Data-driven modeling for unsteady aerodynamics and aeroelasticity
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …
addition to experiment and numerical simulation, due to its low-dimensional representation …
Transonic aeroelasticity: A new perspective from the fluid mode
Within the transonic regime, the aeroelastic problems exhibit many unique characteristics
compared with subsonic and supersonic cases. Although a lot of research has been carried …
compared with subsonic and supersonic cases. Although a lot of research has been carried …
Machine learning methods for turbulence modeling in subsonic flows around airfoils
In recent years, the data-driven turbulence model has attracted widespread concern in fluid
mechanics. The existing approaches modify or supplement the original turbulence model by …
mechanics. The existing approaches modify or supplement the original turbulence model by …
Fast predictions of aircraft aerodynamics using deep-learning techniques
C Sabater, P Stürmer, P Bekemeyer - AIAA Journal, 2022 - arc.aiaa.org
The numerical analysis of aerodynamic components based on the Reynolds–averaged
Navier–Stokes equations has become critical for the design of transport aircraft but still …
Navier–Stokes equations has become critical for the design of transport aircraft but still …
Graph neural networks for the prediction of aircraft surface pressure distributions
D Hines, P Bekemeyer - Aerospace Science and Technology, 2023 - Elsevier
Aircraft design requires a multitude of aerodynamic data and providing this solely based on
high-quality methods such as computational fluid dynamics is prohibitive from a cost and …
high-quality methods such as computational fluid dynamics is prohibitive from a cost and …
Multi-fidelity nonlinear unsteady aerodynamic modeling and uncertainty estimation based on Hierarchical Kriging
By fusing aerodynamic data from multiple sources, multi-fidelity methods can well balance
model accuracy and computational cost. To extend multi-fidelity models for predicting …
model accuracy and computational cost. To extend multi-fidelity models for predicting …
Neural networks-based aerodynamic data modeling: A comprehensive review
L Hu, J Zhang, Y **ang, W Wang - IEEE Access, 2020 - ieeexplore.ieee.org
This paper reviews studies on neural networks in aerodynamic data modeling. In this paper,
we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced …
we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced …
A hybrid reduced-order framework for complex aeroelastic simulations
This paper develops a hybrid and parallel-structured reduced-order framework for modeling
unsteady aerodynamics, which incorporates both linear and nonlinear system identification …
unsteady aerodynamics, which incorporates both linear and nonlinear system identification …
Nonlinear aeroelastic prediction in transonic buffeting flow by deep neural network
Transonic buffet is an aerodynamic phenomenon of self-sustained shock oscillations. The
aeroelastic problem caused by it is very complex, including two different dynamic modes …
aeroelastic problem caused by it is very complex, including two different dynamic modes …
[HTML][HTML] Multi-fidelity modeling framework for nonlinear unsteady aerodynamics of airfoils
Aerodynamic data can be obtained from different sources, which vary in fidelity, availability
and cost. As the fidelity of data increases, the cost of data acquisition usually becomes …
and cost. As the fidelity of data increases, the cost of data acquisition usually becomes …