  A new Micro-Macro-Surrogate (MMS) hybrid method is presented that couples the
Direct Simulation Monte Carlo (DSMC) method with Computational Fluid Dynamics
(CFD) to simulate low-speed rarefied gas flows. The proposed MMS method
incorporates surrogate modelling instead of direct coupling of DSMC data with
the CFD, addressing the limitations CFD has in accurately modelling rarefied
gas flows, the computational cost of DSMC for low-speed and multiscale flows,
as well as the pitfalls of noise in conventional direct coupling approaches.
The surrogate models, trained on the DSMC data using Bayesian inference,
provide noise-free and accurate corrections to the CFD simulation enabling it
to capture the non-continuum physics. The MMS hybrid approach is validated by
simulating low-speed, force-driven rarefied gas flows in a canonical
parallel-plate system and shows excellent agreement with DSMC benchmark
results. A comparison with the typical domain decomposition DSMC-CFD hybrid
method is also presented, to demonstrate the advantages of noise-avoidance in
the proposed approach. The method also inherently captures the uncertainty
arising from micro-model fluctuations, allowing for the quantification of
noise-related uncertainty in the predictions. The proposed MMS method
demonstrates the potential to enable multiscale simulations where CFD is
inaccurate and DSMC is prohibitively expensive.
