Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning

At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a...
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Autor*in:	Shi, Xinhuan [verfasserIn] Li, Zhongchun [verfasserIn] Wang, Jinyu [verfasserIn] Chai, Xiaoming [verfasserIn] Chen, Wei [verfasserIn] Chyu, Minking K. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction

Übergeordnetes Werk:	Enthalten in: Applied thermal engineering - Amsterdam [u.a.] : Elsevier Science, 1996, 240
Übergeordnetes Werk:	volume:240

DOI / URN:	10.1016/j.applthermaleng.2023.122244

Katalog-ID:	ELV066637074

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520			\|a At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at .
650		4	\|a Supercritical CO
650		4	\|a Inclined tubes
650		4	\|a Numerical method
650		4	\|a Deep learning
650		4	\|a Rapid 2D prediction
700	1		\|a Li, Zhongchun \|e verfasserin \|4 aut
700	1		\|a Wang, Jinyu \|e verfasserin \|4 aut
700	1		\|a Chai, Xiaoming \|e verfasserin \|4 aut
700	1		\|a Chen, Wei \|e verfasserin \|4 aut
700	1		\|a Chyu, Minking K. \|e verfasserin \|4 aut
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allfields	10.1016/j.applthermaleng.2023.122244 doi (DE-627)ELV066637074 (ELSEVIER)S1359-4311(23)02273-1 DE-627 ger DE-627 rda eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Shi, Xinhuan verfasserin aut Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at . Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction Li, Zhongchun verfasserin aut Wang, Jinyu verfasserin aut Chai, Xiaoming verfasserin aut Chen, Wei verfasserin aut Chyu, Minking K. verfasserin aut Enthalten in Applied thermal engineering Amsterdam [u.a.] : Elsevier Science, 1996 240 Online-Ressource (DE-627)320594122 (DE-600)2019322-1 (DE-576)256146322 1359-4311 nnns volume:240 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 240
spelling	10.1016/j.applthermaleng.2023.122244 doi (DE-627)ELV066637074 (ELSEVIER)S1359-4311(23)02273-1 DE-627 ger DE-627 rda eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Shi, Xinhuan verfasserin aut Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at . Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction Li, Zhongchun verfasserin aut Wang, Jinyu verfasserin aut Chai, Xiaoming verfasserin aut Chen, Wei verfasserin aut Chyu, Minking K. verfasserin aut Enthalten in Applied thermal engineering Amsterdam [u.a.] : Elsevier Science, 1996 240 Online-Ressource (DE-627)320594122 (DE-600)2019322-1 (DE-576)256146322 1359-4311 nnns volume:240 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 240
allfields_unstemmed	10.1016/j.applthermaleng.2023.122244 doi (DE-627)ELV066637074 (ELSEVIER)S1359-4311(23)02273-1 DE-627 ger DE-627 rda eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Shi, Xinhuan verfasserin aut Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at . Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction Li, Zhongchun verfasserin aut Wang, Jinyu verfasserin aut Chai, Xiaoming verfasserin aut Chen, Wei verfasserin aut Chyu, Minking K. verfasserin aut Enthalten in Applied thermal engineering Amsterdam [u.a.] : Elsevier Science, 1996 240 Online-Ressource (DE-627)320594122 (DE-600)2019322-1 (DE-576)256146322 1359-4311 nnns volume:240 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 240
allfieldsGer	10.1016/j.applthermaleng.2023.122244 doi (DE-627)ELV066637074 (ELSEVIER)S1359-4311(23)02273-1 DE-627 ger DE-627 rda eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Shi, Xinhuan verfasserin aut Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at . Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction Li, Zhongchun verfasserin aut Wang, Jinyu verfasserin aut Chai, Xiaoming verfasserin aut Chen, Wei verfasserin aut Chyu, Minking K. verfasserin aut Enthalten in Applied thermal engineering Amsterdam [u.a.] : Elsevier Science, 1996 240 Online-Ressource (DE-627)320594122 (DE-600)2019322-1 (DE-576)256146322 1359-4311 nnns volume:240 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 240
allfieldsSound	10.1016/j.applthermaleng.2023.122244 doi (DE-627)ELV066637074 (ELSEVIER)S1359-4311(23)02273-1 DE-627 ger DE-627 rda eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Shi, Xinhuan verfasserin aut Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at . Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction Li, Zhongchun verfasserin aut Wang, Jinyu verfasserin aut Chai, Xiaoming verfasserin aut Chen, Wei verfasserin aut Chyu, Minking K. verfasserin aut Enthalten in Applied thermal engineering Amsterdam [u.a.] : Elsevier Science, 1996 240 Online-Ressource (DE-627)320594122 (DE-600)2019322-1 (DE-576)256146322 1359-4311 nnns volume:240 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 240
