About
150
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Introduction
Stefan Görtz works at the DLR Institute of Aerodynamics and Flow Technology, where he is the head of the Center for Computer Applications in AeroSpace Science and Engineering (C²A²S²E). He is also Professor of Multidisciplinary Design Optimization at the Institute of Aircraft Design and Lightweight Structures at TU Braunschweig.
His research interests include CFD, data-driven modeling, MDA, MDO, shape optimization; uncertainty quantification, robust design, quantum computing.
Additional affiliations
December 2019 - May 2020
December 2019 - present
July 2016 - present
Education
September 2000 - March 2005
March 2000 - August 2000
August 1997 - October 1998
Publications
Publications (150)
A gradient-based aeroelastic shape optimization framework making use of a reduced order model to substitute a parameterization based on computer-aided design software is presented. This parameterization concept is not novel in principle, but it is embedded here in a complex high-fidelity optimization process and proven for a high-dimensional design...
Data-driven methods have become an essential part of the methodological portfolio of fluid dynamicists, motivating students and practitioners to gather practical knowledge from a diverse range of disciplines. These fields include computer science, statistics, optimization, signal processing, pattern recognition, nonlinear dynamics, and control. Flu...
Global, grad-free Aero-structural optimization of flexible composite wings with basic maneuver load alleviation
First design and MDO results of high aspect ratio wings.
Grad-based MDO of long range aircraft, turbo-fan powered, trimmed
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 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 vario...
A widespread industrial adoption of Computational Fluid Dynamics in early design phases would significantly increase the modelling fidelity compared to classical conceptual design tools. However, relatively high computational cost to obtain comprehensive data sets and a significant manual effort needed for the creation of suitable geometries for me...
Efficient optimization algorithms are required to reduce the computational costs of Multidisciplinary Design Optimization (MDO), motivating the use of gradient-based algorithms. Gradient-based algorithms can efficiently find the nearest local optimum, provided that the computation of the required gradients itself is efficient. They are known to be...
We introduce a framework for statistical inference of the closure coefficients using machine learning methods. The objective of this framework is to quantify the epistemic uncertainty associated with the closure model by using experimental data via Bayesian statistics. The framework is tailored towards cases for which a limited amount of experiment...
The collaboration between academia, research centers and industry has become a key element to maintain the competitiveness of the European aerospace industry. Industrial needs and use cases can highlight open research topics and industrial partners can turn R&D outcome into innovations and products for the market, while research centers help bridgi...
The robustness of laminar wings is critical, both against instabilities that can unexpectedly trigger transition and against off-design conditions outside the cruise point. However, current inverse design methodologies not only provide suboptimal configurations but are unable to come up with robust configurations. The objective of this paper is the...
The development, testing and production of new aircraft are associated with considerable temporal and financial risks due to the product and manufacturing complexity. In order to accelerate the introduction of innovative technologies for more economical, more environmentally friendly and safer air transport vehicles and to better control the techno...
Aviation is undergoing a transformation, fueled by the Corona pandemic, and methods to evaluate new technologies for more economical and environment-friendly flight in a timelier manner and to enable new aircraft to be designed (almost) exclusively using computers are sought after. The DLR project VicToria brings together disciplinary methods and t...
The development, testing and production of new aircraft and helicopters are associated with considerable temporal and financial risks due to the product and manufacturing complexity. In order to accelerate the introduction of innovative technologies for more economical, more environmentally friendly and safer air vehicles and to better control the...
With the continuous increase in the number of commercial flights, environmental and economic concerns are key drivers towards the reduction of aircraft operational cost and emission of greenhouse gasses. The use of aerodynamic shape optimization, regularly carried out in a deterministic fashion, plays a key role in reducing aerodynamic drag and the...
This paper presents an alternative approach to correct wind tunnel data through the use of CFD solutions. The correction is based on the difference between the measured pressures in the wind tunnel and the pressures predicted with CFD in free flow condition, at angles of attack and Mach numbers surround the wind tunnel data point. Optimization is u...
View Video Presentation: https://doi.org/10.2514/6.2021-3025.vid By virtue of using efficient methods to compute the design sensitivities, such as the coupled adjoint methods, gradient-based optimization techniques allow aircraft designers to efficiently obtain an optimum design, that satisfies all considered constraints. The more constraints and d...
Robust optimization strategies typically aim at minimizing some statistics of the uncertain objective function and can be expensive to solve when the statistic is costly to estimate at each design point. Surrogate models of the uncertain objective function can be used to reduce this computational cost. However, such surrogate approaches classically...
