2023 Fiscal Year Final Research Report

Advanced Prediction Method of Indoor Natural Ventilation Flow using LES-based Domain Decomposition Technique

Research Project

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Project/Area Number	20H02311
Research Category	Grant-in-Aid for Scientific Research (B)
Allocation Type	Single-year Grants
Section	一般
Review Section	Basic Section 23020:Architectural environment and building equipment-related
Research Institution	Osaka University
Principal Investigator	Kobayashi Tomohiro 大阪大学, 大学院工学研究科, 教授 (90580952)
Co-Investigator(Kenkyū-buntansha)	山中俊夫大阪大学, 大学院工学研究科, 教授 (80182575) 山澤春菜大阪大学, 大学院工学研究科, 助教 (80982305) 小林典彰大阪大学, 大学院工学研究科, 技術職員 (60880656) 崔ナレ東洋大学, 理工学部, 助教 (10826481)
Project Period (FY)	2020-04-01 – 2024-03-31
Keywords	通風 / 自然換気 / CFD / LES / 領域分割法 / 風洞実験
Outline of Final Research Achievements	In recent years, natural ventilation has been increasingly introduced in the design of non-residential buildings in Japan for the purpose of energy conservation. The airflow properties and thermal environment inside a building could show various distributions depending on external conditions due to natural ventilation. However, it is not easy to predict this with high accuracy. In this study, the Domain Decomposition Technique (DDT) is proposed, which predicts indoor airflow distribution during natural ventilation based on the results of outdoor airflow analysis using Large Eddy Simulation (LES). The target of the study was a single-room model with a relatively simple opening shape. In addition to isothermal conditions, it was also studied under non-isothermal conditions. Finally, it iwas confirmed that the indoor airflow could be well predicted except some special conditions such as the opening position being close to the ground.
Free Research Field	建築環境工学
Academic Significance and Societal Importance of the Research Achievements	本研究では比較期高い精度で気流を予測するLESを用いて、屋外気流場の予測結果から通風時の室内気流を予測する手法を提案した。同様の目的で屋外に仮想的な面を設けて結果を取得して解析領域を狭めた計算の境界条件に用いるネスティング技術も考えうるが、それでは取得する境界条件が膨大になりLES計算に適用するのは現実的に容易ではない。当該手法は各開口部につき一点のみで室内解析用の境界条件を取得することから、ビル風解析や構造解析等他の目的で設計初期段階に実施される屋外気流解析の実施時に容易にデータを取得することができる。そのため、実用的な簡便さをもって比較的高精度で室内気流が予測可能な手法の提案と考える。