Enhancing nano-scale computational fluid dynamics with molecular pre-simulations: Unsteady problems and design optimisation
Enhancing nano-scale computational fluid dynamics with molecular pre-simulations: Unsteady problems and design optimisation
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DOI：
10.1016/j.compfluid.2015.03.023
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发表时间：
2015-07-22
影响因子：
2.8
通讯作者：
Reese, Jason M.
中科院分区：
文献类型：
--
作者：
Holland, David M.;Borg, Matthew K.;Reese, Jason M.
我们证明，计算流体动力学（CFD）模型增强了分子水平的信息，可以准确地预测非平凡几何的非定常纳米级流动，同时足够有效地用于设计优化。我们首先考虑由时变体力驱动的收敛-发散纳米尺度通道。我们改进的CFD预测的随时间变化的质量流率与相同构型的全分子动力学（MD）模拟非常吻合，并且计算成本很低。传统的CFD预测是完全不够的。然后，我们演示了增强型CFD作为设计优化工具在分岔二维通道上的应用，其目标是在固定的总容积和施加压力下最大化质量流量。在宏观尺度上，优化后的几何结构完全符合穆雷关于血管网络最优分支的定律；然而，在纳米尺度上，最优结果偏离了默里定律，并给出了一个修正方程。(C) 2015年作者。Elsevier Ltd.出版。
We demonstrate that a computational fluid dynamics (CFD) model enhanced with molecular-level information can accurately predict unsteady nano-scale flows in non-trivial geometries, while being efficient enough to be used for design optimisation. We first consider a converging-diverging nano-scale channel driven by a time-varying body force. The time-dependent mass flow rate predicted by our enhanced CFD agrees well with a full molecular dynamics (MD) simulation of the same configuration, and is achieved at a fraction of the computational cost. Conventional CFD predictions of the same case are wholly inadequate. We then demonstrate the application of enhanced CFD as a design optimisation tool on a bifurcating two-dimensional channel, with the target of maximising mass flow rate for a fixed total volume and applied pressure. At macro scales the optimised geometry agrees well with Murray's Law for optimal branching of vascular networks; however, at nanoscales, the optimum result deviates from Murray's Law, and a corrected equation is presented. (C) 2015 The Authors. Published by Elsevier Ltd.