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A new Micro-Macro-Surrogate (MMS) hybrid method is presented that couples theDirect Simulation Monte Carlo (DSMC) method with Computational Fluid Dynamics(CFD) to simulate low-speed rarefied gas flows. The proposed MMS methodincorporates surrogate modelling instead of direct coupling of DSMC data withthe CFD, addressing the limitations CFD has in accurately modelling rarefiedgas 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 itto capture the non-continuum physics. The MMS hybrid approach is validated bysimulating low-speed, force-driven rarefied gas flows in a canonicalparallel-plate system and shows excellent agreement with DSMC benchmarkresults. A comparison with the typical domain decomposition DSMC-CFD hybridmethod is also presented, to demonstrate the advantages of noise-avoidance inthe proposed approach. The method also inherently captures the uncertaintyarising from micro-model fluctuations, allowing for the quantification ofnoise-related uncertainty in the predictions. The proposed MMS methoddemonstrates the potential to enable multiscale simulations where CFD isinaccurate and DSMC is prohibitively expensive.

A DSMC-CFD coupling method using surrogate modelling for low-speed rarefied gas flows