@phdthesis{,
    author = {Di Fabbio, Tony},
    title = {Efficient Turbulence Modelling for Vortical Flows from Swept Leading Edges},
    editor = {},
    booktitle = {},
    series = {},
    journal = {},
    address = {},
    publisher = {},
    edition = {},
    year = {2024},
    isbn = {},
    volume = {},
    number =  {},
    pages = {},
    url = {},
    doi = {},
    keywords = {Computational Fluid Dynamics, Turbulence Modelling, Transonic Aerodynamics, Delta-wing, Vortex Breakdown,  Machine Learning, Gene Expression Programming},
    abstract = {This doctoral research addresses the complexities of aerodynamic design for high-performance fighter aircraft, focusing particularly on the prediction and analysis of leading-edge vortices in delta-wing configurations. The study stems from the necessity to enhance CFD methodologies for improving the fidelity of aerodynamic simulations, which are crucial for designing and optimizing future combat aircraft. It aims to contribute to the advancement of CFD by providing a more accurate, efficient, and versatile tool for predicting the aerodynamic performance of future fighter aircraft. The dissertation delineates the theoretical foundations of vortex-dominated flows and the challenges in accurately simulating such phenomena, especially under high angles of attack and transonic flight conditions. Results from CFD simulations are validated against experimental data, employing both Unsteady RANS and scale-resolving simulations to assess the applicability of current CFD methods. The simulations investigate vortex-dominated flow, analyzing the patterns of leading-edge vortices, shock-vortex and vortex-vortex interactions, with a particular emphasis on accurately predicting the breakdown of leading-edge vortices. A theory and explanation for the occurrence of this phenomenon is provided. It elucidates the reasons behind the inaccuracies of RANS simulation predictions and identifies the limitations of the Spalart-Allmaras one-equation turbulence model. Aiming to significantly improve its predictive capabilities regarding vortex formation and breakdown, a straightforward modification to the SA model is proposed, and the enhanced accuracy of the developed model is presented. The model’s application to complex aircraft configurations showcases its potential to impact the aerodynamic design process in academic research and industrial practice. A novel aspect of this research is the integration of machine learning techniques to optimize RANS turbulence models. By employing a CFD-driven machine learning framework, specifically Gene Expression Programming, the dissertation innovates in calibrating and enhancing turbulence models. This approach addresses the specific aerodynamic challenges posed by delta wings and contributes broadly to the field of aerodynamics by offering insights into the fundamental physics governing turbulent flows. The developed one-equation RANS turbulence model represents a significant step forward for understanding and predicting complex vortex flows, with implications extending beyond aerospace engineering to include a wide range of fluid dynamics applications.},
    note = {},
    
    school = {Universität der Bundeswehr München},
    
}