Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes
- URL: http://arxiv.org/abs/2511.21474v1
- Date: Wed, 26 Nov 2025 15:06:19 GMT
- Title: Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes
- Authors: Fabian Paischer, Leo Cotteleer, Yann Dreze, Richard Kurle, Dylan Rubini, Maurits Bleeker, Tobias Kronlachner, Johannes Brandstetter,
- Abstract summary: We present a new dataset of CFD simulations for 3D wings in the transonic regime.<n>The dataset comprises volumetric and surface-level fields for around $30,000$ samples with unique geometry and inflow conditions.<n>We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT.
- Score: 19.286954413935025
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design [19.941629337887484]
We present SuperWing, a comprehensive open dataset of transonic swept-wing aerodynamics.<n>The wing shapes in the dataset are generated using a simplified yet expressive geometry parameterization.<n>To demonstrate the dataset's utility, we benchmark two state-of-the-art Transformers that accurately predict surface flow.
arXiv Detail & Related papers (2025-12-16T13:35:45Z) - AB-UPT for Automotive and Aerospace Applications [23.03293469767775]
Anchored-Branched Universal Physics Transformers (AB-UPT) shows strong capabilities to replicate automotive computational fluid dynamics simulations.<n>We add two new datasets to the body of empirically evaluated use-cases of AB-UPT, combining high-quality data generation with state-of-the-art neural surrogates.<n>AB-UPT shows strong performances across the board. Notably, it obtains near perfect prediction of integrated aerodynamic forces within seconds from a simple isotopically tesselate geometry representation.
arXiv Detail & Related papers (2025-10-17T16:40:35Z) - DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset [1.184330339427731]
This study proposes a point cloud learning framework called DrivAer Transformer.<n>The DAT structure uses the DrivAerNet++ dataset, which contains high-fidelity CFD data of industrial-standard 3D vehicle shapes.<n>The framework is expected to accelerate the vehicle design process and improve development efficiency.
arXiv Detail & Related papers (2025-04-11T02:50:38Z) - MTGS: Multi-Traversal Gaussian Splatting [51.22657444433942]
Multi-traversal data provides multiple viewpoints for scene reconstruction within a road block.<n>We propose Multi-Traversal Gaussian Splatting (MTGS), a novel approach that reconstructs high-quality driving scenes from arbitrarily collected multi-traversal data.<n>Our results demonstrate that MTGS improves LPIPS by 23.5% and geometry accuracy by 46.3% compared to single-traversal baselines.
arXiv Detail & Related papers (2025-03-16T15:46:12Z) - A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils [61.60175086194333]
aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils.<n>Here, we consider modeling of incompressible flows over solid objects, wherein geometric structures are a key factor in determining aerodynamics.<n>To effectively incorporate geometries, we propose a message passing scheme that efficiently and expressively integrates the airfoil shape with the mesh representation.<n>These design choices lead to a purely data-driven machine learning framework known as GeoMPNN, which won the Best Student Submission award at the NeurIPS 2024 ML4CFD Competition, placing 4th overall.
arXiv Detail & Related papers (2024-12-12T16:05:39Z) - Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations [1.1932047172700866]
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains.
The proposed models can be applied directly to unstructured domains for different flow conditions.
Remarkably, the method can perform inference five order of magnitude faster than the high fidelity solver on the RANS transonic airfoil dataset.
arXiv Detail & Related papers (2024-07-29T11:48:44Z) - Averaging Spatio-temporal Signals using Optimal Transport and Soft
Alignments [110.79706180350507]
We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality.
Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
arXiv Detail & Related papers (2022-03-11T09:46:22Z) - Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism.
We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies.
We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
arXiv Detail & Related papers (2021-12-14T13:14:24Z) - Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural
Network [0.0]
Figuring out the right airfoil is a crucial step in the preliminary stage of any aerial vehicle design.
This study compares different network architectures and training datasets in an attempt to gain insight as to how the network perceives the given airfoil geometries.
arXiv Detail & Related papers (2021-09-24T19:07:19Z) - Voxel Transformer for 3D Object Detection [133.34678177431914]
Voxel Transformer (VoTr) is a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds.
Our proposed VoTr shows consistent improvement over the convolutional baselines while maintaining computational efficiency on the KITTI dataset and the Open dataset.
arXiv Detail & Related papers (2021-09-06T14:10:22Z) - Trajectory Design for UAV-Based Internet-of-Things Data Collection: A
Deep Reinforcement Learning Approach [93.67588414950656]
In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment.
We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm.
Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
arXiv Detail & Related papers (2021-07-23T03:33:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.