The introduction of laminar flow configurations is envisioned to provide new opportunities to further reduce aircraft fuel consumption. The robustness of laminar wings is critical, both against instabilities that can unexpectedly trigger transition and against off-design conditions outside the cruise point. However, current inverse design methodolo...
The development, testing and production of new aircraft are associated with considerable temporal and financial risks due to the product and manufacturing complexity. In order to accelerate the introduction of innovative technologies for more economical, more environmentally friendly and safer air vehicles and to better control the technological ri...
The development, testing and production of new aircraft are associated with considerable temporal and financial risks due to the product and manufacturing complexity. In order to accelerate the introduction of innovative technologies for more economical, more environmentally friendly and safer air vehicles and to better control the technological ri...
The use of robust design in aerodynamic shape optimization is increasing in popularity in order to come up with configurations less sensitive to operational conditions. However, the addition of uncertainties increases the computational cost as both design and stochastic spaces must be explored. The objective of this work is the development of an ef...
The assessment of uncertainties is essential in aerodynamic shape optimization problems to come up with configurations that are more robust against operational and geometrical uncertainties. However, exploring the stochastic design space significantly increases the computational cost. The aim of this paper is to develop a framework for efficient op...
Cybermatrix is a novel approach to aircraft design through multidisciplinary optimization, developed within the DLR project VicToria. It combines three aspects: representing a design problem by an approximate Karush-Kuhn-Tucker system, distributing the rows of the system among disciplinary groups, and employing large computational resources and man...
Within the framework of the project "Uncertainty Management for Robust Industrial Design in Aeronautics" (UMRIDA), funded by the European Union, several machine learning-based predictive models were compared in terms of their efficiency in estimating statistics of aerodynamic performance of aerofoils. The results show that the models based on both...
Over the past decade, profound attention was given to exploring the benefits of engaging numerical multidisciplinary design optimization in aircraft design. Due to its importance, aerostructural wing design optimization is the most visited multidisciplinary problem in research institutes. To deal with this problem efficiently, gradient-based algori...
This paper presents the cybermatrix protocol, a novel approach to multidisciplinary design optimization in the contex of many involved disciplinary experts and high use of high-performance computing resources. The approach is presented from its formal mathematical background to actual on-disk implementation of a running process. As the demonstratio...
The DLR project VicToria brings together disciplinary methods and tools of different fidelity for collaborative multidisciplinary design optimization (MDO) of long-range passenger aircraft configurations, necessitating the use of high-performance computing. Three different approaches are being followed to master complex interactions of disciplines...
Robust optimization strategies typically aim at minimizing some statistics of the uncertain objective function and can be expensive to solve when the statistic is costly to estimate at each design point. Surrogate models of the uncertain objective function can be used to reduce this computational cost. However, such surrogate approaches classically...
Parametric reduced order models (ROMs) for both steady and unsteady aerodynamic applications are presented. The focus is on compressible, turbulent flows with shocks. We consider ROMs combining proper orthogonal decomposition (POD), Isomap, which is a manifold learning method, and autoencoder networks with interpolation methods as well as physics-b...
DLR’s Remote Component Environment (RCE) is an open-source software environment for defining and executing workflows containing distributed simulation tools by integrating them into a peer-to-peer network. It is being developed primarily by DLR and has been used in various engineering projects, including several aerospace projects dealing with mult...
A tremendous number of gust-load cases need to be computed during the aircraft design and certification process. From an aerodynamic point of view, gust-load predictions in industry rely on linear potential flow methods, which are inappropriate at transonic flight conditions. Prediction accuracy can be enhanced by accounting for aerodynamic loads c...
The assessment of uncertainties is essential in aerodynamic shape optimization problems in order to come up with configurations that are more robust. The influence of aleatory fluctuations in flight conditions and manufacturing tolerances is of primary concern when designing shock control bumps, as their effectiveness is highly sensitive to the sho...
In this article gappy proper orthogonal decomposition (POD) is used to fuse wind-tunnel measurements and computational fluid dynamics (CFD) data to provide a consistent and more comprehensive output of greater utility. The technique is used to fuse a very limited and ‘gappy’ set of wind-tunnel surface pressure measurements with computational surfac...
Efficient surrogate modeling approaches are presented in the context of robust design. The type of surrogate model, and the number and distribution of the sample points are discussed. The test case is the UMRIDA BC-02 airfoil with two uncertain operational and 10 uncertain geometrical parameters. Statistics of the quantity of interest (QoI) are eva...
