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| Autore: |
Jenkins Karl
|
| Titolo: |
Computational Aerodynamic Modeling of Aerospace Vehicles
|
| Pubblicazione: | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica: | 1 online resource (294 p.) |
| Soggetto topico: | History of engineering and technology |
| Soggetto non controllato: | aerodynamic performance |
| aerodynamics | |
| aeroelasticity | |
| after-body | |
| angle of attack | |
| bifurcation | |
| bluff body | |
| CFD | |
| characteristics-based scheme | |
| chemistry | |
| computational fluid dynamics | |
| computational fluid dynamics (CFD) | |
| convolution integral | |
| CPACS | |
| DDES | |
| detection | |
| discontinuous Galerkin finite element method (DG-FEM) | |
| dynamic Smagorinsky subgrid-scale model | |
| Euler | |
| flexible wings | |
| flow control | |
| flow distortion | |
| fluid mechanics | |
| flutter | |
| formation | |
| gasdynamics | |
| geometry | |
| Godunov method | |
| high angles of attack | |
| hybrid reduced-order model | |
| hypersonic | |
| installed propeller | |
| kinetic energy dissipation | |
| large eddy simulation | |
| MDO | |
| meshing | |
| microelectromechanical systems (MEMS) | |
| microfluidics | |
| modeling | |
| modified equation analysis | |
| multi-directional | |
| multi-fidelity | |
| MUSCL | |
| neural networks | |
| numerical dissipation | |
| numerical methods | |
| overset grid approach | |
| p-factor | |
| quasi-analytical | |
| RANS | |
| reduced order aerodynamic model | |
| reduced-order model | |
| Riemann solver | |
| S-duct diffuser | |
| sharp-edge gust | |
| shock-channel | |
| slender-body | |
| square cylinder | |
| subsonic | |
| Taylor-Green vortex | |
| truncation error | |
| turbulence model | |
| unsteady aerodynamic characteristics | |
| URANS | |
| variable fidelity | |
| VLM | |
| vortex generators | |
| wake | |
| wind gust responses | |
| wind tunnel | |
| wing-propeller aerodynamic interaction | |
| Persona (resp. second.): | GhoreyshiMehdi |
| Sommario/riassunto: | Currently, the use of computational fluid dynamics (CFD) solutions is considered as the state-of-the-art in the modeling of unsteady nonlinear flow physics and offers an early and improved understanding of air vehicle aerodynamics and stability and control characteristics. This Special Issue covers recent computational efforts on simulation of aerospace vehicles including fighter aircraft, rotorcraft, propeller driven vehicles, unmanned vehicle, projectiles, and air drop configurations. The complex flow physics of these configurations pose significant challenges in CFD modeling. Some of these challenges include prediction of vortical flows and shock waves, rapid maneuvering aircraft with fast moving control surfaces, and interactions between propellers and wing, fluid and structure, boundary layer and shock waves. Additional topic of interest in this Special Issue is the use of CFD tools in aircraft design and flight mechanics. The problem with these applications is the computational cost involved, particularly if this is viewed as a brute-force calculation of vehicle's aerodynamics through its flight envelope. To make progress in routinely using of CFD in aircraft design, methods based on sampling, model updating and system identification should be considered. |
| Titolo autorizzato: | Computational Aerodynamic Modeling of Aerospace Vehicles ![]() |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910346677003321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |