Aircraft-oriented intelligent prediction method for aerodynamic coefficients

Aerodynamic design fundamentally shapes aircraft performance, optimizing metrics such as maneuverability, fuel efficiency, and structural safety. These attributes are critical for aerospace engineering applications, including high-speed missiles and commercial aircraft. Traditional computational fluid dynamics methods deliver accurate simulations but rely on extensive computational resources. Their iterative processes, often spanning hours or days, hinder rapid design iterations essential for modern aerospace development. Current deep learning methods for aerodynamic modeling face challenges in processing complex three-dimensional geometries and integrating physical information. Weight imbalance between geometric features and physical information frequently degrades prediction accuracy, limiting model generalizability. This study proposed AeroPointNet, a neural network-based method to predict lift and drag coefficients with high efficiency and accuracy for diverse three-dimensional aircraft geometries under varying flow conditions, addressing these limitations to advance aerodynamic design.

AeroPointNet utilized three-dimensional point cloud representations of aircraft to capture local and global geometric features. Point clouds underwent farthest point sampling to select 2000 points, standardizing input dimensions. A multilayer perceptron mapped coordinates to a high-dimensional feature space. The network architecture employed a point cloud transformation module with local self-attention and learnable positional encoding, dynamically aggregating geometric features based on neighborhood relationships. Alternating downsampling and transformation modules reduced point counts from 2000 to 7 while expanding feature dimensions from 32 to 512. To model diverse flow conditions, AeroPointNet integrated geometric features with physical information, including Mach number, angle of attack, and sideslip angle. Fusion-based and separation-based weighted attention mechanisms, dynamically adjusted feature weights to mitigate weight imbalance arising from data distribution disparities. The fusion mechanism jointly modeled feature dependencies, while the separation mechanism independently computed weights to enhance interaction. The model was trained and tested on a dataset of 196 missile geometries, comprising 179340 samples across Mach numbers from 1.5 to 2.5, sideslip angles from 0° to 40°, and angles of attack from 0° to 60°. Comparative experiments evaluated AeroPointNet against seven established point cloud neural networks, using mean relative error, mean absolute error, mean squared error, and parameter size.

AeroPointNet significantly enhances prediction accuracy and computational efficiency. It achieves a prediction time of 0.11 seconds per sample, over three orders of magnitude faster than traditional computational fluid dynamics simulations, which average 11 minutes. In geometry generalization tests, AeroPointNet attains a mean relative error of 1.55% for lift coefficients and 1.51% for drag coefficients, surpassing baseline models. Its parameter size of 4.634 MB is 73.95% smaller than PointNeXt's 17.786 MB. In flow condition generalization tests across five randomized datasets, AeroPointNet maintains mean relative errors of 1.84% for lift and 4.87% for drag coefficients, demonstrating robust performance under unseen flow conditions. For most samples, predictions closely align with true values, with errors consistently below 0.25 across angles of attack. Ablation studies validate the weighted attention mechanisms, with their combined use reducing lift coefficient mean relative error by 63.79% and drag coefficient mean relative error by 65.68% compared to a no-attention baseline, adding only 2.89% to parameters. Error visualizations show stable predictions, with 95% confidence intervals narrowing beyond 10° angle of attack, indicating high reliability. Sampling size analysis confirms 2000 points as optimal for balancing accuracy and efficiency.

AeroPointNet provides a robust and efficient solution for aerodynamic coefficient prediction, overcoming limitations of traditional computational fluid dynamics and existing deep learning approaches. Its point cloud-based architecture and weighted attention mechanisms effectively address geometric feature extraction and weight imbalance, ensuring high precision and generalizability across diverse geometries and flow conditions. The model's lightweight design and rapid prediction capabilities enable real-time aerodynamic analysis. Future research will incorporate aerodynamic governing equations and turbulence models to enhance physical interpretability. Extending AeroPointNet to unsteady flow scenarios, such as vortex shedding or flow separation, will broaden its scope. Integration with intelligent shape optimization frameworks could streamline aircraft design processes, enabling automated optimization of aerodynamic performance. These advancements position AeroPointNet as a transformative tool for aerospace engineering, with potential to accelerate development of next-generation aircraft.