This article is concerned with the problem of determining an unknown source of non-potential, external time-dependent perturbations of an incompressible fluid from large-scale observations on the flow field. A relaxation-based approach is proposed for accomplishing this, which leverages a nonlinear property of the equations of motions to asymptotically enslave small scales to large scales. In particular, an algorithm is introduced that systematically produces approximations of the flow field on the unobserved scales in order to generate an approximation to the unknown force; the process is then repeated to generate an improved approximation of the unobserved scales, and so on. A mathematical proof of convergence of this algorithm is established in the context of the two-dimensional Navier-Stokes equations with periodic boundary conditions under the assumption that the force belongs to the observational subspace of phase space; at each stage in the algorithm, it is shown that the model error, represented as the difference between the approximating and true force, asymptotically decrements to zero in a geometric fashion provided that sufficiently many scales are observed and certain parameters of the algorithm are appropriately tuned; the issue of the sharpness of the assumptions, among other practical considerations such as the transient periods between updates, are also discussed.
翻译:本条涉及从流动场的大规模观测中确定一个未知的非潜在、外部时间性扰动源的未知来源,即流动场上无法压缩的流体的外部时间扰动问题。为实现这一目标,建议采取以放松为基础的方法,将运动方程式的非线性属性作为杠杆,将运动方程式的非线性属性作为无线的小型升降尺度作为大尺度。特别是,引入了一种算法,系统地在未观测的尺度上生成流动场的近似,以便产生接近未知力的近似值;随后又重复了这一过程,使未观测的尺度更近似,等等。在二维导航-斯托克方程式中确立了这一算法趋同的数学证据,假设该方程式属于阶段空间的观测子空间;在算法的每个阶段,都表明模型错误代表接近力和真实力之间的差别;在几何时制式时,只要观察到足够多的尺度的近似近似近似近似近似近似度,并且对算法的某些参数进行了适当调整;在分析中,还观察了其他精确的参数之间的跨度。