| Project/Area Number |
18K04470
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 23020:Architectural environment and building equipment-related
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| Research Institution | Osaka Institute of Technology |
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Principal Investigator |
KONO Ryohei 大阪工業大学, 工学部, 准教授 (90572222)
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| Co-Investigator(Kenkyū-buntansha) |
桃井 良尚 福井大学, 学術研究院工学系部門, 講師 (40506870)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | 遺伝的アルゴリズム / ＣＦＤ / 随伴変数法 / 逆解析 / 探査効率向上 / ＣＦＤ解析 / 通風開口 / 最適配置 / 建物周辺気流 / 形状最適化 / 最適化 / 住宅設計 |
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| Outline of Final Research Achievements |
In this study, a method is proposed to apply the adjoined variable method instead of the conventional "crossover" for GA that treats CFD analysis as an evaluation function. From the adjoined variable method that can calculate the gradients of all design variables in one analysis, a gene sequence (design variable) with a high possibility is created. By always adopting a gene sequence that will be even better than the best gene sequence of each generation as a competitor, it is tried to reach the optimum solution with a smaller number of generations than conventional GA. The proposed method was applied to the following two points. At the same time, a basic study on the contingent variable method is also conducted.
I. "Optimization of obstacle placement for the purpose of reducing strong winds outdoors"
Ⅱ. "Optimization of opening arrangement for the purpose of improving ventilation performance indoors"
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| Academic Significance and Societal Importance of the Research Achievements |
複数のGAロジックを試行した結果、設計変数の変化に対する物理量変化（勾配）の比を算出可能な随伴変数法を用いて交叉解を生成した場合では、最適解到達世代数が減少して探査効率が向上した点を示したことに学術的意義がある。併せて、随伴変数法は現状の解に近い局所的最適解を導く手法であるが、GAの一部として用いることで大局的最適解の導出に寄与することが可能となる点にも学術的意義がある。
また、GAの高速化が可能となることで、実設計においても従来では考えられないケース数の計算を実行した場合と同等の最適解の算出が可能となり、新たな「Computational Design」の実現が可能となる点に社会的意義がある。
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