Development of prediction method for drifting snow environments by coupling computational fluid dynamics with Lagrangian particle transport model

2021 Fiscal Year Final Research Report

• Project/Area Number: 18H01592
• 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: Tokyo Institute of Technology
• Principal Investigator: Okaze Tsubasa 東京工業大学, 環境・社会理工学院, 准教授 (40709739)
• Co-Investigator(Kenkyū-buntansha): 大宮 哲 国立研究開発法人土木研究所, 土木研究所(寒地土木研究所), 研究員 (60718451) ; 新屋 啓文 新潟大学, 研究推進機構, 助教 (80794982)
• Project Period (FY): 2018-04-01 – 2022-03-31
• Keywords: 吹雪 / 飛雪 / 数値流体解析 / 粒子飛散 / 吹きだまり / 風洞実験 / 野外観測 / 境界層

Outline of Final Research Achievements

In this study, we developed a new drifting snow model that can predict unsteady phenomena of drifting by coupling a large-eddy simulation, which can accurately predict turbulent fluctuations, with a Lagrangian particle model that predict the motion of individual particles. To validate the drifting snow model, wind tunnel experiments and field observations in a snowfield were conducted to obtain statistics of wind speed and the mass flux of drifting snow. A meso-meteorological simulation was conducted to discuss observation site, and it was decided in Teshikaga, Hokkaido, where wind direction is stable and strong winds blow during the winter season. Based on comparisons of wind tunnel experiments and field observations, the accuracy of the developed drifting snow model was validated.

Free Research Field

都市・建築環境

Academic Significance and Societal Importance of the Research Achievements

強風下で、降雪や再飛散した雪を伴う風を吹雪と呼ぶ。乱流変動に伴い、瞬間的に視程が大きく低下するほか、局所的な吹きだまりが形成され、建物周辺では、流れ場が3次元的に大きく変化することから、より複雑な問題となる。現状、数値流体解析に基づく平均風速分布の予測を基礎とした積雪分布の予測モデルが提案されているが、本研究で提案した非定常現象が予測可能な飛雪モデルにより、非定常的な乱流変動と雪の飛散メカニズムの分析が可能となると考えられ、突風など短い時間での地吹雪による視程の低下や吹きだまりの形成などの対策への貢献が期待できる。