Robust Principal Component Analysis for Particle Image Velocimetry. Scherl, I., Strom, B., Shang, J., K., Williams, O., Polagye, B., L., & Brunton, S., L. 4, 2019. ![Robust Principal Component Analysis for Particle Image Velocimetry [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex 3 downloads Particle image velocimetry (PIV) is an experimental technique used to measure fluid flow fields. However, PIV fields often have spurious and missing velocity vectors that degrade subsequent analyses. Standard post-processing involves the identification and replacement of outliers based on local information. We present a method to identify and fill in erroneous or missing PIV vectors using global information via robust principal component analysis (RPCA), a statistical technique developed for outlier rejection. RPCA decomposes a data matrix into a low-rank matrix containing coherent structures and a sparse matrix of outliers. We explore RPCA on a range of fluid simulations and experiments of varying complexity. First, we analyze direct numerical simulations of flow past a circular cylinder at Reynolds number 100 with artificially added outliers, alongside similar PIV measurements at Reynolds number 413. Next, we apply RPCA to a turbulent channel flow simulation from the Johns Hopkins Turbulence database, demonstrating that dominant coherent structures are maintained and the turbulent kinetic energy spectrum remains largely intact. Finally, we investigate PIV measurements behind a two-bladed cross-flow turbine that exhibits both broadband and coherent phenomena. In all cases, we find that RPCA extracts dominant fluid coherent structures and identifies and fills in corruption and outliers, with minimal degradation of small scale structures.
@article{
title = {Robust Principal Component Analysis for Particle Image Velocimetry},
type = {article},
year = {2019},
websites = {http://arxiv.org/abs/1905.07062},
month = {4},
id = {b2b8c1c5-c3df-3831-acee-24a087c7762f},
created = {2021-04-09T15:24:23.533Z},
file_attached = {false},
profile_id = {75799766-8e2d-3c98-81f9-e3efa41233d0},
group_id = {c9329632-2a50-3043-b803-cadc8dbdfc3f},
last_modified = {2021-04-09T15:24:23.533Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {false},
hidden = {false},
source_type = {article},
private_publication = {false},
abstract = {Particle image velocimetry (PIV) is an experimental technique used to measure fluid flow fields. However, PIV fields often have spurious and missing velocity vectors that degrade subsequent analyses. Standard post-processing involves the identification and replacement of outliers based on local information. We present a method to identify and fill in erroneous or missing PIV vectors using global information via robust principal component analysis (RPCA), a statistical technique developed for outlier rejection. RPCA decomposes a data matrix into a low-rank matrix containing coherent structures and a sparse matrix of outliers. We explore RPCA on a range of fluid simulations and experiments of varying complexity. First, we analyze direct numerical simulations of flow past a circular cylinder at Reynolds number 100 with artificially added outliers, alongside similar PIV measurements at Reynolds number 413. Next, we apply RPCA to a turbulent channel flow simulation from the Johns Hopkins Turbulence database, demonstrating that dominant coherent structures are maintained and the turbulent kinetic energy spectrum remains largely intact. Finally, we investigate PIV measurements behind a two-bladed cross-flow turbine that exhibits both broadband and coherent phenomena. In all cases, we find that RPCA extracts dominant fluid coherent structures and identifies and fills in corruption and outliers, with minimal degradation of small scale structures.},
bibtype = {article},
author = {Scherl, Isabel and Strom, Benjamin and Shang, Jessica K and Williams, Owen and Polagye, Brian L and Brunton, Steven L}
}
Downloads: 3
{"_id":"HgCP3mhNKfoj8Z6nx","bibbaseid":"scherl-strom-shang-williams-polagye-brunton-robustprincipalcomponentanalysisforparticleimagevelocimetry-2019","authorIDs":[],"author_short":["Scherl, I.","Strom, B.","Shang, J., K.","Williams, O.","Polagye, B., L.","Brunton, S., L."],"bibdata":{"title":"Robust Principal Component Analysis for Particle Image Velocimetry","type":"article","year":"2019","websites":"http://arxiv.org/abs/1905.07062","month":"4","id":"b2b8c1c5-c3df-3831-acee-24a087c7762f","created":"2021-04-09T15:24:23.533Z","file_attached":false,"profile_id":"75799766-8e2d-3c98-81f9-e3efa41233d0","group_id":"c9329632-2a50-3043-b803-cadc8dbdfc3f","last_modified":"2021-04-09T15:24:23.533Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"source_type":"article","private_publication":false,"abstract":"Particle