Modeling for Concise Space Mission Utility Simulation with Apollo as Exemplar
Abstract Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing st...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Watson, Ja’Mar A. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© American Astronautical Society 2019 |
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| Übergeordnetes Werk: |
Enthalten in: The journal of the astronautical sciences - Springer US, 1958, 66(2019), 4 vom: 22. Mai, Seite 404-418 |
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| Übergeordnetes Werk: |
volume:66 ; year:2019 ; number:4 ; day:22 ; month:05 ; pages:404-418 |
| Links: |
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| DOI / URN: |
10.1007/s40295-019-00174-3 |
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OLC2094905373 |
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10.1007/s40295-019-00174-3 doi (DE-627)OLC2094905373 (DE-He213)s40295-019-00174-3-p DE-627 ger DE-627 rakwb eng 620 VZ Watson, Ja’Mar A. verfasserin (orcid)0000-0002-3592-6300 aut Modeling for Concise Space Mission Utility Simulation with Apollo as Exemplar 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © American Astronautical Society 2019 Abstract Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation. Mission utility simulation Space mission engineering Surrogate modeling Apollo missions Progspexion Enthalten in The journal of the astronautical sciences Springer US, 1958 66(2019), 4 vom: 22. Mai, Seite 404-418 (DE-627)12935905X (DE-600)160505-7 (DE-576)014731371 0021-9142 nnns volume:66 year:2019 number:4 day:22 month:05 pages:404-418 https://doi.org/10.1007/s40295-019-00174-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-AST SSG-OPC-AST GBV_ILN_70 GBV_ILN_2018 AR 66 2019 4 22 05 404-418 |
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10.1007/s40295-019-00174-3 doi (DE-627)OLC2094905373 (DE-He213)s40295-019-00174-3-p DE-627 ger DE-627 rakwb eng 620 VZ Watson, Ja’Mar A. verfasserin (orcid)0000-0002-3592-6300 aut Modeling for Concise Space Mission Utility Simulation with Apollo as Exemplar 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © American Astronautical Society 2019 Abstract Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation. Mission utility simulation Space mission engineering Surrogate modeling Apollo missions Progspexion Enthalten in The journal of the astronautical sciences Springer US, 1958 66(2019), 4 vom: 22. Mai, Seite 404-418 (DE-627)12935905X (DE-600)160505-7 (DE-576)014731371 0021-9142 nnns volume:66 year:2019 number:4 day:22 month:05 pages:404-418 https://doi.org/10.1007/s40295-019-00174-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-AST SSG-OPC-AST GBV_ILN_70 GBV_ILN_2018 AR 66 2019 4 22 05 404-418 |
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10.1007/s40295-019-00174-3 doi (DE-627)OLC2094905373 (DE-He213)s40295-019-00174-3-p DE-627 ger DE-627 rakwb eng 620 VZ Watson, Ja’Mar A. verfasserin (orcid)0000-0002-3592-6300 aut Modeling for Concise Space Mission Utility Simulation with Apollo as Exemplar 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © American Astronautical Society 2019 Abstract Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation. Mission utility simulation Space mission engineering Surrogate modeling Apollo missions Progspexion Enthalten in The journal of the astronautical sciences Springer US, 1958 66(2019), 4 vom: 22. Mai, Seite 404-418 (DE-627)12935905X (DE-600)160505-7 (DE-576)014731371 0021-9142 nnns volume:66 year:2019 number:4 day:22 month:05 pages:404-418 https://doi.org/10.1007/s40295-019-00174-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-AST SSG-OPC-AST GBV_ILN_70 GBV_ILN_2018 AR 66 2019 4 22 05 404-418 |
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Abstract Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation. © American Astronautical Society 2019 |
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Abstract Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation. © American Astronautical Society 2019 |
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Abstract Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation. © American Astronautical Society 2019 |
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