Next Article in Journal
Softwarized Edge Intelligence for Advanced IIoT Ecosystems: A Data-Driven Architecture Across the Cloud/Edge Continuum
Previous Article in Journal
An Automated Approach for Calibrating Gafchromic EBT3 Films and Mapping 3D Doses in HDR Brachytherapy
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

FLOW-GLIDE: Global–Local Interleaved Dynamics Estimator for Flow Field Prediction

1
College of Computer Science, Sichuan University, Chengdu 610065, China
2
National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, Sichuan University, Chengdu 610207, China
3
School of Aeronautics and Astronautics, Sichuan University, Chengdu 610200, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(19), 10834; https://doi.org/10.3390/app151910834
Submission received: 30 August 2025 / Revised: 20 September 2025 / Accepted: 23 September 2025 / Published: 9 October 2025
(This article belongs to the Section Fluid Science and Technology)

Abstract

Accurate prediction of the flow field is crucial to evaluating the aerodynamic performance of an aircraft. While traditional computational fluid dynamics (CFD) methods solve the governing equations to capture both global flow structures and localized gradients, they are computationally intensive. Deep learning-based surrogate models offer a promising alternative, yet often struggle to simultaneously model long-range dependencies and near-wall flow gradients with sufficient fidelity. To address this challenge, this paper introduces the Message-passing And Global-attention block (MAG-BLOCK), a graph neural network module that combines local message passing with global self-attention mechanisms to jointly learn fine-scale features and large-scale flow patterns. Building on MAG-BLOCK, we propose FLOW-GLIDE, a cross-architecture deep learning framework that learns a mapping from initial conditions to steady-state flow fields in a latent space. Evaluated on the AirfRANS dataset, FLOW-GLIDE outperforms existing models on key performance metrics. Specifically, it reduces the error in the volumetric flow field by 62% and surface pressure prediction by 82% compared to the state-of-the-art.
Keywords: graph neural network; attention mechanism; flow field prediction graph neural network; attention mechanism; flow field prediction

Share and Cite

MDPI and ACS Style

Su, J.; Xiao, L.; Wang, J. FLOW-GLIDE: Global–Local Interleaved Dynamics Estimator for Flow Field Prediction. Appl. Sci. 2025, 15, 10834. https://doi.org/10.3390/app151910834

AMA Style

Su J, Xiao L, Wang J. FLOW-GLIDE: Global–Local Interleaved Dynamics Estimator for Flow Field Prediction. Applied Sciences. 2025; 15(19):10834. https://doi.org/10.3390/app151910834

Chicago/Turabian Style

Su, Jinghan, Li Xiao, and Jingyu Wang. 2025. "FLOW-GLIDE: Global–Local Interleaved Dynamics Estimator for Flow Field Prediction" Applied Sciences 15, no. 19: 10834. https://doi.org/10.3390/app151910834

APA Style

Su, J., Xiao, L., & Wang, J. (2025). FLOW-GLIDE: Global–Local Interleaved Dynamics Estimator for Flow Field Prediction. Applied Sciences, 15(19), 10834. https://doi.org/10.3390/app151910834

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop