High Performance Deep Learning Framework for Computational Fluid Dynamics Modelling in Aerospace Applications

Abstract

High fidelity modelling and simulation of complex fluid dynamic phenomena has become a crucial tool for advancing aerospace technology. For many years, a key technique that is utilized to analyze aerodynamic behavior, predict flow behavior, and improve aircraft design, is termed as Numerical Fluid Flow Analysis. For the purpose of improving the modelling of Computational Fluid Dynamics (CFDs) in aerospace engineering, a high-performance Deep Learning (DL) framework that integrates Numerical Fluid Flow Analysis simulations and data-driven neural architectures is presented in this study. This study speeds up aerodynamic analysis, cut down computational costs, and maintain physical accuracy, this will support in attaining optimized airplane structure, main goal of this study. Standard CFD solvers with sophisticated DL-based approaches, like Numerical Fluid Flow Analysis, Physics-Informed AI Network, fusion framework that consider statistical constraints and physical constraints are all integrated in the suggested method. A precise prediction regarding critical aerodynamic characteristics like lifting and dragging, pressure distribution, and turbulent kinetic energy over various air foil configurations (NACA 2412, 4415, and 0012) is offered by this framework based on high-fidelity CFD datasets and experimental wind tunnel data. From the flow fields, spatial-temporal features are extracted automatically with the DL frameworks. Identifying nonlinear interactions within boundary layers and turbulent zones are mostly computationally expensive when resolved using conventional numerical solvers. But the DL model has the potential to detect nonlinear interactions within boundary layers and turbulent zones. An average fidelity of 97.2%, accuracy of 98.2%, and F1-score of 97.8% are attained by the suggested model, and it executes better than the baseline architectures like ResNet, MoCo, and Transformer networks, as demonstrated by the comparative analysis. From this analysis, it is validated that the suggested model attains high predictive performance. Robustness and generalization are improved by integrating PINN, as it ensures compliance with governing physical laws (e.g., Navier-Stokes equations). This hybrid model helps in reducing the simulation time up to 60% without impacting fidelity, so this model is considered as an effective tool for real-time aerodynamic design, flight optimization, and future intelligent aerospace systems.