{"id":29580,"date":"2025-07-24T09:41:47","date_gmt":"2025-07-24T07:41:47","guid":{"rendered":"https:\/\/www.revistanuclear.es\/?p=29580"},"modified":"2025-07-24T09:41:54","modified_gmt":"2025-07-24T07:41:54","slug":"contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.revistanuclear.es\/en\/fuel-cycle\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\/","title":{"rendered":"Contribution to PWR Core Loading Pattern Design assisted by Artificial Intelligence"},"content":{"rendered":"<p style=\"text-align: justify;\"><div class=\"rn-icon-panel__wrap\" style=\"display:flex;justify-content:flex-start;\"><a class=\"rn-icon-panel descargar\" href=\"https:\/\/www.revistanuclear.es\/wp-content\/uploads\/2025\/07\/Patrones-de-carga-PWR-IA.pdf\" target=\"_blank\" rel=\"noopener noreferrer nofollow\" style=\"--rn-bg:#8baf31;--rn-color:#ffffff;--rn-border:0px solid;--rn-shadow:0px 0px 0px;--rn-rounded:12px;--rn-padding:14px;--rn-gap:10px;--rn-icon-size:30px;\"><span class=\"rn-icon-panel__icon\" aria-hidden=\"true\"><img src=\"https:\/\/revistanuclear.es\/wp-content\/uploads\/2022\/12\/click.png\" alt=\"\" loading=\"lazy\" decoding=\"async\" \/><\/span><span class=\"rn-icon-panel__text\">SEE FULL VERSION<\/span><\/a><br><\/div><span class=\"su-dropcap su-dropcap-style-simple\" style=\"font-size:2.5em\">C<\/span><span style=\"font-weight: 400;\">ore design is the a priori study of the behavior of a reactor core throughout a cycle between two reloadings.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There is ongoing interest in using Artificial Intelligence (AI) tools to accelerate these types of calculations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Coupled thermalhydraulic\/neutronic calculations provide access to many variables of particular interest for developing a metamodel and its optimization, as they present constraints from an economic, safety, and licensing perspective. Of particular interest are maximum power, duration of the cycle, and boron concentration.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The various computational programs used for this study were PARCS (v3.4.2) and PATHS (v1.08). PATHS is a thermalhydraulic code that can be coupled with PARCS. The latter calculates the neutron part of the problem. PATHS uses a drift flux model for two-phase flow, rather than the typical six-equation two-phase flow model used in system codes. Matlab and Python are also used for tasks such as the automatic generation of input files for PATHS and PARCS, with a random distribution of the loading pattern.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The random generation of a certain number of different fuel configurations must meet several criteria (correct number of each fuel element type, impossibility of two identical fuel elements being side by side, etc.). The outputs of the automatic post-processing programs of the huge amount of results obtained (Boron Concentration, duration of cycle, , PCT, Maximum Power, etc.), make up the database that serves as input for the training and verification of a neural network (NN) (Keras), whose objective is to obtain a metamodel with which to accelerate the calculation of optimization in future works of the optimal fuel configuration based on certain criteria.<\/span><\/p>\n<p class=\"p1\"><div class=\"rn-icon-panel__wrap\" style=\"display:flex;justify-content:flex-start;\"><a class=\"rn-icon-panel\" href=\"https:\/\/www.revistanuclear.es\/wp-content\/uploads\/2025\/07\/Patrones-de-carga-PWR-IA.pdf\" target=\"_blank\" rel=\"noopener noreferrer nofollow\" style=\"--rn-bg:#8baf31;--rn-color:#ffffff;--rn-border:0px solid;--rn-shadow:0px 0px 0px;--rn-rounded:12px;--rn-padding:14px;--rn-gap:10px;--rn-icon-size:0px;\"><span class=\"rn-icon-panel__icon\" aria-hidden=\"true\"><img src=\"https:\/\/revistanuclear.es\/wp-content\/uploads\/2022\/12\/click.png\" alt=\"\" loading=\"lazy\" decoding=\"async\" \/><\/span><span class=\"rn-icon-panel__text\">SEE FULL VERSION<\/span><\/a><br><\/div>\n","protected":false},"excerpt":{"rendered":"<p>ore design is the a priori study of the behavior of a reactor core throughout a cycle between two reloadings. There is ongoing interest in using Artificial Intelligence (AI) tools to accelerate these types of calculations. Coupled thermalhydraulic\/neutronic