:37","guid":{"rendered":"https:\/\/greth.fr\/?p=27654"},"modified":"2026-05-19T11:17:33","modified_gmt":"2026-05-19T09:17:33","slug":"intelligence-artificielle-pour-les-materiaux-a-changement-de-phase","status":"publish","type":"post","link":"https:\/\/greth.fr\/en\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/","title":{"rendered":"Artificial Intelligence for Phase Change Materials"},"content":{"rendered":"<strong><span style=\"text-decoration: underline;\">Summary:<\/span><\/strong><\/p>\n<p style=\"text-align: justify;\">The continuous increase in power density of electronic and energy storage systems, particularly lithium-ion batteries, imposes increasingly stringent thermal management requirements. Effective temperature control is essential to ensure the reliability, safety, and lifetime of these systems. Among the various passive cooling solutions, phase change materials (PCMs) have attracted considerable attention due to their ability to store and release large amounts of thermal energy in the form of latent heat during solid\u2013liquid transitions. Their integration into passive thermal management systems helps to smooth temperature peaks and maintain stable operating conditions. Nevertheless, conventional PCMs suffer from inherently low thermal conductivity, which limits their performance under high heat flux conditions. To overcome this limitation, several enhancement techniques have been investigated, including the incorporation of highly conductive nanoparticles and the addition of metallic fins. However, the optimal design of such hybrid systems remains a challenging task due to the large number of interacting parameters and their strong nonlinear dependencies. In this context, the present PhD thesis proposes the use of artificial intelligence (AI) to model, interpret, and optimize the thermal behavior of PCM-based systems. The first stage focuses on developing a predictive model based on artificial neural networks (ANNs) to estimate the effective thermal conductivity of nanoparticle-enhanced phase change materials (NEPCMs) using literature-derived datasets. The model was further analyzed using the SHAP (SHapley Additive exPlanations) method, which enabled the identification of the most influential parameters and provided a physical interpretation of the nonlinear relationships between nanoparticle characteristics and thermal response. The second stage addresses the optimization of a passive thermal management system for cylindrical lithium-ion cells incorporating a PCM enriched with copper nanoparticles and equipped with aluminum fins. A three-dimensional parametric CFD study was conducted using ANSYS Fluent to evaluate the effects of key design and material parameters such as PCM thickness, fin number, nanoparticle concentration, and PCM type. A numerical campaign of 450 configurations combining these parameters was performed to generate a comprehensive database for training an ANN-based metamodel designed to predict the maximum cell temperature and the average liquid fraction of the PCM. This surrogate model was then coupled with a genetic algorithm (GA) within a deterministic design optimization (DDO) framework to identify the optimal configuration that minimizes the maximum temperature while maintaining a controlled partial melting of the PCM. The results confirm the efficiency of the ANN-GA coupling, which enables a significant reduction in computational cost compared to direct CFD-based optimization while ensuring high prediction accuracy. The interpretability analysis also provided valuable insights into the physical mechanisms governing the system\u2019s thermal behavior. Overall, this thesis contributes to the advancement of AI-assisted modeling and optimization of PCM-based thermal systems. It demonstrates that the integration of artificial intelligence, numerical simulation, and evolutionary algorithms provides a robust and efficient framework for the design of next-generation passive thermal management systems. Future work could extend this methodology toward multi-objective and reliability-based design optimization (RBDO) approaches, incorporating uncertainties in material properties and operating conditions to achieve even more reliable and adaptive thermal solutions.