Machine learning models increasingly assist in simulating airflow over textured and complex surfaces, offering computational efficiency for design iterations.

Applications in engineering design

Across aerospace and automotive sectors, practitioners use surrogate models to approximate computational fluid dynamics outputs and accelerate early-stage evaluation cycles.

Such approaches allow rapid assessment of surface geometries that include porosity, roughness or non‑uniform textures without running full high‑fidelity simulations.

Methods and model types

Researchers commonly combine reduced‑order modelling with supervised learning to predict integral quantities like lift and drag from simplified flow descriptors.

Convolutional neural networks, graph neural networks and physics‑informed networks serve different roles depending on data structure and imposed physical constraints.

Limitations and verification

Data coverage and generalization remain central challenges: models trained on limited operating conditions can mispredict outside their training envelopes without proper uncertainty quantification.

Consequently, validation against experimental measurements or high‑fidelity simulations remains necessary before deploying predictions in safety‑critical applications.

Outlook

Integration of machine learning with classical simulation workflows aims to reduce iteration time and support design exploration while preserving physical consistency through hybrid modelling.

Ongoing work focuses on improving robustness, interpretability and the ability to estimate prediction confidence in varied aerodynamic regimes.