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arXiv 2602.17136physics.flu-dyn
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周期性强迫多相湍流中动能与表面能的转换

Conversions between kinetic and surface energy in periodically forced multiphase turbulence

CNRS, CORIA, UMR 6614, Normandy Univ., UNIROUEN, INSA Rouen(法国国家科学研究中心,CORIA,联合研究实验室6614,诺曼底大学,鲁昂大学,鲁昂国立应用科学学院)

Jonathan Vahé, Fabien Thiesset

AI总结:

通过周期性强迫多相湍流数值模拟,研究动能与表面能之间的动态转换,提出改进的Ka-Pi-bara模型捕捉非平衡效应和相位滞后。

摘要:

在多相流中,动能和界面能共存,它们的相互转换可能影响整体能量平衡。然而,在统计稳态流中,这些能量库保持不变,使得这种转换无法被检测到。为了观察它们,必须引入一定程度的非稳态性,这里通过有意使用波动的周期性动能输入来实现。本研究的主要焦点是连接能量注入、转换和耗散的动态循环,我们通过对多相均匀各向同性湍流进行直接数值模拟来探索,该湍流受到周期性强迫。数据库包括各种雷诺数、韦伯数以及稠密状态下的体积分数。为了解释和复现观察到的动力学,我们将文献[1]的Ka-Pi-bara模型(k-ε模型的扩展)重新表述为总能量(动能与表面能之和),并通过添加表面能及其破坏的方程进一步增强该模型。该模型准确捕捉了湍流的一个关键特征:非平衡效应,表现为动能与其耗散率之间的相位滞后,这被发现也在多相流中起作用。对模型进行线性化突出了系统的各种相关时间尺度,并提供了不同可观测量如何耦合以及对能量输入响应的预测。

英文摘要:

In multiphase flows, kinetic and interfacial energies coexist, and their mutual conversion can potentially influence the overall energy balance. However, in statistically steady flows these energy reservoirs remain constant, making such conversions undetectable. For them to be observed, a degree of unsteadiness must be introduced, here provided by the deliberate use of a fluctuating time-periodic input of kinetic energy into the system. The main focus of the present work is on the dynamical cycle connecting energy injection, conversion, and dissipation which we explore using {direct} numerical simulations of multiphase homogeneous isotropic turbulence, subjected to periodic forcing. The database includes various Reynolds and Weber numbers and volume fractions in the dense regime. To interpret and replicate the observed dynamics, we reformulate the \textit{Ka-Pi-bara} model of \cite{Bos2026} (an extension of the $k$--$ε$ model) in terms of total energy (the sum of kinetic and surface energy), which we further enhance by adding equations for the surface energy and its destruction. This model accurately captures a key feature of turbulence: non-equilibrium effects, seen as the phase lag between kinetic energy and its rate of dissipation, which are found to operate also in multiphase flows. Linearizing the model highlights the various relevant time scales of the system and provides predictions of how different observables are coupled and respond to the energy input.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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