image velocimetry (PIV) is an experimental technique used to measure fluid flow fields. However, PIV fields often have spurious and missing velocity vectors that degrade subsequent analyses. Standard post-processing involves the identification and replacement of outliers based on local information. We present a method to identify and fill in erroneous or missing PIV vectors using global information via robust principal component analysis (RPCA), a statistical technique developed for outlier rejection. RPCA decomposes a data matrix into a low-rank matrix containing coherent structures and a sparse matrix of outliers. We explore RPCA on a range of fluid simulations and experiments of varying complexity. First, we analyze direct numerical simulations of flow past a circular cylinder at Reynolds number 100 with artificially added outliers, alongside similar PIV measurements at Reynolds number 413. Next, we apply RPCA to a turbulent channel flow simulation from the Johns Hopkins Turbulence database, demonstrating that dominant coherent structures are maintained and the turbulent kinetic energy spectrum remains largely intact. Finally, we investigate PIV measurements behind a two-bladed cross-flow turbine that exhibits both broadband and coherent phenomena. In all cases, we find that RPCA extracts dominant fluid coherent structures and identifies and fills in corruption and outliers, with minimal degradation of small scale structures.","bibtype":"article","author":"Scherl, Isabel and Strom, Benjamin and Shang, Jessica K and Williams, Owen and Polagye, Brian L and Brunton, Steven L","bibtex":"@article{\n title = {Robust Principal Component Analysis for Particle Image Velocimetry},\n type = {article},\n year = {2019},\n websites = {http://arxiv.org/abs/1905.07062},\n month = {4},\n id = {b2b8c1c5-c3df-3831-acee-24a087c7762f},\n created = {2021-04-09T15:24:23.533Z},\n file_attached = {false},\n profile_id = {75799766-8e2d-3c98-81f9-e3efa41233d0},\n group_id = {c9329632-2a50-3043-b803-cadc8dbdfc3f},\n last_modified = {2021-04-09T15:24:23.533Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Particle image velocimetry (PIV) is an experimental technique used to measure fluid flow fields. However, PIV fields often have spurious and missing velocity vectors that degrade subsequent analyses. Standard post-processing involves the identification and replacement of outliers based on local information. We present a method to identify and fill in erroneous or missing PIV vectors using global information via robust principal component analysis (RPCA), a statistical technique developed for outlier rejection. RPCA decomposes a data matrix into a low-rank matrix containing coherent structures and a sparse matrix of outliers. We explore RPCA on a range of fluid simulations and experiments of varying complexity. First, we analyze direct numerical simulations of flow past a circular cylinder at Reynolds number 100 with artificially added outliers, alongside similar PIV measurements at Reynolds number 413. Next, we apply RPCA to a turbulent channel flow simulation from the Johns Hopkins Turbulence database, demonstrating that dominant coherent structures are maintained and the turbulent kinetic energy spectrum remains largely intact. Finally, we investigate PIV measurements behind a two-bladed cross-flow turbine that exhibits both broadband and coherent phenomena. In all cases, we find that RPCA extracts dominant fluid coherent structures and identifies and fills in corruption and outliers, with minimal degradation of small scale structures.},\n bibtype = {article},\n author = {Scherl, Isabel and Strom, Benjamin and Shang, Jessica K and Williams, Owen and Polagye, Brian L and Brunton, Steven L}\n}","author_short":["Scherl, I.","Strom, B.","Shang, J., K.","Williams, O.","Polagye, B., L.","Brunton, S., L."],"urls":{"Website":"http://arxiv.org/abs/1905.07062"},"biburl":"https://bibbase.org/service/mendeley/75799766-8e2d-3c98-81f9-e3efa41233d0","bibbaseid":"scherl-strom-shang-williams-polagye-brunton-robustprincipalcomponentanalysisforparticleimagevelocimetry-2019","role":"author","metadata":{"authorlinks":{}},"downloads":3},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/75799766-8e2d-3c98-81f9-e3efa41233d0","creationDate":"2019-09-10T22:29:16.427Z","downloads":3,"keywords":[],"search_terms":["robust","principal","component","analysis","particle","image","velocimetry","scherl","strom","shang","williams","polagye","brunton"],"title":"Robust Principal Component Analysis for Particle Image Velocimetry","year":2019,"dataSources":["hY2XmpNnxf2BGtC9Z","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}