Quantifying air–water turbulence with moment field equations
Energy transfer in turbulent fluids is non-Gaussian. We quantify non-Gaussian energy transfer between the atmosphere and bodies of water using a turbulent diffusion operator coupled with temporally self-affine velocity distributions and a recursive integration method that produce multifractal measur...
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Veröffentlicht in: | Journal of fluid mechanics 2021-04, Vol.917, Article A39 |
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description | Energy transfer in turbulent fluids is non-Gaussian. We quantify non-Gaussian energy transfer between the atmosphere and bodies of water using a turbulent diffusion operator coupled with temporally self-affine velocity distributions and a recursive integration method that produce multifractal measures. The measures serve as input to a system of moment field equations (derived from Navier–Stokes) that generate and track high-frequency gravity waves that propagate through the water surface (as a result of the air–water interactions). The dimension of the support of the air–water turbulence produced by our methods falls within the range of theory and observation, and correspondingly, hindcast statistical measures of the water-wave surface such as significant water-wave height and wave period are well correlated to observational buoy data. Further, our recursive integration method can be used by spectral resolving phase-averaged models to interpolate temporal wind data to smaller scales to capture the non-Gaussian behaviour of the air–water interaction. |
doi_str_mv | 10.1017/jfm.2021.242 |
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We quantify non-Gaussian energy transfer between the atmosphere and bodies of water using a turbulent diffusion operator coupled with temporally self-affine velocity distributions and a recursive integration method that produce multifractal measures. The measures serve as input to a system of moment field equations (derived from Navier–Stokes) that generate and track high-frequency gravity waves that propagate through the water surface (as a result of the air–water interactions). The dimension of the support of the air–water turbulence produced by our methods falls within the range of theory and observation, and correspondingly, hindcast statistical measures of the water-wave surface such as significant water-wave height and wave period are well correlated to observational buoy data. 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Published by Cambridge University Press</rights><rights>The Author(s), 2021. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). 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Fluid Mech</addtitle><description>Energy transfer in turbulent fluids is non-Gaussian. We quantify non-Gaussian energy transfer between the atmosphere and bodies of water using a turbulent diffusion operator coupled with temporally self-affine velocity distributions and a recursive integration method that produce multifractal measures. The measures serve as input to a system of moment field equations (derived from Navier–Stokes) that generate and track high-frequency gravity waves that propagate through the water surface (as a result of the air–water interactions). The dimension of the support of the air–water turbulence produced by our methods falls within the range of theory and observation, and correspondingly, hindcast statistical measures of the water-wave surface such as significant water-wave height and wave period are well correlated to observational buoy data. Further, our recursive integration method can be used by spectral resolving phase-averaged models to interpolate temporal wind data to smaller scales to capture the non-Gaussian behaviour of the air–water interaction.</description><subject>Aerodynamics</subject><subject>Air</subject><subject>Air-water interactions</subject><subject>Atmospheric models</subject><subject>Buoys</subject><subject>Computational fluid dynamics</subject><subject>Cyclones</subject><subject>Dimensions</subject><subject>Eddy diffusion</subject><subject>Energy</subject><subject>Energy transfer</subject><subject>Fluid flow</subject><subject>Fluids</subject><subject>Gravity waves</subject><subject>Hurricanes</subject><subject>Integration</subject><subject>JFM Papers</subject><subject>Partial differential equations</subject><subject>Rain</subject><subject>Random variables</subject><subject>Statistical analysis</subject><subject>Tidal waves</subject><subject>Turbulence</subject><subject>Turbulent diffusion</subject><subject>Vortices</subject><subject>Water</subject><subject>Water waves</subject><subject>Wave height</subject><subject>Wave period</subject><subject>Wind data</subject><issn>0022-1120</issn><issn>1469-7645</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>IKXGN</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNptkM1KxDAUhYMoOI7ufICCW1tvkjZtNoIM_sGACLoOaXozZpi2M0nKMDvfwTf0SewwghtXZ3G_ew58hFxSyCjQ8mZp24wBoxnL2RGZ0FzItBR5cUwmAIyllDI4JWchLAEoB1lOyO3roLvo7M51i0Q7__35tdURfRIHXw8r7AwmWxc_krZvsYuJdbhqEtwMOrq-C-fkxOpVwIvfnJL3h_u32VM6f3l8nt3NU8NziCnXTUVpLhtdGJBciqKS1jQoKwBtbSEoFsbmvDGSoaiBSW1HSgiwNR_vfEquDr1r328GDFEt-8F346RiBZWMVyXbU9cHyvg-BI9Wrb1rtd8pCmpvSI2G1N6QGg2NePaL67b2rlngX-u_Dz8hqmlm</recordid><startdate>20210429</startdate><enddate>20210429</enddate><creator>Conroy, Colton J.</creator><creator>Mandli, Kyle T.</creator><creator>Kubatko, Ethan J.</creator><general>Cambridge University Press</general><scope>IKXGN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TB</scope><scope>7U5</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>L7M</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0W</scope><orcidid>https://orcid.org/0000-0003-3018-5539</orcidid><orcidid>https://orcid.org/0000-0002-8267-5989</orcidid></search><sort><creationdate>20210429</creationdate><title>Quantifying air–water turbulence with moment field equations</title><author>Conroy, Colton J. ; 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Fluid Mech</addtitle><date>2021-04-29</date><risdate>2021</risdate><volume>917</volume><artnum>A39</artnum><issn>0022-1120</issn><eissn>1469-7645</eissn><abstract>Energy transfer in turbulent fluids is non-Gaussian. We quantify non-Gaussian energy transfer between the atmosphere and bodies of water using a turbulent diffusion operator coupled with temporally self-affine velocity distributions and a recursive integration method that produce multifractal measures. The measures serve as input to a system of moment field equations (derived from Navier–Stokes) that generate and track high-frequency gravity waves that propagate through the water surface (as a result of the air–water interactions). 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subjects | Aerodynamics Air Air-water interactions Atmospheric models Buoys Computational fluid dynamics Cyclones Dimensions Eddy diffusion Energy Energy transfer Fluid flow Fluids Gravity waves Hurricanes Integration JFM Papers Partial differential equations Rain Random variables Statistical analysis Tidal waves Turbulence Turbulent diffusion Vortices Water Water waves Wave height Wave period Wind data |
title | Quantifying air–water turbulence with moment field equations |
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