Bringing the DrivAerNet recipe to aircraft
Blended wing body (BWB) configurations promise major fuel-efficiency gains, but designing them requires expensive CFD campaigns over unconventional geometry. BlendedNet applies the data-driven playbook pioneered in automotive aerodynamics to aircraft: a public dataset of parametrically generated BWB geometries with high-fidelity CFD, paired with a neural surrogate for rapid aerodynamic prediction.
What the paper delivers
Public BWB dataset
Parametric blended-wing-body geometries with high-fidelity CFD — openly released on Harvard Dataverse for the aircraft-design community.
Aerodynamic surrogate
A learned model predicts aerodynamic performance directly from geometry, replacing hours of simulation in early-stage design.
Design-space coverage
Systematic parametric sampling spans the practically relevant BWB configuration space, enabling generalizable learning.
Citation
@proceedings{sung2025blendednet,
author = {Sung, Nicholas and Spreizer, Steven and Elrefaie, Mohamed and
Samuel, Kaira and Jones, Matthew C. and Ahmed, Faez},
title = {BlendedNet: A Blended Wing Body Aircraft Dataset and Surrogate
Model for Aerodynamic Predictions},
series = {IDETC-CIE},
pages = {V03BT03A049},
year = {2025}
}