Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region

A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to te...
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Autor*in:	Li, Hao [verfasserIn] Yu, Ganglin [verfasserIn] Huang, Shanfang [verfasserIn] Wang, Kan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC

Übergeordnetes Werk:	Enthalten in: Annals of nuclear energy - Amsterdam [u.a.] : Elsevier Science, 1975, 159
Übergeordnetes Werk:	volume:159

DOI / URN:	10.1016/j.anucene.2021.108329

Katalog-ID:	ELV006105432

Internformat


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245	1	0	\|a Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region
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520			\|a A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature.
650		4	\|a Temperature Perturbation
650		4	\|a Reactivity coefficient
650		4	\|a Derivative
650		4	\|a Monte carlo
650		4	\|a RMC
700	1		\|a Yu, Ganglin \|e verfasserin \|4 aut
700	1		\|a Huang, Shanfang \|e verfasserin \|4 aut
700	1		\|a Wang, Kan \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Annals of nuclear energy \|d Amsterdam [u.a.] : Elsevier Science, 1975 \|g 159 \|h Online-Ressource \|w (DE-627)320406679 \|w (DE-600)2000768-1 \|w (DE-576)120883511 \|x 0306-4549 \|7 nnns
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912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
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912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 33.00 \|j Physik: Allgemeines
936	b	k	\|a 52.55 \|j Kerntechnik \|j Reaktortechnik
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author_variant	h l hl g y gy s h sh k w kw
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bklnumber	33.00 52.55
publishDate	2021
allfields	10.1016/j.anucene.2021.108329 doi (DE-627)ELV006105432 (ELSEVIER)S0306-4549(21)00205-X DE-627 ger DE-627 rda eng 530 DE-600 33.00 bkl 52.55 bkl Li, Hao verfasserin aut Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature. Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC Yu, Ganglin verfasserin aut Huang, Shanfang verfasserin aut Wang, Kan verfasserin aut Enthalten in Annals of nuclear energy Amsterdam [u.a.] : Elsevier Science, 1975 159 Online-Ressource (DE-627)320406679 (DE-600)2000768-1 (DE-576)120883511 0306-4549 nnns volume:159 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 33.00 Physik: Allgemeines 52.55 Kerntechnik Reaktortechnik AR 159
spelling	10.1016/j.anucene.2021.108329 doi (DE-627)ELV006105432 (ELSEVIER)S0306-4549(21)00205-X DE-627 ger DE-627 rda eng 530 DE-600 33.00 bkl 52.55 bkl Li, Hao verfasserin aut Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature. Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC Yu, Ganglin verfasserin aut Huang, Shanfang verfasserin aut Wang, Kan verfasserin aut Enthalten in Annals of nuclear energy Amsterdam [u.a.] : Elsevier Science, 1975 159 Online-Ressource (DE-627)320406679 (DE-600)2000768-1 (DE-576)120883511 0306-4549 nnns volume:159 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 33.00 Physik: Allgemeines 52.55 Kerntechnik Reaktortechnik AR 159
allfields_unstemmed	10.1016/j.anucene.2021.108329 doi (DE-627)ELV006105432 (ELSEVIER)S0306-4549(21)00205-X DE-627 ger DE-627 rda eng 530 DE-600 33.00 bkl 52.55 bkl Li, Hao verfasserin aut Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature. Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC Yu, Ganglin verfasserin aut Huang, Shanfang verfasserin aut Wang, Kan verfasserin aut Enthalten in Annals of nuclear energy Amsterdam [u.a.] : Elsevier Science, 1975 159 Online-Ressource (DE-627)320406679 (DE-600)2000768-1 (DE-576)120883511 0306-4549 nnns volume:159 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 33.00 Physik: Allgemeines 52.55 Kerntechnik Reaktortechnik AR 159
allfieldsGer	10.1016/j.anucene.2021.108329 doi (DE-627)ELV006105432 (ELSEVIER)S0306-4549(21)00205-X DE-627 ger DE-627 rda eng 530 DE-600 33.00 bkl 52.55 bkl Li, Hao verfasserin aut Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature. Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC Yu, Ganglin verfasserin aut Huang, Shanfang verfasserin aut Wang, Kan verfasserin aut Enthalten in Annals of nuclear energy Amsterdam [u.a.] : Elsevier Science, 1975 159 Online-Ressource (DE-627)320406679 (DE-600)2000768-1 (DE-576)120883511 0306-4549 nnns volume:159 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 33.00 Physik: Allgemeines 52.55 Kerntechnik Reaktortechnik AR 159
