| Project/Area Number |
13852012
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| Research Category |
Grant-in-Aid for Scientific Research (S)
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| Allocation Type | Single-year Grants |
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| Research Field |
Architectural environment/equipment
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| Research Institution | The University of Tokyo |
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Principal Investigator |
KATO Shinsuke The University of Tokyo, Institute of Industrial Science, Professor, 生産技術研究所, 教授 (00142240)
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| Co-Investigator(Kenkyū-buntansha) |
MURAKAMI Shuzo Keio University, Faculty of Science and Technology, Professor, 理工学部, 教授 (40013180)
OOKA Ryozo The University of Tokyo, Institute of Industrial Science, Associate Professor, 生産技術研究所, 助教授 (90251470)
HUANG Hong The University of Tokyo, Institute of Industrial Science, Research Associate, 生産技術研究所, 助手 (30376636)
宋 斗三 東京大学, 生産技術研究所, 助手 (40345129)
白石 靖幸 北九州市立大学, 国際環境工学部, 講師 (50302633)
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| Project Period (FY) |
2001 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥117,390,000 (Direct Cost: ¥90,300,000、Indirect Cost: ¥27,090,000)
Fiscal Year 2005: ¥12,350,000 (Direct Cost: ¥9,500,000、Indirect Cost: ¥2,850,000)
Fiscal Year 2004: ¥12,350,000 (Direct Cost: ¥9,500,000、Indirect Cost: ¥2,850,000)
Fiscal Year 2003: ¥18,590,000 (Direct Cost: ¥14,300,000、Indirect Cost: ¥4,290,000)
Fiscal Year 2002: ¥24,700,000 (Direct Cost: ¥19,000,000、Indirect Cost: ¥5,700,000)
Fiscal Year 2001: ¥49,400,000 (Direct Cost: ¥38,000,000、Indirect Cost: ¥11,400,000)
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| Keywords | CFD / Indoor thermal environment / Optimization system / FGA / Robust design / Indoor air quality / 室内温熱・空気環境の最適設計 / 最適化手法 / GA(Genetic Algorithms) / 温熱快適性 / 空気質 / 省エネルギー / 自動化設計支援システム / GA (Genetic Algorithms) / 逆問題解析 / フィードバックシステム / 最適設計 / 遺伝的アルゴリズム / 温熱環境 |
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| Research Abstract |
In this research we aimed to build an integrated thermal and air quality design system that uses inverse transform methods for indoor (and outdoor) thermal and air quality analysis simulations based on CFD (Computer Fluid Dynamics). With this aim we developed procedures, and more specifically used inverse problem analysis to develop an automatic optimized environmental design system, seeking physical boundary conditions that best approximate design target values of indoor (and outdoor) environmental conditions and behavior.
The following specific steps were undertaken ::
1.Modeling of design behavior :
We modeled the design behavior of designers by determining the hierarchical structure of design targets in the environmental design, determining the hierarchical structure of the design process, and building a feedback system into the design process.
2.Using CFD to build an integrated indoor thermal and air quality analysis system :
Using CFD coupled analysis of convection, radiation, and moi
… More
sture transportation in indoor locations of uneven temperature, velocity, radiation and humidity to quantitatively assess contributions to the indoor environment of each control factor.
3.Development of an optimal assessment system :
We developed a quantitative assessment procedure for optimized design of indoor environments. It exhibited multi-purpose decision-making logic for choosing the preferred solution to a multi-purpose optimization problem from a set of Pareto solutions.
4.Development of procedures for optimized design :
We introduced genetic algorithms (GA) to analyze inverse problems efficiently. In inverse analysis of CFD, CFD results are fed back into input changes and calculations repeated. Introducing GA into this process facilitates efficient inverse analysis. We also developed Stage 2 type optimized design procedures to reduce the calculation load in optimized inquiries that use GA. We also introduced dependability design and robust design procedures that take into account probability changes in design variables.
5.Creation of a proto-type optimized design system.
We applied the optimized design system developed in 1 to 4 above to indoor and outdoor thermal environment optimized design and investigated its effectiveness.
6.Completion of the optimized design system. Less
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