| People | Locations | Statistics |
|---|---|---|
| Mouftah, Hussein T. |
| |
| Dugay, Fabrice |
| |
| Rettenmeier, Max |
| |
| Tomasch, Ernst | Graz |
|
| Cornaggia, Greta |
| |
| Palacios-Navarro, Guillermo |
| |
| Uspenskyi, Borys V. |
| |
| Khan, Baseem |
| |
| Fediai, Natalia |
| |
| Derakhshan, Shadi |
| |
| Somers, Bart | Eindhoven |
|
| Anvari, B. |
| |
| Kraushaar, Sabine | Vienna |
|
| Kehlbacher, Ariane |
| |
| Das, Raj |
| |
| Werbińska-Wojciechowska, Sylwia |
| |
| Brillinger, Markus |
| |
| Eskandari, Aref |
| |
| Gulliver, J. |
| |
| Loft, Shayne |
| |
| Kud, Bartosz |
| |
| Matijošius, Jonas | Vilnius |
|
| Piontek, Dennis |
| |
| Kene, Raymond O. |
| |
| Barbosa, Juliana |
|
Widhalm, Markus
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (22/22 displayed)
- 2023Comparison of Reduced Order Models for Evaluating Stability Derivatives for the DLR-F22 ONERA modelcitations
- 2023Expeditious Evaluation of the Dynamic Response of Natural Laminar Flow Configurations to Small Pitching Oscillations
- 2023Nonlinear Low-Dimensional Model Order Reduction with Subspace Interpolation for Gust Applications with the Linear Frequency Domain Approach
- 2023Flight Dynamic Stability Prediction for an Aircraft in Transonic Separated Flow Conditions with a Linear Frequency Domain Solver
- 2022Data-Driven Aerodynamic Modeling Using the DLR SMARTy Toolboxcitations
- 2022DLR Results for the First AIAA Stability and Control Prediction Workshop
- 2020Linear Frequency Domain Method For Aerodynamic Applications
- 2020Load Control for Unsteady Gusts with Control Surfaces using the Linear Frequency Domain
- 2020Efficient Prediction of Aerodynamic Control Surface Responses Using the Linear Frequency Domaincitations
- 2019Linear Frequency Domain Method for Load Control by Fluidic Actuation
- 2017Efficient Evaluation of Dynamic Response Data with a Linearized Frequency Domain Solver at Transonic Separated Flow Conditionscitations
- 2015Lagrangian Trajectory Simulation of Rotating Regular Shaped Ice Particlescitations
- 2014Evaluating Stability Derivatives with a Linearised Frequency Domain Solver
- 2013Linear Frequency Domain and Harmonic Balance Predictions of Dynamic Derivativescitations
- 2013Linear Frequency Domain and Harmonic Balance Predictions of Dynamic Derivativescitations
- 2012LINEAR FREQUENCY DOMAIN PREDICTIONS OF DYNAMIC DERIVATIVES FOR THE DLR F12 WIND TUNNEL MODEL
- 2012State of the art at DLR in solving aerodynamic shape optimization problems using the discrete viscous adjoint method
- 2012Efficient polar optimization of transport aircraft in transonic RANS flow using adjoint gradient based approach
- 2010Efficient Computation of Dynamic Stability Data with a Linearized Frequency Domain Solver.
- 2008Lagrangian Particle Tracking on Large Unstructured Three-Dimensional Meshes
- 2007Improvement of the Automatic Grid Adaptation for Vortex Dominated Flows using Advanced Vortex Indicators with the DLR-TAU Code.
- 2007Comparison between Gradient-free and Adjoint Based Aerodynamic Optimization of a Flying Wing Transport Aircraft in the Preliminary Design.
Places of action
| Organizations | Location | People |
|---|
conferencepaper
Data-Driven Aerodynamic Modeling Using the DLR SMARTy Toolbox
Abstract
From aircraft design to certification a huge amount of aerodynamic data is needed for the entire flight envelope including pressure and shear stress distributions, global coefficients as well as derivatives. The goal of data-driven methods is to provide aerodynamic data based on various data-sources but with lower evaluation time and storage than the original models. These data-sources might include flight tests, wind tunnel experiments or numerical simulations, and they are often available at various levels of fidelity, ranging from simple hand book methods over high-fidelity numerical simulations to in-flight measurements. Within the past few years, the demand for efficient exploitation and exploration of these data sets became evident to further enhance existing designs and approaches, evaluate new technical capabilities and foster the availability of high-fidelity aerodynamic data in closely related disciplines. The German Aerospace Center is continuously developing the Surrogate Modeling for Aero-Data Toolbox in python (SMARTy) with the aim of providing state-of-the-art data-driven techniques for both, developers and practical engineers. SMARTy is designed following an Application Programming Interface approach that enables easy combination of different modules into larger, complex applications. Moreover, integration into multi-disciplinary analysis and optimization workflows is possible relying on the FlowSimulator. The SMARTy capabilities are highlighted herein by means of several application cases. This includes surrogate modeling, multi-fidelity modeling, data fusion, reduced order modeling, deep learning as well as highly integrated tasks such as surrogate-based robust design, intrusive reduced order modeling for unsteady responses or data-driven turbulence modeling.
Topics
Search in FID move catalog