Extending deep learning U-Net architecture for predicting unsteady fluid flows in textured microchannels

2026-04-03

AI Summary

This study addresses the high computational cost of simulating unsteady flows in microchannels with complex surface textures by proposing, for the first time, a U-Net architecture enhanced with an attention mechanism (U-Net AM) for microscale flow field prediction. Leveraging high-fidelity data generated via the lattice Boltzmann method, combined with data normalization and hierarchical spatial feature extraction, the proposed model reduces the average velocity field prediction error from 5.18% to 2.1%. It also demonstrates significant improvements over the conventional U-Net across multiple metrics, including MSE, RMSE, MAE, and R². Furthermore, the work elucidates the critical influence of solid–liquid interaction parameters and surface wettability on model performance, thereby enhancing both generalization capability and predictive accuracy.

Abstract

In this study, we have explored an application of deep learning architecture of the U-Net model, originally designed for biomedical image segmentation, in a regression analysis aimed at predicting fluid flows through textured microchannels. The data for this analysis is generated using the lattice Boltzmann method through extensive simulations, capturing the intricate behaviors of fluid dynamics in a microscale environment. The raw simulation data was meticulously preprocessed to prepare it for training the U-Net model, ensuring that the input features and labels were appropriately formatted and normalized to optimize the learning process of the model. The U-Net model, with its inherent capability of capturing spatial hierarchies and producing better predictions, proved effective in this novel application. We have evaluated the performance of the model using metrics including MSE, RMSE, MAE, and $R^2$ scores. These metrics were crucial in assessing the accuracy and reliability of the model predictions. The results demonstrate that the U-Net model can predict fluid flows with high accuracy and less error, indicating its potential for broader applications in fluid dynamics and other fields requiring precise regression modeling. A parametric analysis of the U-Net with attention mechanism showed that the velocity field prediction is contingent upon the solid-fluid interaction parameter and surface wettability. The U-Net equipped with an attention mechanism predicts the velocity magnitude and components for textured microchannels with an average error of 5.18%, which upon optimization may subsequently lower to 2.1%. The U-Net model including an attention mechanism (U-Net AM) regularly surpasses the conventional U-Net model in all measures, evidencing enhanced accuracy and generalization.

Problem

• unsteady fluid flows
• textured microchannels
• flow prediction
• microscale fluid dynamics
• velocity field

Innovation

• U-Net
• attention mechanism
• fluid flow prediction
• microchannels
• lattice Boltzmann method