Meshing TFG Context and Summary

Effect of Turbulence Modeling and Automatic Mesh Refinement on the External Aerodynamics of Automobiles

Additional Mesh Coarsening Approaches and Their Effects

Automated Parallel Hex-Dominant Volume Mesh Generation for the DrivAer Notchback Using ChopMesh

Scale Resolving Simulation TFG Context and Summary

High Order Spectral Element Method DrivAer

Effect of time-step and grid refinement on DDES approach on flow topology and separation behaviour for the DrivAer model

Evaluation of Hybrid RANS–LES Scale-Resolving Simulations for the DrivAer Notchback Configuration

Flexcompute contribution - SRS and Meshing Group

Effect of LES model on the onset of separation

Revisiting DrivAer Aerodynamics: Updated results integrating Noise Factors and AI

Assessment of the open-source CFD code code_saturne on the AutoCFD workshop cases: from steady RANS to hybrid RANS-LES

Investigation of the Effect of Targeted Octree Mesh Refinement on WFLES and SBES solutions for the DrivAer Notchback Configuration

The Role of Numerical Scheme Accuracy in WMLES of the Aerodynamics of the Windsor Body

Scale-resolving OpenFOAM simulations of the DriveAer case 2 using a novel family of dynamic RANS closures

HPC TFG Context and Summary

GPU-Accelerated Scale Resolving Simulations for AutoCFD5 on Fixed and Adaptive Octrees

Detailed comparison of performance and quality between GPU and CPU simulations with OpenFOAM

Volcano ScaLES Results for AutoCFD5 Cases

Noise Factors TFG Context and Summary

Quantification of uncertainties from mesh approaches and hardware architecture using PowerFLOW and DrivAer model

Using Ensemble Averaging to Accelerate Uncertainty Quantification in Automotive Aerodynamics Simulations

Towards Multi-Factor Noise Assessment in Automotive CFD Using Design of Experiments

Pacefish‘ LBM-based GPU-native CFD

Ensemble Averaging Method for Efficient Transient Simulations of External Automotive Aerodynamics

Quantifying Noise Factors: Sampling Length and CrossSolver Uncertainty Analysis

AI/ML TFG context, datasets and summary

Impact of Input Quantities on Aerodynamic Predictions Using Geometric Deep Learning

IngeniNetSim: Fast and Scalable Geometric Deep Learning for DrivAerML Aerodynamic Prediction

Spatially Smooth Multi-Granularity Conformal Prediction for Reliable Automotive Aerodynamic Surrogate Modeling

Trustworthy AI Surrogates for DrivAerML: Cross-Architecture Evaluation, Data-Source Ablation, and Calibrated Uncertainty Quantification

Sensitivity Testing of Transformer-Based Architectures for AutoCFD

Single-Pass versus Sequential Multi-Field Prediction with the Courant Neural Surrogate for the DrivAer Case

Data-Centric Large Physics Models for Automotive Aerodynamics: From Architecture to Scalable Training Data