Data-Driven Aerodynamic Modeling
A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".
Deadline for manuscript submissions: 30 April 2024 | Viewed by 2204
Special Issue Editors
Interests: data-driven modeling; aerodynamics; reduced-order modeling; machine learning; computational fluid dynamics; uncertainty management; numerical simulation; fluid–structure interaction; aeroelasticity; robust design; intrusive methods; data fusion
Interests: aerodynamics; computational fluid dynamics; multidisciplinary analysis and multidisciplinary optimization; shape optimization; uncertainties; robust design; machine learning; surrogate and reduced-order modeling; data fusion; physics-informed neural networks; expert systems
Special Issue Information
Dear Colleagues,
Data-driven modeling in general and machine learning techniques in particular have transformed our everyday life over the past few years. In areas for which vast amounts of data are available, the aforementioned techniques have achieved remarkable success, especially when mathematical models are lacking. Instead, aerodynamic tools such as computational fluid dynamics solvers rely on first principles that directly enable us to describe and investigate system behavior. Numerical simulation tools derived from these principles have become invaluable in aircraft design and are about to significantly contribute to the green transformation of the aviation sector. However, such tools are far from perfect and suffer from several shortcomings, e.g., computational cost may become prohibitive once a large number of simulations are required, or there is the problem of deriving accurate and reliable turbulence models to describe small-scale turbulent flow behavior. Data-driven modeling is generally regarded as a promising approach to enhance and complement existing aerodynamic methods and tools to circumvent some of these shortcomings and to improve physical modeling. This Aerospace Special Issue covers recent advances in data-driven aerodynamic modeling including surrogate and reduced-order modeling; machine learning for aerodynamics; data fusion; uncertainty propagation and management; aerodynamic shape optimization, physics-informed neural networks, and data-driven turbulence modeling. The Guest Editors of this Special Issue invite authors to submit papers addressing topics in the aforementioned areas of data-driven modeling, with a special focus on aerodynamic applications.
Dr. Philipp Bekemeyer
Prof. Dr. Stefan Görtz
Guest Editors
Manuscript Submission Information
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