[M-IS21] 地球流体力学：地球惑星現象への分野横断的アプローチ

[MIS21-P03] 熱対流に見られる準周期的な振動とデータ駆動分析によるその特徴の抽出

*柳澤 孝寿^1,4、亀山 真典^2,1、能登 大輔³、田坂 裕司^4,1 (1.国立研究開発法人海洋研究開発機構、2.愛媛大学地球深部ダイナミクス研究センター、3.ペンシルバニア大学、4.北海道大学大学院工学研究院)

キーワード：熱対流、準周期性、モード分解

Convection in an enclosed volume is ubiquitous in nature, such as water flow in ice sheet, magma flow in dikes or sills, and various geothermal systems. As a simplified fundamental setting of these phenomena, we investigated thermal convection in three-dimensional enclosed rectangular geometries (xyz) driven by the temperature difference between the top and bottom boundaries, with all fixed velocity boundary conditions and adiabatic side walls. When the scale of one horizontal direction is extremely small, the setting is named as Hele-Shaw cell. It is known that convection in extremely narrow gaps like this, is comparable to the convection in porous medium described by the Darcy law. On the other hand, behaviors of convection in moderately narrow gaps are not fully understood yet, because of their complicated effect of two- and three- dimensionality. To make the complicated behaviors clear, we systematically searched convection patterns with various length scales of Lx and Ly, keeping Lz=1, by laboratory experiments and numerical simulations. With the increase of the Rayleigh number (Ra) under a fixed narrow geometry, we observed that the convective flow pattern changes from steady quasi-two-dimensional, time dependent quasi-two-dimensional, steady three-dimensional, and to time dependent three-dimensional one. In these various flow behaviors, time dependent quasi-two-dimensional pattern shows quasi-periodic oscillation at a certain range of Ra. We investigated the time-space features of the quasi-periodicity in more detail. As an example of data-driven analysis, we applied Proper Orthogonal Decomposition (POD), and Dynamic Mode Decomposition (DMD), for the convection with quasi-periodic behaviors. By either method, the flow behavior can be reconstructed by the sum of a small number of decomposed modes, and we succeeded in extracting time-space features of the behavior. In addition, we can identify specific frequencies of the oscillation and their fluctuations by DMD.