  While a big wave of artificial intelligence (AI) has propagated to the field
of computational fluid dynamics (CFD) acceleration studies, recent research has
highlighted that the development of AI techniques that reconciles the following
goals remains our primary task: (1) accurate prediction of unseen (future) time
series in long-term CFD simulations (2) acceleration of simulations (3) an
acceptable amount of training data and time (4) within a multiple PDEs
condition. In this study, we propose a residual-based physics-informed transfer
learning (RePIT) strategy to achieve these four objectives using ML-CFD hybrid
computation. Our hypothesis is that long-term CFD simulation is feasible with
the hybrid method where CFD and AI alternately calculate time series while
monitoring the first principle's residuals. The feasibility of RePIT strategy
was verified through a CFD case study on natural convection. In a single
training approach, a residual scale change occurred around 100th timestep,
resulting in predicted time series exhibiting non-physical patterns as well as
a significant deviations from the ground truth. Conversely, RePIT strategy
maintained the residuals within the defined range and demonstrated good
accuracy throughout the entire simulation period. The maximum error from the
ground truth was below 0.4 K for temperature and 0.024 m/s for x-axis velocity.
Furthermore, the average time for 1 timestep by the ML-GPU and CFD-CPU
calculations was 0.171 s and 0.015 s, respectively. Including the
parameter-updating time, the simulation was accelerated by a factor of 1.9. In
conclusion, our RePIT strategy is a promising technique to reduce the cost of
CFD simulations in industry. However, more vigorous optimization and
improvement studies are still necessary.
