Abstract
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.
Highlights
Many emerging applications of nanofluidic technology take advantage of different physical effects that dominate at small scales; examples can be found in air and water purification [1,2], and in micro chemical reactors [3,4]
To test whether our enhanced Computational fluid dynamics (CFD) model is an improvement over conventional CFD, we compare results with predictions from compressible CFD with no-slip at the wall and without incorporating a CFD surface offset
We demonstrate how the enhanced CFD model can be used in design optimisation problems at the nanoscale
Summary
Many emerging applications of nanofluidic technology take advantage of different physical effects that dominate at small scales; examples can be found in air and water purification [1,2], and in micro chemical reactors [3,4]. The design of these technologies would be greatly facilitated by being able to perform numerical simulations that predict mass flow rates and heat transfer. Computational fluid dynamics (CFD) is regularly used to model and create optimal every-day engineering designs efficiently. The drawback is that MD is extremely computationally intensive, especially when used to model systems comprising hundreds of thousands of molecules that would be required for engineering applications
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