{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:29:30Z","timestamp":1762918170543},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This article introduces some soft computing methods generally used for data analysis and flow pattern detection in fluid dynamics. These techniques decompose the original flow field as an expansion of modes, which can be either orthogonal in time (variants of dynamic mode decomposition), or in space (variants of proper orthogonal decomposition) or in time and space (spectral proper orthogonal decomposition), or they can simply be selected using some sophisticated statistical techniques (empirical mode decomposition). The performance of these methods is tested in the turbulent wake of a wall-mounted square cylinder. This highly complex flow is suitable to show the ability of the aforementioned methods to reduce the degrees of freedom of the original data by only retaining the large scales in the flow. The main result is a reduced-order model of the original flow case, based on a low number of modes. A deep discussion is carried out about how to choose the most computationally efficient method to obtain suitable reduced-order models of the flow. The techniques introduced in this article are data-driven methods that could be applied to model any type of non-linear dynamical system, including numerical and experimental databases.<\/jats:p>","DOI":"10.1093\/jigpal\/jzaa060","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T20:11:48Z","timestamp":1604002308000},"page":"263-276","source":"Crossref","is-referenced-by-count":6,"title":["Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder"],"prefix":"10.1093","volume":"30","author":[{"given":"Christian","family":"Amor","sequence":"first","affiliation":[{"name":"School of Aerospace Engineering, Universidad Polit\u00e9cnica de Madrid, 28040, Madrid, Spain"}]},{"given":"Jos\u00e9 M","family":"P\u00e9rez","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Universidad Polit\u00e9cnica de Madrid, 28040, Madrid, Spain"}]},{"given":"Philipp","family":"Schlatter","sequence":"additional","affiliation":[{"name":"Linn\u00e9 FLOW Centre, KTH Mechanics, Stockholm 114 28, Sweden"}]},{"given":"Ricardo","family":"Vinuesa","sequence":"additional","affiliation":[{"name":"Linn\u00e9 FLOW Centre, KTH Mechanics, Stockholm 114 28, Sweden"}]},{"given":"Soledad","family":"Le Clainche","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Universidad Polit\u00e9cnica de Madrid, 28040, Madrid, Spain"}]}],"member":"286","published-online":{"date-parts":[[2020,11,6]]},"reference":[{"key":"2022032112132601000_ref1","doi-asserted-by":"crossref","first-page":"015107","DOI":"10.1063\/1.4939712","article-title":"Predicting the response of small-scale near-wall turbulence to large-scale outer motions","volume":"28","author":"Agostini","year":"2016","journal-title":"Physics of 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