| Project/Area Number |
23KJ0594
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund |
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| Section | 国内 |
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| Review Section |
Basic Section 27010:Transport phenomena and unit operations-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SHI QI 東京大学, 工学系研究科, 特別研究員(DC2)
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| Project Period (FY) |
2023-04-25 – 2024-03-31
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| Project Status |
Discontinued (Fiscal Year 2023)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2024: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2023: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | powder mixing / data-driven method / discrete element method |
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| Outline of Research at the Start |
By combining data mining techniques with numerical studies, proper orthogonal decomposition (POD)-analysis of variance (ANOVA) is established and realizes the quantification of powder mixing mechanisms. POD-based reduced order modeling will conduct efficient parametrization of the particle systems.
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| Outline of Annual Research Achievements |
This research project, conducted over the past year, has developed and applied advanced mathematical-analytical methods for a detailed analysis of powder mixing processes. The project successfully established a novel proper orthogonal decomposition (POD)-analysis of variance (ANOVA) framework, tailored for discrete element method (DEM) simulations in Lagrangian particle systems. This innovative approach was crucial in quantifying convection and diffusion in powder mixing processes, leading to a deeper understanding of the complex interactions within various mixing systems.
First, the POD-ANOVA method was rigorously tested and validated using a benchmark rolling drum system. This validation demonstrated the method's effectiveness in simpler systems. The approach was then extended to more complex industrial container blenders, showcasing its versatility and robustness in analyzing complex mixing mechanisms influenced by airflow. The later period focused on establishing a time-localized reduced order modeling (ROM) strategy, which was applied to a benchmark rolling drum system. This strategy led to efficient reconstruction of particle flows (particle positions and velocities) within the rolling mixer. This model significantly improved simulation accuracy while reducing computational costs and time, as compared to the conventional standard ROM approach. The findings and methodologies developed in this study have been published in high-quality international journals and presented at numerous prestigious conferences.
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