Radhakrishnan Ravi, Yu Hsiu-Yu, Eckmann David M, Ayyaswamy Portonovo S
Department of Chemical and Biomolecular Engineering; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 e-mail:
Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104 e-mail:
J Heat Transfer. 2017 Mar;139(3):0330011-330019. doi: 10.1115/1.4035006. Epub 2016 Nov 22.
Traditionally, the numerical computation of particle motion in a fluid is resolved through computational fluid dynamics (CFD). However, resolving the motion of nanoparticles poses additional challenges due to the coupling between the Brownian and hydrodynamic forces. Here, we focus on the Brownian motion of a nanoparticle coupled to adhesive interactions and confining-wall-mediated hydrodynamic interactions. We discuss several techniques that are founded on the basis of combining CFD methods with the theory of nonequilibrium statistical mechanics in order to simultaneously conserve thermal equipartition and to show correct hydrodynamic correlations. These include the fluctuating hydrodynamics (FHD) method, the generalized Langevin method, the hybrid method, and the deterministic method. Through the examples discussed, we also show a top-down multiscale progression of temporal dynamics from the colloidal scales to the molecular scales, and the associated fluctuations, hydrodynamic correlations. While the motivation and the examples discussed here pertain to nanoscale fluid dynamics and mass transport, the methodologies presented are rather general and can be easily adopted to applications in convective heat transfer.
传统上，流体中粒子运动的数值计算是通过计算流体动力学（CFD）来解决的。然而，由于布朗力和流体动力之间的耦合，解析纳米粒子的运动带来了额外的挑战。在这里，我们关注与粘附相互作用以及受限壁介导的流体动力相互作用相耦合的纳米粒子的布朗运动。我们讨论了几种基于将CFD方法与非平衡统计力学理论相结合的技术，以便同时保持热均分并显示正确的流体动力相关性。这些技术包括涨落流体动力学（FHD）方法、广义朗之万方法、混合方法和确定性方法。通过所讨论的示例，我们还展示了从胶体尺度到分子尺度的时间动力学的自上而下的多尺度进展，以及相关的涨落、流体动力相关性。虽然这里讨论的动机和示例涉及纳米尺度的流体动力学和质量传输，但所提出的方法相当通用，并且可以很容易地应用于对流热传递。
