Physics-based, High-Fidelity Computational modelling for Aerospace Application
A special issue of Aerospace (ISSN 2226-4310).
Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 6396
Special Issue Editors
Interests: aerodynamics; fluid mechanics; structural dynamics; numerical analysis; cfd simulation; finite element analysis; computational fluid dynamics; numerical simulation; modeling and simulation; engineering thermodynamics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear colleagues,
A remarkable progress in computational multi-physics and multi-disciplinary aero-sciences has been achieved over the last two decades, driven by the rightful decision to reduce the footprint of aviation on the environment and to improve the well-being of the communities situated around airports. This Special Issue wants to provide the current state-of-the-art in the development and deployment of high-fidelity computational methods for the analysis and simulation of aerospace vehicles by collecting multi-faceted contributions in key areas, such as: the analysis of low-speed stall, the appearance and evolution of stall cells for clean and take-off/landing wing configurations; characterization of flow separations and turbulent structures, (identification of) noise sources and dynamic loads for slats/flats installations and landing gear configurations; ice accretion and the impact of icing on the vehicle performances; the interactions between the propulsive system and the airframe, with associated thermal, acoustic, fatigue and radar cross-section issues; off-design conditions, including buffeting and fluid-structure interaction problems during gust encounters and manoeuvres. Hybrid RANS-LES turbulence modelling techniques such as DDES, IDDES, ZDES, WMLES are of particular relevance along with special techniques based on the acoustic analogies in order to estimate the unsteady noise pressure fluctuations. Additional areas of interest of this Special Issue are physics-based and data-driven modelling, such as deep learning, applied to cases of practical relevance, not limited to the cases above mentioned. The application of such methods to optimisation as well as the propagation of uncertainties (aleatory uncertainties in flow and flight conditions and/or geometry as well as epistemic uncertainties, e.g., concerned with turbulence modelling) onto quantities of interest related to performance and noise are also very appreciated.
Prof. Dr. Andrea Da-Ronch
Prof. Dr. Marcello Righi
Guest Editors
Manuscript Submission Information
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Keywords
- multi-physics
- high-fidelity
- aerospace
- uncertainty quantification
- turbulence modelling
- deep learning
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