Heyman J, Lester D R, Le Borgne T
Geosciences Rennes, UMR 6118, Université de Rennes 1, CNRS, 35042 Rennes, France.
School of Engineering, RMIT University, 3000 Melbourne, Australia.
Phys Rev Lett. 2021 Jan 22;126(3):034505. doi: 10.1103/PhysRevLett.126.034505.
Steady laminar flows through porous media spontaneously generate Lagrangian chaos at pore scale, with qualitative implications for a range of transport, reactive, and biological processes. The characterization and understanding of mixing dynamics in these opaque environments is an outstanding challenge. We address this issue by developing a novel technique based upon high-resolution imaging of the scalar signature produced by push-pull flows through porous media samples. Owing to the rapid decorrelation of particle trajectories in chaotic flows, the scalar image measured outside the porous material is representative of in situ mixing dynamics. We present a theoretical framework for estimation of the Lyapunov exponent based on extension of Lagrangian stretching theories to correlated aggregation. This method provides a full characterization of chaotic mixing dynamics in a large class of porous materials.
通过多孔介质的稳定层流在孔隙尺度上自发产生拉格朗日混沌,这对一系列传输、反应和生物过程具有定性影响。在这些不透明环境中表征和理解混合动力学是一项严峻挑战。我们通过开发一种新技术来解决这个问题,该技术基于对通过多孔介质样品的推拉流产生的标量特征进行高分辨率成像。由于混沌流中粒子轨迹的快速去相关,在多孔材料外部测量的标量图像代表了原位混合动力学。我们基于将拉格朗日拉伸理论扩展到相关聚集,提出了一个用于估计李雅普诺夫指数的理论框架。该方法全面表征了一大类多孔材料中的混沌混合动力学。