Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory

Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to me...
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Autor*in:	Tsukahara, Takahiro [verfasserIn] Oyagi, Kana [verfasserIn] Kawaguchi, Yasuo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Channel flow High Schmidt number Inverse analysis PLIF Pollutant dispersion Source estimation Turbulence

Übergeordnetes Werk:	Enthalten in: Environmental fluid mechanics - Dordrecht [u.a.] : Springer Science + Business Media B.V, 2001, 16(2015), 3 vom: 21. Nov., Seite 521-537
Übergeordnetes Werk:	volume:16 ; year:2015 ; number:3 ; day:21 ; month:11 ; pages:521-537

Links:	Volltext

DOI / URN:	10.1007/s10652-015-9436-x

Katalog-ID:	SPR012271780

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245	1	0	\|a Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory
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520			\|a Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation.
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650		4	\|a Source estimation \|7 (dpeaa)DE-He213
650		4	\|a Turbulence \|7 (dpeaa)DE-He213
700	1		\|a Oyagi, Kana \|e verfasserin \|4 aut
700	1		\|a Kawaguchi, Yasuo \|e verfasserin \|4 aut
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author_variant	t t tt k o ko y k yk
matchkey_str	article:15731510:2015----::siainehdodniyclronsucitruetlwae
hierarchy_sort_str	2015
bklnumber	50.33 50.35 58.50
publishDate	2015
allfields	10.1007/s10652-015-9436-x doi (DE-627)SPR012271780 (SPR)s10652-015-9436-x-e DE-627 ger DE-627 rakwb eng 690 ASE 50.33 bkl 50.35 bkl 58.50 bkl Tsukahara, Takahiro verfasserin aut Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation. Channel flow (dpeaa)DE-He213 High Schmidt number (dpeaa)DE-He213 Inverse analysis (dpeaa)DE-He213 PLIF (dpeaa)DE-He213 Pollutant dispersion (dpeaa)DE-He213 Source estimation (dpeaa)DE-He213 Turbulence (dpeaa)DE-He213 Oyagi, Kana verfasserin aut Kawaguchi, Yasuo verfasserin aut Enthalten in Environmental fluid mechanics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2001 16(2015), 3 vom: 21. Nov., Seite 521-537 (DE-627)325610029 (DE-600)2037932-8 1573-1510 nnns volume:16 year:2015 number:3 day:21 month:11 pages:521-537 https://dx.doi.org/10.1007/s10652-015-9436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.33 ASE 50.35 ASE 58.50 ASE AR 16 2015 3 21 11 521-537
spelling	10.1007/s10652-015-9436-x doi (DE-627)SPR012271780 (SPR)s10652-015-9436-x-e DE-627 ger DE-627 rakwb eng 690 ASE 50.33 bkl 50.35 bkl 58.50 bkl Tsukahara, Takahiro verfasserin aut Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation. Channel flow (dpeaa)DE-He213 High Schmidt number (dpeaa)DE-He213 Inverse analysis (dpeaa)DE-He213 PLIF (dpeaa)DE-He213 Pollutant dispersion (dpeaa)DE-He213 Source estimation (dpeaa)DE-He213 Turbulence (dpeaa)DE-He213 Oyagi, Kana verfasserin aut Kawaguchi, Yasuo verfasserin aut Enthalten in Environmental fluid mechanics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2001 16(2015), 3 vom: 21. Nov., Seite 521-537 (DE-627)325610029 (DE-600)2037932-8 1573-1510 nnns volume:16 year:2015 number:3 day:21 month:11 pages:521-537 https://dx.doi.org/10.1007/s10652-015-9436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.33 ASE 50.35 ASE 58.50 ASE AR 16 2015 3 21 11 521-537
allfields_unstemmed	10.1007/s10652-015-9436-x doi (DE-627)SPR012271780 (SPR)s10652-015-9436-x-e DE-627 ger DE-627 rakwb eng 690 ASE 50.33 bkl 50.35 bkl 58.50 bkl Tsukahara, Takahiro verfasserin aut Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation. Channel flow (dpeaa)DE-He213 High Schmidt number (dpeaa)DE-He213 Inverse analysis (dpeaa)DE-He213 PLIF (dpeaa)DE-He213 Pollutant dispersion (dpeaa)DE-He213 Source estimation (dpeaa)DE-He213 Turbulence (dpeaa)DE-He213 Oyagi, Kana verfasserin aut Kawaguchi, Yasuo verfasserin aut Enthalten in Environmental fluid mechanics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2001 16(2015), 3 vom: 21. Nov., Seite 521-537 (DE-627)325610029 (DE-600)2037932-8 1573-1510 nnns volume:16 year:2015 number:3 day:21 month:11 pages:521-537 https://dx.doi.org/10.1007/s10652-015-9436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.33 ASE 50.35 ASE 58.50 ASE AR 16 2015 3 21 11 521-537
