コーヒーを淹れることと乱流を理解することの意外なつながり(The Unexpected Connection Between Brewing Coffee and Understanding Turbulence)

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2024-06-03 カリフォルニア大学サンディエゴ校(UCSD)

1883年、オズボーン・レイノルズは、インクを水に注入して流れを観察し、速度が増すと流れが滑らかな層流から乱流へと変わることを発見しました。しかし、なぜこの変遷が起こるのかは不明でした。この問題を、カリフォルニア大学サンディエゴ校のナイジェル・ゴールデンフェルド教授とオーストリア科学技術研究所のビョルン・ホフが率いる国際研究チームが解決しました。彼らは統計力学を用い、乱流が起こる非平衡相転移が「有向浸透」で説明できることを示しました。彼らの研究は「Nature Physics」に掲載されました。この発見により、乱流と層流の遷移が理解され、乱流の予測が可能となりました。

<関連情報>

パイプ乱流への遷移に近い方向パーコレーションとパフジャミング Directed percolation and puff jamming near the transition to pipe turbulence

Grégoire Lemoult,Vasudevan Mukund,Hong-Yan Shih,Gaute Linga,Joachim Mathiesen,Nigel Goldenfeld & Björn Hof
Nature Physics  Published:27 May 2024
DOI:https://doi.org/10.1038/s41567-024-02513-0

extended data figure 1

Abstract

The onset of turbulence in pipe flow has defied detailed understanding ever since the first observations of the spatially heterogeneous nature of the transition. Recent theoretical studies and experiments in simpler, shear-driven flows suggest that the onset of turbulence is a directed-percolation non-equilibrium phase transition, but whether these findings are generic and also apply to open or pressure-driven flows is unknown. In pipe flow, the extremely long time scales near the transition make direct observations of critical behaviour virtually impossible. Here we find a technical solution to that limitation and show that the universality class of the transition is directed percolation, from which a jammed phase of puffs emerges above the critical point. Our method is to experimentally characterize all pairwise interactions between localized patches of turbulence puffs and use these interactions as input for renormalization group and computer simulations of minimal models that extrapolate to long length and time scales. The strong interactions in the jamming regime enable us to explicitly measure the turbulent fraction and confirm model predictions. Our work shows that directed-percolation scaling applies beyond simple closed shear flows and underscores how statistical mechanics can lead to profound, quantitative and predictive insights on turbulent flows and their phases.

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