Computational fluctuating hydrodynamics aims at understanding the impact of thermal fluctuations on fluid motions at small scales through numerical exploration. These fluctuations are modeled as stochastic flux terms and incorporated into the classical Navier-Stokes equations, which need to be solved numerically. In this paper, we present a novel projection-based method for solving the incompressible fluctuating hydrodynamics (FHD) equations. By analyzing the equilibrium structure factor spectrum of the velocity field for the linearized FHD equations, we investigate how the inherent splitting errors affect the numerical solution of the stochastic partial differential equations in the presence of non-periodic boundary conditions, and how iterative corrections can reduce these errors. Our computational examples demonstrate both the capability of our approach to reproduce correctly stochastic properties of fluids at small scales as well as its potential use in the simulations of multi-physics problems.
翻译:通过数字探索,了解热波动对流体运动小范围流体运动的影响。这些波动以随机通量术语为模型,并纳入古典纳维埃-斯托克斯方程式,需要用数字方法加以解决。在本文中,我们提出一种新的基于预测的办法来解决流体动力(FHD)不可压缩波动方程式。通过分析线性流体动力方程式速度场的平衡结构要素频谱,我们调查内在的分裂差错如何影响非周期边界条件下随机部分差异方程式的数值解决方案,以及迭代校正如何减少这些错误。我们的计算实例表明,我们的方法有能力在小尺度上正确复制流体的随机特性,以及在模拟多物理问题时可能使用这些特性。