<\/p>\n<table border=\"1\" width=\"100%\">\n<tbody>\n<tr>\n<td><strong>Author<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Abir MSALMI<\/td>\n<\/tr>\n<tr>\n<td><strong>Date of presentation<\/strong><\/td>\n<\/tr>\n<tr>\n<td>2026, january 30th<\/td>\n<\/tr>\n<tr>\n<td><strong>Keywords<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Computational fluid dynamics, Artificial Intelligence, Phase Change Materials (PCM), Nano-Phase Change Materials (Nano-PCM), Lithium-Ion battery, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Metamodel-Based Design Optimization (MBDO), SHapley Additive exPlanations (SHAP), Heat--Transmission, Structural optimization<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n<div class=\"eds-animate edsanimate-sis-hidden\" data-eds-entry-animation=\"bounceIn\" data-eds-entry-delay=\"0\" data-eds-entry-duration=\"1\" data-eds-entry-timing=\"linear\" data-eds-exit-animation=\"\" data-eds-exit-delay=\"\" data-eds-exit-duration=\"\" data-eds-exit-timing=\"\" data-eds-repeat-count=\"1\" data-eds-keep=\"yes\" data-eds-animate-on=\"load\" data-eds-scroll-offset=\"\">\n\t\t<div class=\"box info\">\n\t\t\t<div class=\"box-inner-block\">\n\t\t\t\t<span class=\"fa tie-shortcode-boxicon\"><\/span>\n<p style=\"text-align: justify;\"><strong>&#x2666; The full version is only available for subscribers <a style=\"text-decoration: none;\" href=\"\/en\/offre-du-greth\/\"><span style=\"background-color: #3db2ea; color: #fff;\">\"ESSENTIEL\"<\/span><\/a> or <a style=\"text-decoration: none;\" href=\"\/en\/offre-du-greth\/\"><span style=\"background-color: #c6002c; color: #fff;\">\"PREMIUM\"<\/span><\/a> of GRETh!<\/strong><\/p>\n<p>&#x2666; If you are already a member \/ subscriber, you must identify yourself by <a href=\"\/wp-login.php\"><strong>clicking here<\/strong><\/a>.<br \/>\n&#x2666; If you are not a member, you can consult the offer proposed by GRETh by <a href=\"\/en\/offre-du-greth\/\"><strong>clicking here<\/strong><\/a> as well as the conditions of membership by <a href=\"\/en\/conditions-adhesion\/\"><strong>clicking here<\/strong><\/a>.\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>","protected":false},"excerpt":{"rendered":"<p>R\u00e9sum\u00e9 : L\u2019augmentation constante de la densit\u00e9 de puissance des dispositifs \u00e9lectroniques et des syst\u00e8mes de stockage d\u2019\u00e9nergie, tels que les batteries lithium-ion, impose des exigences thermiques de plus en plus s\u00e9v\u00e8res. Le contr\u00f4le de la temp\u00e9rature constitue un enjeu essentiel pour garantir la fiabilit\u00e9, la s\u00e9curit\u00e9 et la durabilit\u00e9 de ces syst\u00e8mes. Parmi les solutions explor\u00e9es, les mat\u00e9riaux \u00e0 changement de phase (MCP) repr\u00e9sentent une voie particuli\u00e8rement prometteuse gr\u00e2ce \u00e0 leur capacit\u00e9 \u00e0 stocker et \u00e0 restituer l\u2019\u00e9nergie thermique sous forme de chaleur latente lors des transitions solide\u2013liquide. Leur int\u00e9gration dans des syst\u00e8mes passifs permet d\u2019att\u00e9nuer les pics de temp\u00e9rature et d\u2019assurer un fonctionnement plus stable des composants. Cependant, les MCP pr\u00e9sentent certaines limites, notamment leur faible conductivit\u00e9 thermique, qui restreint leur efficacit\u00e9 dans les r\u00e9gimes de fonctionnement intensifs. Pour pallier cette contrainte, l\u2019introduction de nanoparticules hautement conductrices et l\u2019ajout d\u2019ailettes m\u00e9talliques constituent des approches efficaces pour renforcer le transfert de chaleur.