calculations provide access to many variables of particular interest for developing a metamodel and its optimization, as they present constraints from an economic, safety, and licensing perspective. Of particular interest are maximum power, duration of the cycle, and boron concentration. The various [&hellip;]<\/p>\n","protected":false},"author":1722,"featured_media":29584,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"mc4wp_mailchimp_campaign":[],"footnotes":""},"categories":[70],"tags":[3955,3957,3956],"coauthors":[3945,3947,3948,3950,3952,3954],"class_list":["post-29580","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fuel-cycle","tag-core-design","tag-keras-en","tag-paths-parcs-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Contribution to PWR Core Loading Pattern Design assisted by Artificial Intelligence - Fuel Cycle<\/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:\/\/www.revistanuclear.es\/en\/fuel-cycle\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Contribution to PWR Core Loading Pattern Design assisted by Artificial Intelligence - Fuel Cycle\" \/>\n<meta property=\"og:description\" content=\"ore design is the a priori study of the behavior of a reactor core throughout a cycle between two reloadings. There is ongoing interest in using Artificial Intelligence (AI) tools to accelerate these types of calculations. Coupled thermalhydraulic\/neutronic calculations provide access to many variables of particular interest for developing a metamodel and its optimization, as they present constraints from an economic, safety, and licensing perspective. Of particular interest are maximum power, duration of the cycle, and boron concentration. The various [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.revistanuclear.es\/en\/fuel-cycle\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\/\" \/>\n<meta property=\"og:site_name\" content=\"Revista Nuclear Espa\u00f1a\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/SNE.spain\" \/>\n<meta property=\"article:published_time\" content=\"2025-07-24T07:41:47+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-24T07:41:54+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.revistanuclear.es\/wp-content\/uploads\/2025\/07\/Combustible.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Pablo Pallar\u00e9s Font de Mora, Rafael Mir\u00f3 Herrero, Teresa Barrachina Celda, Enrique Quintana-Ort\u00ed, Jos\u00e9 Garc\u00eda Sanjuan, Javier Jorge L\u00f3pez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@SNEu235\" \/>\n<meta name=\"twitter:site\" content=\"@SNEu235\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Pablo Pallar\u00e9s Font de Mora, Rafael Mir\u00f3 Herrero, Teresa Barrachina Celda, Enrique Quintana-Ort\u00ed, Jos\u00e9 Garc\u00eda Sanjuan, Javier Jorge L\u00f3pez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/fuel-cycle\\\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/fuel-cycle\\\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\\\/\"},\"author\":{\"name\":\"Pablo Pallar\u00e9s Font de Mora\",\"@id\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/#\\\/schema\\\/person\\\/02772da6814516d829d9a8ace1ca9155\"},\"headline\":\"Contribution to PWR Core Loading Pattern Design assisted by Artificial Intelligence\",\"datePublished\":\"2025-07-24T07:41:47+00:00\",\"dateModified\":\"2025-07-24T07:41:54+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/fuel-cycle\\\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\\\/\"},\"wordCount\":377,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/fuel-cycle\\\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.revistanuclear.es\\\/wp-content\\\/uploads\\\/2025\\\/07\\\/Combustible.jpg\",\"keywords\":[\"core design\",\"KERAS\",\"PATHS\\\/PARCS\"],\"articleSection\":[\"Fuel Cycle\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/fuel-cycle\\\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/fuel-cycle\\\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\\\/\",\"url\":\"https:\\\/\\\/www.revistanuclear.es\\\/en\\\/fuel-cycle\\\/contribution-to-pwr-core-loading-pattern-design-assisted-by-artificial-intelligence\\\/\",\"name\":\"Contribution to PWR Core Loading Pattern Design assisted by Artificial Intelligence - 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