Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment
Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To thi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kim, Taewung [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© KSAE 2023 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of automotive technology - Berlin : Springer, 2008, 24(2023), 3 vom: 12. Mai, Seite 633-642 |
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| Übergeordnetes Werk: |
volume:24 ; year:2023 ; number:3 ; day:12 ; month:05 ; pages:633-642 |
| Links: |
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| DOI / URN: |
10.1007/s12239-023-0053-2 |
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| Katalog-ID: |
SPR052437841 |
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| 520 | |a Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. | ||
| 700 | 1 | |a Song, Kihoon |4 aut | |
| 700 | 1 | |a Hong, Seok-ho |4 aut | |
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10.1007/s12239-023-0053-2 doi (DE-627)SPR052437841 (SPR)s12239-023-0053-2-e DE-627 ger DE-627 rakwb eng Kim, Taewung verfasserin aut Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSAE 2023 Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. Song, Kihoon aut Hong, Seok-ho aut Enthalten in International journal of automotive technology Berlin : Springer, 2008 24(2023), 3 vom: 12. Mai, Seite 633-642 (DE-627)565515268 (DE-600)2423925-2 1976-3832 nnns volume:24 year:2023 number:3 day:12 month:05 pages:633-642 https://dx.doi.org/10.1007/s12239-023-0053-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 3 12 05 633-642 |
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10.1007/s12239-023-0053-2 doi (DE-627)SPR052437841 (SPR)s12239-023-0053-2-e DE-627 ger DE-627 rakwb eng Kim, Taewung verfasserin aut Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSAE 2023 Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. Song, Kihoon aut Hong, Seok-ho aut Enthalten in International journal of automotive technology Berlin : Springer, 2008 24(2023), 3 vom: 12. Mai, Seite 633-642 (DE-627)565515268 (DE-600)2423925-2 1976-3832 nnns volume:24 year:2023 number:3 day:12 month:05 pages:633-642 https://dx.doi.org/10.1007/s12239-023-0053-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 3 12 05 633-642 |
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10.1007/s12239-023-0053-2 doi (DE-627)SPR052437841 (SPR)s12239-023-0053-2-e DE-627 ger DE-627 rakwb eng Kim, Taewung verfasserin aut Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSAE 2023 Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. Song, Kihoon aut Hong, Seok-ho aut Enthalten in International journal of automotive technology Berlin : Springer, 2008 24(2023), 3 vom: 12. Mai, Seite 633-642 (DE-627)565515268 (DE-600)2423925-2 1976-3832 nnns volume:24 year:2023 number:3 day:12 month:05 pages:633-642 https://dx.doi.org/10.1007/s12239-023-0053-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 3 12 05 633-642 |
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10.1007/s12239-023-0053-2 doi (DE-627)SPR052437841 (SPR)s12239-023-0053-2-e DE-627 ger DE-627 rakwb eng Kim, Taewung verfasserin aut Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSAE 2023 Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. Song, Kihoon aut Hong, Seok-ho aut Enthalten in International journal of automotive technology Berlin : Springer, 2008 24(2023), 3 vom: 12. Mai, Seite 633-642 (DE-627)565515268 (DE-600)2423925-2 1976-3832 nnns volume:24 year:2023 number:3 day:12 month:05 pages:633-642 https://dx.doi.org/10.1007/s12239-023-0053-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 3 12 05 633-642 |
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10.1007/s12239-023-0053-2 doi (DE-627)SPR052437841 (SPR)s12239-023-0053-2-e DE-627 ger DE-627 rakwb eng Kim, Taewung verfasserin aut Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © KSAE 2023 Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. Song, Kihoon aut Hong, Seok-ho aut Enthalten in International journal of automotive technology Berlin : Springer, 2008 24(2023), 3 vom: 12. Mai, Seite 633-642 (DE-627)565515268 (DE-600)2423925-2 1976-3832 nnns volume:24 year:2023 number:3 day:12 month:05 pages:633-642 https://dx.doi.org/10.1007/s12239-023-0053-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 3 12 05 633-642 |
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Kim, Taewung |
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Kim, Taewung Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment |
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Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment |
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estimation of whole-body injury metrics for evaluating effect of airbag deployment |
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Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment |
| abstract |
Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. © KSAE 2023 |
| abstractGer |
Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. © KSAE 2023 |
| abstract_unstemmed |
Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems. © KSAE 2023 |
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| up_date |
2024-07-04T02:48:24.559Z |
| _version_ |
1803615002493452288 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR052437841</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230726103807.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230726s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12239-023-0053-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR052437841</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12239-023-0053-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Taewung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Estimation of Whole-Body Injury Metrics for Evaluating Effect of Airbag Deployment</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© KSAE 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The current study aimed to develop a method for estimating whole-body Injury metric values (WBIMs), which are widely used in epidemiological studies, to determine airbag deployment threshold during frontal crashes using a computational human surrogate model and real-world crash data. To this end, a finite element human body model was instrumented to predict the risk of the injuries. The whole body was divided into 22 body regions. Then, body region-specific injury pattern databases were constructed for these body regions using the NASS-CDS database. Monte Carlo Sampling was performed to calculate WBIMs, such as the probability of death and lost years of life. A series of frontal crash simulations was performed for various Delta-V with and without deploying pyrotechnic restraint systems. Lastly, the WBIM values obtained from the proposed method were compared to those obtained from the NASS-CDS. From the Delta-V of 25 km/h, the airbag-deployed conditions demonstrated a protective effect compared to the non-deployed conditions. The predicted WBIM values using the proposed method demonstrated a similar trend to that presented in the field data. The proposed method to estimate WBIM values can be used to evaluate various occupant protection systems.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Kihoon</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hong, Seok-ho</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of automotive technology</subfield><subfield code="d">Berlin : Springer, 2008</subfield><subfield code="g">24(2023), 3 vom: 12. 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