Engine performance is affected by cooling airflow onto the engine cooling module.
During initial design, frontal openings, grills, cooling module size, placement,
and location are optimized to ensure sufficient airflow onto the cooling module.
Currently, design concepts are validated using 3D computational fluid dynamics
(CFD) simulations performed iteratively on full vehicle models to predict and
optimize cooling airflow onto cooling modules. Each design concept iteration
consumes significant time and resources. This study introduces a machine
learning (ML) model to streamline underhood airflow prediction, reducing
reliance on iterative CFD. Previous CFD simulation data is used to create a
training dataset, which calibrates the ML model, describing underhood airflow as
a function of input parameters. The relevant ML algorithm is used to calibrate
the model, perform data fitting of the training values, after which a testing
dataset is created to validate the model for a range of design parameters and
vehicle conditions. Upon achieving the target testing accuracy (90% accuracy
target in this particular case), the ML model is ready for implementation. The
ML model is used to predict initial estimates of airflow and refine the design
iterations, while CFD simulations are performed for the finalized concepts. This
eliminates the need for expensive and lengthy design analysis iteration loops,
effectively replacing them with a highly flexible model capable of predicting
underhood airflow for even minor design changes quickly. Use of this model can
decrease the time required per iteration by more than 90% compared to
conventional CFD, thus enabling analysis of more designs in a given time
frame.