allfieldsSound	10.1016/j.anucene.2021.108329 doi (DE-627)ELV006105432 (ELSEVIER)S0306-4549(21)00205-X DE-627 ger DE-627 rda eng 530 DE-600 33.00 bkl 52.55 bkl Li, Hao verfasserin aut Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature. Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC Yu, Ganglin verfasserin aut Huang, Shanfang verfasserin aut Wang, Kan verfasserin aut Enthalten in Annals of nuclear energy Amsterdam [u.a.] : Elsevier Science, 1975 159 Online-Ressource (DE-627)320406679 (DE-600)2000768-1 (DE-576)120883511 0306-4549 nnns volume:159 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 33.00 Physik: Allgemeines 52.55 Kerntechnik Reaktortechnik AR 159
language	English
source	Enthalten in Annals of nuclear energy 159 volume:159
sourceStr	Enthalten in Annals of nuclear energy 159 volume:159
format_phy_str_mv	Article
bklname	Physik: Allgemeines Kerntechnik Reaktortechnik
institution	findex.gbv.de
topic_facet	Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC
dewey-raw	530
isfreeaccess_bool	false
container_title	Annals of nuclear energy
authorswithroles_txt_mv	Li, Hao @@aut@@ Yu, Ganglin @@aut@@ Huang, Shanfang @@aut@@ Wang, Kan @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320406679
dewey-sort	3530
id	ELV006105432
language_de	englisch
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author	Li, Hao
spellingShingle	Li, Hao ddc 530 bkl 33.00 bkl 52.55 misc Temperature Perturbation misc Reactivity coefficient misc Derivative misc Monte carlo misc RMC Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region
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topic_title	530 DE-600 33.00 bkl 52.55 bkl Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region Temperature Perturbation Reactivity coefficient Derivative Monte carlo RMC
topic	ddc 530 bkl 33.00 bkl 52.55 misc Temperature Perturbation misc Reactivity coefficient misc Derivative misc Monte carlo misc RMC
topic_unstemmed	ddc 530 bkl 33.00 bkl 52.55 misc Temperature Perturbation misc Reactivity coefficient misc Derivative misc Monte carlo misc RMC
topic_browse	ddc 530 bkl 33.00 bkl 52.55 misc Temperature Perturbation misc Reactivity coefficient misc Derivative misc Monte carlo misc RMC
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title	Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region
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title_full	Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region
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author_browse	Li, Hao Yu, Ganglin Huang, Shanfang Wang, Kan
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title_sort	temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region
title_auth	Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region
abstract	A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature.
abstractGer	A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature.
abstract_unstemmed	A negative fuel temperature reactivity coefficient is an important reactor physical parameter that indicates the inherent reactor operating safety. However, existing Monte Carlo perturbation methods still cannot accurately and efficiently predict the derivative of the k-eigenvalue with respect to temperature. The main difficulty lies in calculating the derivatives of the microscopic cross-section with respect to the temperature. This paper presents a Temperature Perturbation Method (TPM) that incorporates on-the-fly cross-section treatment as well as the free gas model into the differential operator method to calculate derivatives of the k-eigenvalue with respect to fuel temperature. TPM was then used to predict the k-eigenvalue at various temperatures for the Mosteller pin model. The differences between TPM and the direct difference method are less than 3% within 3 times of standard deviation. The results indicate that TPM can accurately and efficiently estimate the derivatives of the k-eigenvalue with respect to the fuel temperature.
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title_short	Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region
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doi_str	10.1016/j.anucene.2021.108329
up_date	2024-07-06T20:14:32.999Z
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Temperature perturbation method using on-the-fly treatment of the cross-sections in the resolved resonance region

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