Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

The paper proposes a novel cost-effective framework that combines deep learning
convolutional neural network (CNN) and blade element momentum (BEM) models for …

This paper focuses on the temporal prediction of unsteady flow based on a combination of
mesh transformation and deep learning technology. To this end, a body-fitted mesh …

In order to obtain the information about flow field, traditional computational fluid dynamics
methods need to solve the Navier-Stokes equations on the mesh with boundary conditions …

To alleviate the computational burden associated with the computational fluid dynamics
(CFD) simulation stage and improve aerodynamic optimization efficiency, this work develops …

Recently, there has been a growing interest for mixed-categorical meta-models based on
Gaussian process (GP) surrogates. In this setting, several existing approaches use different …

Airfoil optimization is the key to improving the aerodynamic performance of a rotor. However,
conventional optimization approaches cannot modify the airfoil shape intelligently in the way …

Inverse design is an important part of the initial design stage of blade profile, while the
traditional inverse design methods are highly dependent on design experience and physical …

The traditional optimization of airfoils relies on, and is limited by, the knowledge and
experience of the designer. As a method of intelligent decision-making, reinforcement …

Unmanned aerial vehicles (UAVs) are increasingly attracting investment and development
attention from many countries all over the world due to their great advantages. However, one …