Flight Simulation and Aircraft Autonomy
A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".
Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 16049
Special Issue Editor
Interests: aircraft design; flight simulation and control; urban air mobility; unmanned aerial vehicles; design optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Flight simulation is one of the core disciplines in aerospace and aviation. From the days of early fliers, FS was mostly used as part of the pilot training system, as a design tool for dedicated flight controls, and as a design validation method in the late stages of the aircraft development process. With the recent development of autonomous robotic systems, increasing computational and data transfer capabilities, FS has once again gained popularity. The general principles of FS are preserved but the simulation environment, supporting analysis methods, validation methods, and application area have dramatically expanded and improved. Freely available and supported by community flight simulation and flight dynamics models (AirSim, JSBSim, Gazebo and others) and widely available in academia (MATLAB Simulink and other FS tools), they reduce the entry barrier to specialists from different fields, especially computer science. FS today runs faster, is more accurate, and is very flexible for integration. Research on robotic autonomy requires collaborative work of multidisciplinary specialists from aerospace, civil engineering, computer science, chemical engineering, and even non-technical disciplines. This Special Issue aims to collect publications on innovative research on the following topics, with a special focus on the autonomy technologies of aircraft.
- Software-in-the-loop simulation
- Hardware-in-the-loop simulation
- Improving accuracy of FS
- Construction and validation of simulation database (look-up-tables)
- Calibration of simulation models using experimental data
- Artificial intelligence and machine learning for FS
- Simulation methods for novel types of aircraft and systems
- eVTOL
- Hydrogen powered aircraft
- Distributed electric propulsion
- Others
- Simulation validation methods
- Advanced flight control algorithms
- Aircraft autonomy
- Path and mission planning
- UAV swarm
- Situational awareness and decision making
- Obstacle detection and avoidance
- Artificial intelligence and machine learning
- Applications of FS
- Digital twin systems
- Virtual certification
- Pilot training
- Traffic management
Prof. Dr. Maxim Tyan
Guest Editor
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