@inbook{05243a83f432431084c17cee23909281,
title = "Spalart-Allmaras Turbulence Model Conditioning for Leading-Edge Vortex Flows",
abstract = "The physical modeling of turbulence for the partial differential equations closure used to numerically solve large-scale leading-edge vortex flows maintains a significant grade of complexity and interest in the research community. This plays a major factor for the discrepancies with experimental or high fidelity data. A baseline common turbulence model is enhanced by means of a series of additional destruction terms which are formulated with a correlated physical feature of vortical flows as fundament and calibrated by means of an automatic gradient descent optimization procedure towards experimental data. The numerical accuracy is augmented for a certain cluster of cases around the calibration case in the parametric space which describes the classification of vortex flows over highly swept aerodynamic planforms.",
keywords = "Modeling enhancement, Optimization, Turbulence modeling, Vortex flow",
author = "Matteo Moioli and Christian Breitsamter and Kaare S{\o}rensen-Libik",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.",
year = "2025",
doi = "10.1007/978-3-031-69425-7_8",
language = "English",
series = "Notes on Numerical Fluid Mechanics and Multidisciplinary Design",
publisher = "Springer Nature",
pages = "129--144",
booktitle = "Notes on Numerical Fluid Mechanics and Multidisciplinary Design",
}