Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
An improved criterion to select dominant modes from dynamic mode decomposition
Dynamic mode decomposition (DMD) has been extensively utilized to analyze the coherent
structures in many complex flows. Although specific flow patterns with dominant frequency …
structures in many complex flows. Although specific flow patterns with dominant frequency …
Data-driven aerodynamic modeling using the DLR SMARTy toolbox
P Bekemeyer, A Bertram, DA Hines Chaves… - AIAA Aviation 2022 …, 2022 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2022-3899. vid From aircraft design to
certification a huge amount of aerodynamic data is needed for the entire flight envelope …
certification a huge amount of aerodynamic data is needed for the entire flight envelope …
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 …
Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils
Computational-fluid-dynamics-based prediction of unsteady aerodynamics is an essential
research topic in the design of aircraft, which usually requires very high computational cost …
research topic in the design of aircraft, which usually requires very high computational cost …
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 …
Reduced-order models for aerodynamic applications, loads and MDO
M Ripepi, MJ Verveld, NW Karcher, T Franz… - CEAS Aeronautical …, 2018 - Springer
This article gives an overview of reduced-order modeling work performed in the DLR project
Digital-X. Parametric aerodynamic reduced-order models (ROMs) are used to predict …
Digital-X. Parametric aerodynamic reduced-order models (ROMs) are used to predict …
Surrogate modeling of aerodynamic simulations for multiple operating conditions using machine learning
R Dupuis, JC Jouhaud, P Sagaut - Aiaa Journal, 2018 - arc.aiaa.org
This paper describes a methodology, called local decomposition method, which aims at
building a surrogate model based on steady turbulent aerodynamic fields at multiple …
building a surrogate model based on steady turbulent aerodynamic fields at multiple …
Layered reduced-order models for nonlinear aerodynamics and aeroelasticity
A layered reduced-order modeling approach for nonlinear unsteady aerodynamics
comprising both linear and nonlinear characteristics is developed. The constructed reduced …
comprising both linear and nonlinear characteristics is developed. The constructed reduced …
Reduced order modeling methods for aviation noise estimation
A key enabler for sustainable growth of aviation is the mitigation of adverse environmental
effects. One area of concern is community noise exposure at large hub airports serving …
effects. One area of concern is community noise exposure at large hub airports serving …