allfieldsGer	10.1007/s10652-015-9436-x doi (DE-627)SPR012271780 (SPR)s10652-015-9436-x-e DE-627 ger DE-627 rakwb eng 690 ASE 50.33 bkl 50.35 bkl 58.50 bkl Tsukahara, Takahiro verfasserin aut Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation. Channel flow (dpeaa)DE-He213 High Schmidt number (dpeaa)DE-He213 Inverse analysis (dpeaa)DE-He213 PLIF (dpeaa)DE-He213 Pollutant dispersion (dpeaa)DE-He213 Source estimation (dpeaa)DE-He213 Turbulence (dpeaa)DE-He213 Oyagi, Kana verfasserin aut Kawaguchi, Yasuo verfasserin aut Enthalten in Environmental fluid mechanics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2001 16(2015), 3 vom: 21. Nov., Seite 521-537 (DE-627)325610029 (DE-600)2037932-8 1573-1510 nnns volume:16 year:2015 number:3 day:21 month:11 pages:521-537 https://dx.doi.org/10.1007/s10652-015-9436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.33 ASE 50.35 ASE 58.50 ASE AR 16 2015 3 21 11 521-537
allfieldsSound	10.1007/s10652-015-9436-x doi (DE-627)SPR012271780 (SPR)s10652-015-9436-x-e DE-627 ger DE-627 rakwb eng 690 ASE 50.33 bkl 50.35 bkl 58.50 bkl Tsukahara, Takahiro verfasserin aut Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation. Channel flow (dpeaa)DE-He213 High Schmidt number (dpeaa)DE-He213 Inverse analysis (dpeaa)DE-He213 PLIF (dpeaa)DE-He213 Pollutant dispersion (dpeaa)DE-He213 Source estimation (dpeaa)DE-He213 Turbulence (dpeaa)DE-He213 Oyagi, Kana verfasserin aut Kawaguchi, Yasuo verfasserin aut Enthalten in Environmental fluid mechanics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2001 16(2015), 3 vom: 21. Nov., Seite 521-537 (DE-627)325610029 (DE-600)2037932-8 1573-1510 nnns volume:16 year:2015 number:3 day:21 month:11 pages:521-537 https://dx.doi.org/10.1007/s10652-015-9436-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.33 ASE 50.35 ASE 58.50 ASE AR 16 2015 3 21 11 521-537
language	English
source	Enthalten in Environmental fluid mechanics 16(2015), 3 vom: 21. Nov., Seite 521-537 volume:16 year:2015 number:3 day:21 month:11 pages:521-537
sourceStr	Enthalten in Environmental fluid mechanics 16(2015), 3 vom: 21. Nov., Seite 521-537 volume:16 year:2015 number:3 day:21 month:11 pages:521-537
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Channel flow High Schmidt number Inverse analysis PLIF Pollutant dispersion Source estimation Turbulence
dewey-raw	690
isfreeaccess_bool	false
container_title	Environmental fluid mechanics
authorswithroles_txt_mv	Tsukahara, Takahiro @@aut@@ Oyagi, Kana @@aut@@ Kawaguchi, Yasuo @@aut@@
publishDateDaySort_date	2015-11-21T00:00:00Z
hierarchy_top_id	325610029
dewey-sort	3690
id	SPR012271780
language_de	englisch
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author	Tsukahara, Takahiro
spellingShingle	Tsukahara, Takahiro ddc 690 bkl 50.33 bkl 50.35 bkl 58.50 misc Channel flow misc High Schmidt number misc Inverse analysis misc PLIF misc Pollutant dispersion misc Source estimation misc Turbulence Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory
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topic_title	690 ASE 50.33 bkl 50.35 bkl 58.50 bkl Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory Channel flow (dpeaa)DE-He213 High Schmidt number (dpeaa)DE-He213 Inverse analysis (dpeaa)DE-He213 PLIF (dpeaa)DE-He213 Pollutant dispersion (dpeaa)DE-He213 Source estimation (dpeaa)DE-He213 Turbulence (dpeaa)DE-He213
topic	ddc 690 bkl 50.33 bkl 50.35 bkl 58.50 misc Channel flow misc High Schmidt number misc Inverse analysis misc PLIF misc Pollutant dispersion misc Source estimation misc Turbulence
topic_unstemmed	ddc 690 bkl 50.33 bkl 50.35 bkl 58.50 misc Channel flow misc High Schmidt number misc Inverse analysis misc PLIF misc Pollutant dispersion misc Source estimation misc Turbulence
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title	Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory
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title_full	Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory
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title_sort	estimation method to identify scalar point source in turbulent flow based on taylor’s diffusion theory
title_auth	Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory
abstract	Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation.
abstractGer	Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation.
abstract_unstemmed	Abstract We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation.
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container_issue	3
title_short	Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory
url	https://dx.doi.org/10.1007/s10652-015-9436-x
remote_bool	true
author2	Oyagi, Kana Kawaguchi, Yasuo
author2Str	Oyagi, Kana Kawaguchi, Yasuo
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doi_str	10.1007/s10652-015-9436-x
up_date	2024-07-04T02:29:42.072Z
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Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory

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