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In an effort to add geometrical diversity and explore singular morphologies, we have developed an algorithm capable of characterizing almost any geometry, based on an extensive CFD database with more than 15\u2009800 geometries obtained from a Monte Carlo sampling of the space of possible geometries. With this framework, it is possible to estimate various quantities of interest, such as the heat flux in the enhanced zone and total drag, with relative errors below 10% and 2%, respectively. Thus, we establish the utility of machine learning to develop surrogate models for the rapid performance prediction of novel enhanced microsurfaces.<\/jats:p>","DOI":"10.1088\/2632-2153\/acca60","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T18:38:17Z","timestamp":1680633497000},"page":"025012","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Data-driven prediction of the performance of enhanced surfaces from an extensive CFD-generated parametric search space"],"prefix":"10.1088","volume":"4","author":[{"given":"A","family":"Larra\u00f1aga","sequence":"first","affiliation":[]},{"given":"S L","family":"Brunton","sequence":"additional","affiliation":[]},{"given":"J","family":"Mart\u00ednez","sequence":"additional","affiliation":[]},{"given":"S","family":"Chapela","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2197-3269","authenticated-orcid":true,"given":"J","family":"Porteiro","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"mlstacca60bib1","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1016\/j.rser.2013.07.028","article-title":"High temperature latent heat thermal energy storage: phase change materials, design considerations and performance enhancement techniques","volume":"27","author":"C\u00e1rdenas","year":"2013","journal-title":"Renew. 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