[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119345-en":3,"doc-seo-119345-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119345,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning-enhanced PIV for analyzing microfiber-wall turbulence interactions - Key findings","A machine learning-based approach, RAFT-PIV, is used to measure the flow field around a microplastic fiber in a turbulent channel flow with single-pixel resolution at a Shear Reynolds number of 1000. The measurements reveal how the fiber interacts with a hairpin vortex. Fiber rotation rate is correlated with slip velocity distributions along the fiber length, showing higher rotation rates with larger slip-velocity gradients. Alignment with the spanwise direction emerges through progressive alignment with the vortex head, identified via swirling strength, shear strain rate, and local flow velocity, supporting machine learning-enhanced PIV for studying fiber–turbulence interactions in microplastic pollution mitigation.","| Machine learning-enhanced PIV for analyzing microfiber-wall turbulence interactions |  |  |  |\n| --- | --- | --- | --- |\n| Vlad Giurgiua, Leonel Beckedorffa, Giuseppe C.A. Caridia, Christian Lagemann b, Alfredo Soldati a,c,∗\u003Cbr>a Institute of Fluid Mechanics and Heat Transfer, TU Wien, 1060 Wien, Austria bAI Institute in Dynamic Systems, University of Washington, 98195 Seattle, USAc Polytechnic Department, University of Udine, 33100 Udine, Italy |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Machine learning PIV\u003Cbr>Wall turbulence Channel flow Microplastic Fiber\u003Cbr>Rotation rate Hairpin vortex |  | A machine learning-based approach, RAFT-PIV, is used to measure with single-pixel resolution the flow field around a microplastic fiber in a turbulent channel flow at a Shear Reynolds number of 1000. The results reveal the interaction of the fiber with a hairpin vortex. The fiber rotation rate is correlated with slip velocity distributions along the fiber length, demonstrating higher rotation rates with increased slip velocity gradients. The fiber’s alignment with the spanwise direction during its trajectory is explained through its progressive alignment with the head of a hairpin vortex, characterized by the swirling strength, shear strain rate, and local flow velocity. Higher fiber rotation rates were found likelier in the presence of a vortical structure. These findings highlight the potential of machine learning-enhanced PIV techniques to deepen our understanding of fiber-turbulence interactions, essential for applications such as microplastic pollution mitigation. |  |\n\n1. Introduction  \nUnderstanding the dynamics of microscopic fibers in turbulent flows is crucial for various environmental and industrial applications, including pollution control, marine biology, and chemical engineering (Voth and Soldati, 2017). The anisotropy of these particles leads to orientation-dependent drag coefficients, resulting in a resistance tensor which promotes more complex modes of solid-body rotation in comparison with spherical particle (Voth and Soldati, 2017).  \nIn the specific case of oceanic pollution, microplastics have a size of about one millimetre, a length-to-diameter aspect ratio of about 100, and their dynamics depend on the forces and torques applied by the smallest turbulence scales. Since no closed form of the drag on nonspherical particles is currently available, the prediction of the dynamics of anisotropic particles and in turn of their sedimentation and dispersion rates remains beyond current possibilities. To find such detailed information field measurements are out of question, with controlled experimental campaigns remaining the only viable option. In previous works, we have described a channel facility in which controlled and reproducible wall turbulence can be realized (Giurgiu et al., 2023), and the optical techniques by which we can measure the dynamics of quasiinertialess high aspect ratio fibers longer than few tens Kolmogorov length scales (Alipour et al., 2021; Giurgiu et al., 2024). However, to  \n∗ Corresponding author.  \nE-mail address: [alfredo.soldati@tuwien.ac.at](alfredo.soldati@tuwien.ac.at) (A. Soldati).  \nmodel fiber dynamics in turbulence it is necessary to measure simultaneously fiber motion and the motion of the surrounding fluid. This study uses a machine learning approach to achieve precise (i.e., singlepixel resolution) flow prediction around a fiber with higher resolution than the traditional cross-correlation-based Particle Image Velocimetry (CC-PIV) technique.  \nCC-PIV has been widely used to measure velocity fields in fluid flows. The procedure involves seeding the fluid with tracing particles, which are illuminated and imaged with a camera. Image pairs are recorded at a known time separation during which the tracers are displaced by the flow. The images are then divided into interrogation windows, e.g. with a size of 16 × 16 px. The tracers’ displacement within each w","cbCaivR5yHmfqm3a","https://ap.wps.com/l/cbCaivR5yHmfqm3a","pdf",2726969,1,"English","en",105,"# Introduction\n## Microfiber/microplastic dynamics in turbulence\n## Experimental and optical measurement background\n## Need for simultaneous fiber and flow measurements\n# CC-PIV fundamentals and prior work\n## Cross-correlation procedure\n## Applications to multiphase and fiber-laden flows\n## Limitations and motivation for RAFT-PIV","[{\"question\":\"What does the RAFT-PIV method measure in this study?\",\"answer\":\"RAFT-PIV measures the flow field around a microplastic fiber in a turbulent channel flow with single-pixel resolution, enabling simultaneous characterization of the surrounding fluid motion.\"},{\"question\":\"How is the fiber rotation rate related to flow quantities?\",\"answer\":\"The fiber rotation rate correlates with slip velocity distributions along the fiber length, with higher rotation rates occurring when slip-velocity gradients increase.\"},{\"question\":\"Why does the fiber align with the spanwise direction during its trajectory?\",\"answer\":\"The alignment is explained by the fiber progressively aligning with the head of a hairpin vortex, characterized using swirling strength, shear strain rate, and local flow velocity, and vortical structures tend to produce larger rotation rates.\"}]","Machine learning-enhanced PIV for analyzing microfiber-wall turbulence interactions - Key findings | PDF",1785723805,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-enhanced-piv-for-analyzing-microfiber-wall-turbulence-interactions-key-findings","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-enhanced-piv-for-analyzing-microfiber-wall-turbulence-interactions-key-findings/119345/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does the RAFT-PIV method measure in this study?","Question",{"text":74,"@type":75},"RAFT-PIV measures the flow field around a microplastic fiber in a turbulent channel flow with single-pixel resolution, enabling simultaneous characterization of the surrounding fluid motion.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is the fiber rotation rate related to flow quantities?",{"text":79,"@type":75},"The fiber rotation rate correlates with slip velocity distributions along the fiber length, with higher rotation rates occurring when slip-velocity gradients increase.",{"name":81,"@type":72,"acceptedAnswer":82},"Why does the fiber align with the spanwise direction during its trajectory?",{"text":83,"@type":75},"The alignment is explained by the fiber progressively aligning with the head of a hairpin vortex, characterized using swirling strength, shear strain rate, and local flow velocity, and vortical structures tend to produce larger rotation rates.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]