Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes

TL;DR

This paper introduces a new 3D transonic wing CFD dataset with 30,000 samples, evaluates neural surrogates like AB-UPT for out-of-distribution generalization, and demonstrates AB-UPT's effectiveness in aerodynamic optimization tasks.

Contribution

The paper provides a novel large-scale 3D transonic wing dataset and assesses neural surrogate models' ability to generalize to unseen geometries and flow conditions.

Findings

AB-UPT performs well on transonic flowfield predictions.

AB-UPT accurately reproduces drag-lift Pareto fronts for unseen geometries.

The dataset enables data-driven aerodynamic optimization in transonic regimes.

Abstract

The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity of compressible flows and 3D effects such as wingtip vortices. Existing aerospace datasets predominantly focus on 2D airfoils, neglecting these critical 3D phenomena. To address this gap, we present a new dataset of CFD simulations for 3D wings in the transonic regime. The dataset comprises volumetric and surface-level fields for around $30,000$ samples with unique geometry and inflow conditions. This allows computation of lift and drag coefficients, providing a foundation for data-driven aerodynamic optimization of the drag-lift Pareto front. We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT, focusing on their out-of-distribution (OOD) generalization over geometry and inflow variations. AB-UPT demonstrates strong performance for transonic flowfields and reproduces physically consistent drag-lift Pareto fronts even for unseen wing configurations. Our results demonstrate that AB-UPT can approximate drag-lift Pareto fronts for unseen geometries, highlighting its potential as an efficient and effective tool for rapid aerodynamic design exploration. To facilitate future research, we open-source our dataset at https://huggingface.co/datasets/EmmiAI/Emmi-Wing.