Neural operator improves fine-scale car airflow predictions efficiently
CIS-Transolver: A Cross-Layer Incremental-State Neural Operator for Fine-Scale Automotive Aerodynamic Flow Prediction
Machine LearningArtificial Intelligence
Summary
Predicting how air flows around cars is important but tricky, especially for small details near complex shapes. The authors propose a new neural method called CIS-Transolver that keeps track of fine details as it processes the airflow data across multiple steps. This approach helps the model better understand local and global airflow patterns together. Tested on three different car airflow datasets, CIS-Transolver made more accurate predictions than previous methods, especially for pressure and velocity fields.
What this means in practice
- •For automotive engineers: Improve aerodynamic simulations of car designs by more accurately predicting airflow details near complex shapes.
- •For computational fluid dynamics teams: Accelerate fine-scale aerodynamic flow estimations using data-driven models that maintain local and global flow coherence.
Authors
Jianglong Liu, Zhicheng Zhu, Jia Hao, Liangyue Jia
Abstract
Neural operators offer an efficient data-driven approach to rapid prediction of complex automotive aerodynamic flows. However, spectral bias and other factors limit accurate reconstruction of fine-scale flow variations near complex geometries. Existing studies improve the representation and fitting of fine-scale information mainly through information extraction and training objectives. Further study is needed to determine how extracted local details can be used continually in subsequent operator computations and contribute effectively to the overall flow-field representation. We propose CIS-Transolver, a cross-layer incremental-state neural operator that extends fine-scale modeling from local information extraction to its use across layers. The method extracts fine-scale information through local multiscale analysis and encodes it as geometric moment states that are propagated and updated across layers. By combining information from previous layers with feedback to the value representations in Physics-Attention, the method allows local details to contribute continually to subsequent modeling of global physical correlations. We systematically evaluate CIS-Transolver on three public automotive aerodynamics benchmarks, ShapeNet-Car, AhmedML, and DrivAerNet++, covering surface pressure fields and volumetric pressure and velocity fields. CIS-Transolver achieves the lowest mean relative L2 error among the evaluated methods for all five prediction metrics across the three benchmarks. The relative error reductions average 8.72% across the five metrics compared with the best baseline for each metric, reaching 16.38% for ShapeNet-Car pressure prediction and 15.60% for AhmedML velocity prediction. These results provide a new approach to fine-scale flow prediction for complex geometries.