Nonlinear problems have drawn great interest and extensive attention from engineers,
physicists and mathematicians and many other scientists because most real systems are …

An approach for the generation of aerodynamic tables using computational fluid dynamics is
discussed. For aircraft flight dynamics, forces and moments are often tabulated in multi …

Machine learning and artificial intelligence algorithms typically require a large amount of
data for training. This means that for nonlinear aeroelastic applications, where small training …

The identification of optimally sparse Taylor partial derivatives presents a new opportunity in
efficient nonlinear model reduction for complex aeroelastic systems. Unfortunately, for this …

Reduced-order modelling (ROM) methods are applied to the Computational Fluid Dynamics
(CFD)-based aeroelastic analysis of the AGARD 445.6 wing in order to gain insight …

A reduced-order model for unsteady aerodynamic calculations across a range of Mach
regimes based on linear convolution and a nonlinear correction factor is developed …

The generation of reduced-order models for computing the unsteady and nonlinear
aerodynamic loads on an aircraft from pitching motions in the transonic speed range is …

Carbon fiber composites have been widely used in rotor blades due to their excellent
mechanical performance, lightweight and designing flexibility. The modern fluid–structure …

Nonlinear aeroelastic reduced-order models (ROMs) based on machine learning or artificial
intelligence algorithms can be complex and computationally demanding to train, meaning …

With the development of the large scale numerical simulation, computational fluid dynamics
and computational solid mechanics couplesimulation becomes the powerful accurate tool in …