<\/p>","protected":false},"author":1,"featured_media":962,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20,21],"tags":[6469,6977,6979,6978],"class_list":["post-27654","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-documents","category-memoires-theses","tag-gestion-thermique","tag-metamodele","tag-nano-mcp","tag-reseaux-de-neurones-artificiels-ann"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.9 (Yoast SEO v27.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Intelligence artificielle pour les mat\u00e9riaux \u00e0 changement de phase - GRETh<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/greth.fr\/en\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Intelligence artificielle pour les mat\u00e9riaux \u00e0 changement de phase\" \/>\n<meta property=\"og:description\" content=\"R\u00e9sum\u00e9 : L\u2019augmentation constante de la densit\u00e9 de puissance des dispositifs \u00e9lectroniques et des syst\u00e8mes de stockage d\u2019\u00e9nergie, tels que les batteries lithium-ion, impose des exigences thermiques de plus en plus s\u00e9v\u00e8res. Le contr\u00f4le de la temp\u00e9rature constitue un enjeu essentiel pour garantir la fiabilit\u00e9, la s\u00e9curit\u00e9 et la durabilit\u00e9 de ces syst\u00e8mes. Parmi les solutions explor\u00e9es, les mat\u00e9riaux \u00e0 changement de phase (MCP) repr\u00e9sentent une voie particuli\u00e8rement prometteuse gr\u00e2ce \u00e0 leur capacit\u00e9 \u00e0 stocker et \u00e0 restituer l\u2019\u00e9nergie thermique sous forme de chaleur latente lors des transitions solide\u2013liquide. Leur int\u00e9gration dans des syst\u00e8mes passifs permet d\u2019att\u00e9nuer les pics de temp\u00e9rature et d\u2019assurer un fonctionnement plus stable des composants. Cependant, les MCP pr\u00e9sentent certaines limites, notamment leur faible conductivit\u00e9 thermique, qui restreint leur efficacit\u00e9 dans les r\u00e9gimes de fonctionnement intensifs. Pour pallier cette contrainte, l\u2019introduction de nanoparticules hautement conductrices et l\u2019ajout d\u2019ailettes m\u00e9talliques constituent des approches efficaces pour renforcer le transfert de chaleur.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/greth.fr\/en\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/\" \/>\n<meta property=\"og:site_name\" content=\"GRETh\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/grethfr\/\" \/>\n<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/grethfr\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-19T08:50:37+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-19T09:17:33+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/greth.fr\/wp-content\/uploads\/2015\/08\/theses-6.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"387\" \/>\n\t<meta property=\"og:image:height\" content=\"310\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"GRETh\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@grethfr\" \/>\n<meta name=\"twitter:site\" content=\"@grethfr\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"GRETh\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/greth.fr\\\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/greth.fr\\\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\\\/\"},\"author\":{\"name\":\"GRETh\",\"@id\":\"https:\\\/\\\/greth.fr\\\/#\\\/schema\\\/person\\\/6e173032c4ce46c14339ff999b721f00\"},\"headline\":\"Intelligence artificielle pour les mat\u00e9riaux \u00e0 changement de phase\",\"datePublished\":\"2026-05-19T08:50:37+00:00\",\"dateModified\":\"2026-05-19T09:17:33+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/greth.fr\\\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\\\/\"},\"wordCount\":1570,\"publisher\":{\"@id\":\"https:\\\/\\\/greth.fr\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/greth.fr\\\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/greth.fr\\\/wp-content\\\/uploads\\\/2015\\\/08\\\/theses-6.jpg\",\"keywords\":[\"gestion thermique\",\"M\u00e9tamod\u00e8le\",\"Nano-MCP\",\"R\u00e9seaux de neurones artificiels (ANN)\"],\"articleSection\":[\"Documents\",\"M\u00e9moires de