2017 Fiscal Year Final Research Report
Construction of novel principle for knowledge discovery in particle methods for fluid dynamics using statistical models
| Project/Area Number |
15H05303
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Single-year Grants |
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| Research Field |
Statistical science
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| Research Institution | Meiji University |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Keywords | 粒子法 / データ同化 / 統計モデル |
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| Outline of Final Research Achievements |
In the particle methods for fluid analysis, fluids are represented by many particles and analyzed. In this study, we constructed the framework and principles for the error of the particle methods in the form of distribution through construction of estimation method of prediction errors, measurements of water tank experiments, and evaluation of statistical error. Especially, we obtained the effectiveness of bounded Gaussian and uniform mixture distribution for error model of macroscopic parameters and the effectiveness of the use of heavy-tailed distributions for error distribution. In addition, we obtained visualization results in which we can easily confirm key physical quantities and check the validity of the analysis. We also obtained evaluation results of errors in stochastic cellular automata model and relationship between local noise sensitivity and particle methods analysis.
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| Free Research Field |
統計科学
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