language	English
source	Enthalten in Applied thermal engineering 240 volume:240
sourceStr	Enthalten in Applied thermal engineering 240 volume:240
format_phy_str_mv	Article
bklname	Kältetechnik Thermische Energieerzeugung Wärmetechnik Heizungstechnik Lüftungstechnik Klimatechnik Technische Thermodynamik
institution	findex.gbv.de
topic_facet	Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction
dewey-raw	690
isfreeaccess_bool	false
container_title	Applied thermal engineering
authorswithroles_txt_mv	Shi, Xinhuan @@aut@@ Li, Zhongchun @@aut@@ Wang, Jinyu @@aut@@ Chai, Xiaoming @@aut@@ Chen, Wei @@aut@@ Chyu, Minking K. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320594122
dewey-sort	3690
id	ELV066637074
language_de	englisch
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author	Shi, Xinhuan
spellingShingle	Shi, Xinhuan ddc 690 bkl 52.43 bkl 52.52 bkl 52.42 bkl 50.38 misc Supercritical CO misc Inclined tubes misc Numerical method misc Deep learning misc Rapid 2D prediction Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning
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topic_title	690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning Supercritical CO Inclined tubes Numerical method Deep learning Rapid 2D prediction
topic	ddc 690 bkl 52.43 bkl 52.52 bkl 52.42 bkl 50.38 misc Supercritical CO misc Inclined tubes misc Numerical method misc Deep learning misc Rapid 2D prediction
topic_unstemmed	ddc 690 bkl 52.43 bkl 52.52 bkl 52.42 bkl 50.38 misc Supercritical CO misc Inclined tubes misc Numerical method misc Deep learning misc Rapid 2D prediction
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title	Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning
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title_full	Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning
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doi_str_mv	10.1016/j.applthermaleng.2023.122244
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title_sort	rapid 2-dimensional prediction of supercritical co 2 heat transfer behaviors in inclined tubes based on deep learning
title_auth	Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning
abstract	At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at .
abstractGer	At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at .
abstract_unstemmed	At present, there is a dearth of empirical correlations for the prediction of the inclined supercritical heat transfer. Considering the shortage of the rapid prediction method in the initial design stages for the supercritical fluid heat exchanger with inclined tubes or under inclined conditions, a 2-Dimensional rapid prediction method based on the numerical method combining deep learning method was proposed to reconcile the accuracy and the efficiency of the inclined supercritical heat transfer behaviors prediction. In order to train the deep learning model, 200 numerical cases with 520,000 data were conducted. After that, 7 new cases were chosen to analyze the inclined heat transfer behaviors and validate the current model. The results were indicative of a significant circumferential inner wall temperature difference in the inclined supercritical heat transfer. The temperature difference was larger when the inclined angle was closer to 90°, even though the circumferentially averaged temperature is smooth and acceptable. This potential temperature difference would not only cause large thermal stress which may accelerate fatigue failure, but also increase the risk of over-temperature. Fortunately, the current deep learning model could predict the 2D temperature distribution of these new cases with the max absolute relative error of 16.60%. Therefore, the circumferential inner wall temperature distribution of the inclined supercritical CO2 heat transfer could be evaluated during the initial heat exchanger design. Moreover, the trained model and the relative files are available at .
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title_short	Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning
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author2	Li, Zhongchun Wang, Jinyu Chai, Xiaoming Chen, Wei Chyu, Minking K.
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up_date	2024-07-06T18:27:49.284Z
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Rapid 2-Dimensional prediction of supercritical CO 2 heat transfer behaviors in inclined tubes based on deep learning

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