  Computational fluid dynamics (CFD) simulations, a critical tool in various
engineering applications, often require significant time and compute power to
predict flow properties. The high computational cost associated with CFD
simulations significantly restricts the scope of design space exploration and
limits their use in planning and operational control. To address this issue,
machine learning (ML) based surrogate models have been proposed as a
computationally efficient tool to accelerate CFD simulations. However, a lack
of clarity about CFD data requirements often challenges the widespread adoption
of ML-based surrogates among design engineers and CFD practitioners. In this
work, we propose an ML-based surrogate model to predict the temperature
distribution inside the cabin of a passenger vehicle under various operating
conditions and use it to demonstrate the trade-off between prediction
performance and training dataset size. Our results show that the prediction
accuracy is high and stable even when the training size is gradually reduced
from 2000 to 200. The ML-based surrogates also reduce the compute time from ~30
minutes to around ~9 milliseconds. Moreover, even when only 50 CFD simulations
are used for training, the temperature trend (e.g., locations of hot/cold
regions) predicted by the ML-surrogate matches quite well with the results from
CFD simulations.
