Ali Sk Zeeshan, Dey Subhasish, Mahato Rajesh K
Department of Civil Engineering, Indian Institute of Technology Hyderabad, Telangana 502284, India.
Department of Civil Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
Proc Math Phys Eng Sci. 2021 Aug;477(2252):20210331. doi: 10.1098/rspa.2021.0331. Epub 2021 Aug 11.
In this paper, we explore the mega riverbed-patterns, whose longitudinal and vertical length dimensions scale with a few channel widths and the flow depth, respectively. We perform the stability analyses from both linear and weakly nonlinear perspectives by considering a steady-uniform flow in an erodible straight channel comprising a uniform sediment size. The mathematical framework stands on the dynamic coupling between the depth-averaged flow model and the particle transport model including both bedload and suspended load via the Exner equation, which drives the pattern formation. From the linear perspective, we employ the standard linearization technique by superimposing the periodic perturbations on the undisturbed system to find the dispersion relationship. From the weakly nonlinear perspective, we apply the centre-manifold-projection technique, where the fast dynamics of stable modes is projected on the slow dynamics of weakly unstable modes to obtain the Stuart-Landau equation for the amplitude dynamics. We examine the marginal stability, growth rate and amplitude of patterns for a given quintet formed by the channel aspect ratio, wavenumber of patterns, shear Reynolds number, Shields number and relative roughness number. This study highlights the sensitivity of pattern formation to the key parameters and shows how the classical results can be reconstructed on the parameter space.
在本文中,我们探讨了巨型河床形态,其纵向和垂直长度尺度分别与几个河槽宽度和水流深度成比例。我们通过考虑在具有均匀泥沙粒径的可侵蚀直河道中的稳定均匀流,从线性和弱非线性两个角度进行稳定性分析。数学框架基于深度平均水流模型与包括推移质和悬移质的颗粒输运模型之间的动态耦合,通过埃克斯纳方程驱动形态形成。从线性角度来看,我们采用标准线性化技术,通过在未受干扰的系统上叠加周期性扰动来找到色散关系。从弱非线性角度来看,我们应用中心流形投影技术,其中稳定模态的快速动力学投影到弱不稳定模态的慢动力学上,以获得振幅动力学的斯图尔特 - 兰道方程。我们研究了由河道纵横比、形态波数、剪切雷诺数、希尔兹数和相对粗糙度数组成的给定五重态下形态的边际稳定性、增长率和振幅。本研究突出了形态形成对关键参数的敏感性,并展示了如何在参数空间上重构经典结果。