BlendedNet: A Blended Wing Body Aircraft Dataset and Surrogate Model for Aerodynamic Predictions
Nicholas Sung, Steven Spreizer, Mohamed Elrefaie, Kaira Samuel, Matthew D. Jones, Faez Ahmed
Abstract
Abstract We introduce BlendedNet, a publicly available aerodynamic dataset featuring 999 unique blended wing body (BWB) geometries. Each geometry was simulated across approximately 9 distinct aerodynamic cases, resulting in a total of 8,830 successfully converged cases. BlendedNet geometries are systematically generated using sampling across geometric design parameters and flight conditions, and analyzed with high-fidelity Reynolds-Averaged Navier-Stokes (RANS) simulations employing the Spalart-Allmaras turbulence model using 9 to 14 million volume cells per case. In addition to this dataset, we introduce a fully end-to-end surrogate modeling framework for point-wise aerodynamic coefficient prediction (Cp, Cfx, Cfz) which contributes to lift and drag. This framework consists of two separate models: (1) a permutation-invariant PointNet model which predicts geometric design parameters from sampled point clouds, and (2) a Feature-wise Linear Modulation (FiLM) network that takes these predicted parameters, along with flight conditions, to generate pointwise aerodynamic coefficient predictions. Our evaluations demonstrate that the surrogate model achieves accurate pointwise aerodynamic performance predictions with low errors. BlendedNet addresses critical data scarcity in the field, enabling future research into data-driven surrogate modeling methods for complex BWB aircraft aerodynamic design.