th\u00e8ses\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/greth.fr\\\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\\\/\",\"url\":\"https:\\\/\\\/greth.fr\\\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\\\/\",\"name\":\"Intelligence artificielle pour les mat\u00e9riaux \u00e0 changement de phase - 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GRETh","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/greth.fr\/en\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/","og_locale":"en_US","og_type":"article","og_title":"Intelligence artificielle pour les mat\u00e9riaux \u00e0 changement de phase","og_description":"R\u00e9sum\u00e9 : L\u2019augmentation constante de la densit\u00e9 de puissance des dispositifs \u00e9lectroniques et des syst\u00e8mes de stockage d\u2019\u00e9nergie, tels que les batteries lithium-ion, impose des exigences thermiques de plus en plus s\u00e9v\u00e8res. Le contr\u00f4le de la temp\u00e9rature constitue un enjeu essentiel pour garantir la fiabilit\u00e9, la s\u00e9curit\u00e9 et la durabilit\u00e9 de ces syst\u00e8mes. Parmi les solutions explor\u00e9es, les mat\u00e9riaux \u00e0 changement de phase (MCP) repr\u00e9sentent une voie particuli\u00e8rement prometteuse gr\u00e2ce \u00e0 leur capacit\u00e9 \u00e0 stocker et \u00e0 restituer l\u2019\u00e9nergie thermique sous forme de chaleur latente lors des transitions solide\u2013liquide. Leur int\u00e9gration dans des syst\u00e8mes passifs permet d\u2019att\u00e9nuer les pics de temp\u00e9rature et d\u2019assurer un fonctionnement plus stable des composants. Cependant, les MCP pr\u00e9sentent certaines limites, notamment leur faible conductivit\u00e9 thermique, qui restreint leur efficacit\u00e9 dans les r\u00e9gimes de fonctionnement intensifs. Pour pallier cette contrainte, l\u2019introduction de nanoparticules hautement conductrices et l\u2019ajout d\u2019ailettes m\u00e9talliques constituent des approches efficaces pour renforcer le transfert de chaleur.","og_url":"https:\/\/greth.fr\/en\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/","og_site_name":"GRETh","article_publisher":"https:\/\/www.facebook.com\/grethfr\/","article_author":"https:\/\/www.facebook.com\/grethfr\/","article_published_time":"2026-05-19T08:50:37+00:00","article_modified_time":"2026-05-19T09:17:33+00:00","og_image":[{"width":387,"height":310,"url":"https:\/\/greth.fr\/wp-content\/uploads\/2015\/08\/theses-6.jpg","type":"image\/jpeg"}],"author":"GRETh","twitter_card":"summary_large_image","twitter_creator":"@grethfr","twitter_site":"@grethfr","twitter_misc":{"Written by":"GRETh","Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/greth.fr\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/#article","isPartOf":{"@id":"https:\/\/greth.fr\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/"},"author":{"name":"GRETh","@id":"https:\/\/greth.fr\/#\/schema\/person\/6e173032c4ce46c14339ff999b721f00"},"headline":"Intelligence artificielle pour les mat\u00e9riaux \u00e0 changement de phase","datePublished":"2026-05-19T08:50:37+00:00","dateModified":"2026-05-19T09:17:33+00:00","mainEntityOfPage":{"@id":"https:\/\/greth.fr\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/"},"wordCount":1570,"publisher":{"@id":"https:\/\/greth.fr\/#organization"},"image":{"@id":"https:\/\/greth.fr\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/#primaryimage"},"thumbnailUrl":"https:\/\/greth.fr\/wp-content\/uploads\/2015\/08\/theses-6.jpg","keywords":["gestion thermique","M\u00e9tamod\u00e8le","Nano-MCP","R\u00e9seaux de neurones artificiels (ANN)"],"articleSection":["Documents","M\u00e9moires de th\u00e8ses"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/greth.fr\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/","url":"https:\/\/greth.fr\/intelligence-artificielle-pour-les-materiaux-a-changement-de-phase\/","name":"Intelligence artificielle pour les mat\u00e9riaux \u00e0 changement de phase - 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