AI-Powered Fluid Dynamics: Neural Networks Simulate Soft Swimmer Locomotion

Summary:This research introduces a neural network-based approach for fluid dynamics simulation to predict the motion trajectories of soft swimmers in fluids. By training neural network models on high-fidelity data generated from adaptive fluid-structure simulations, the method achieves precise predictions of hydrodynamic fields for planar and volumetric eel swimmers. Experimental results demonstrate the method's effectiveness in handling high-Reynolds-number trajectories, with a full-domain global relat

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Background and Motivation

High-fidelity immersed-boundary simulation can accurately resolve the coupled motion of a deforming swimmer and its surrounding fluid, but its computational cost limits repeated evaluations for engineering design, parameter studies, and control. To address this, researchers have developed neural network-based surrogate models to predict the hydrodynamic fields generated by planar and volumetric eel swimmers.

Methodology and Models

• Data Source: The surrogate models are trained on regular-grid fields exported from adaptive fluid-structure simulations and conditioned on swimmer geometry and Reynolds number.
• Planar Model: Jointly predicts two velocity components, scalar vorticity, and pressure. In tests on five high-Reynolds-number trajectories, the full-domain global relative L^2 error is 3.51%.
• Volumetric Model: Uses three target-specific models (predicting three-dimensional velocity, vorticity, and pressure, respectively). On five within-range trajectories, the full-domain global relative L^2 errors are 3.44%, 5.58%, and 19.2%.

Technical Highlights

1. High-Precision Prediction: The planar model demonstrates exceptional performance in handling high-Reynolds-number trajectories with an error rate as low as 3.51%.
2. Multi-Model Collaboration: The volumetric model leverages three independent models to collaboratively predict different physical quantities.
3. Enhanced Real-Time Capability: Compared to traditional fluid dynamics simulation methods, the neural network surrogate models significantly improve computational efficiency.

Industry Impact and Future Directions

This research demonstrates the significant potential of neural networks in complex fluid dynamics simulations, providing new tools and methods for engineering design and control optimization. Future research directions include improving pressure prediction accuracy and physical consistency to further enhance the model's practicality and reliability.

Recommendations for Developers

For developers working in fluid dynamics simulation, it is advisable to consider neural network surrogate models as a supplement to traditional simulation methods to improve computational efficiency. Additionally, it is recommended to pay attention to the model's performance in different application scenarios and optimize and adjust according to specific needs.

Original Source

ArXiv Machine Learning (cs.LG)

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Tags: #Neural Networks #Fluid Dynamics #AI Simulation