This chapter introduces two popular surrogate modeling methods which can be used to quantify uncertainties such as statistics of the aerodynamic coefficients from scattered data obtained by computational fluid dynamics (CFD) simulations. One is Kriging, which is able not only to interpolate predicted data but also to provide statistical information...
A large number of geometrical uncertainties of a transonic RAE2822 airfoil are parameterized by a truncated Karhunen–Loève expansion (KLE), and the influence of the truncation on the statistics of aerodynamic quantities is investigated both in terms of efficiency and accuracy. Direct integration of a very large number of quasi-Monte Carlo samples c...
Two kinds of robustness measures are introduced and applied to design optimization of the UMRIDA BC-02 transonic airfoil test case under uncertainty. Robust design optimization (RDO) aims at minimizing the mean and standard deviation of the drag coefficient. Reliability-based design optimization (RBDO) targets minimizing the maximum drag coefficien...
Different surrogate models are compared in terms of their efficiency in estimating statistics of aerodynamic coefficients of the RAE2822 airfoil due to geometric input uncertainties. A comparison with direct integration and polynomial chaos methods is also performed. The aerodynamic coefficients and their partial gradients with respect to the uncer...
This chapter introduces two popular surrogate modeling methods which can be used to quantify uncertainties such as statistics of the aerodynamic coefficients from scattered data obtained by Computational Fluid Dynamics (CFD) simulations. One is Kriging, which is able not only to interpolate predicted data but also provides statistical information a...
Different surrogate models are compared in terms of their efficiency in estimating statistics of aerodynamic coefficients of the RAE2822 airfoil due to geometric input uncertainties. A comparison with direct integration and polynomial chaos methods is also performed. The aerodynamic coefficients and their partial gradients with respect to the uncer...
A large number of geometrical uncertainties of a transonic RAE2822 airfoil is parameterized by a truncated Karhunen-Loève expansion (KLE) and the influence of the truncation on the statistics of aerodynamic quantities is investigated both in terms of efficiency and accuracy. Direct integration of a very large number of quasi Monte Carlo samples com...
Two kinds of robustness measures are introduced and applied to design optimization of the UMRIDA BC-02 transonic airfoil test case under uncertainty. Robust design optimization (RDO) aims at minimizing the mean and standard deviation of the drag coefficient. Reliability-based design optimization (RBDO) targets minimizing the maximum drag coefficien...
Efficient surrogate modeling approaches are presented in the context of robust design. The type of surrogate model and the number and distribution of the sample points are discussed. The test case is the UMRIDA BC-02 airfoil with two uncertain operational and 10 uncertain geometrical parameters. Statistics of the quantity of interest (QoI) are eval...
Surrogate models have become a popular choice to enable the inclusion of high-dimensional, physics-based computational models in time-critical processes such as design, optimization and uncertainty quantification. Among the vast amount of different surrogate modeling strategies Kriging is one of the most promising offering accurate and rapid predic...
A gradient-based aeroelastic shape optimization framework making use of a reduced or model to substitute a parameterization based on computer-aided design software is presented. The design software is used initially to generate a parametric model of a three-dimensional transport aircraft configuration. To streamline the actual optimization process,...
A tremendous number of gust load cases needs to be computed during the aircraft design and certification process. From an aerodynamic point of view, gust loads predictions in industry rely on linear potential flow methods which are inappropriate at transonic flight conditions. Prediction accuracy can be enhanced by accounting for aerodynamic loads...
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 surface pressure distributions based on high-fidelity computational fluid dynamics (CFD), but at lower evaluation time and storage than the original CFD model. ROMs for steady a...
Reduced-order modeling is evaluated as a means to speed up unsteady computational fluid dynamics (CFD) simulations while maintaining the desired Level of accuracy. In the reduced order modeling approach, proper orthogonal decomposition (POD) is applied to some computed response time history from a compressible, unsteady CFD solver to compute a set...
Aerodynamic shape optimization driven by high-fidelity computational fluid dynamics (CFD) simulations is still challenging, especially for complex aircraft configurations. The main difficulty is not only associated with the extremely large computational cost, but also related to the complicated design space with many local optima and a large number...
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 surface pressure distributions based on high-fidelity computational fluid dynamics (CFD), but at lower evaluation time and storage than the original CFD model. ROMs for steady a...
Aerodynamic shape optimization driven by high-fidelity computational fluid dynamics (CFD) simulations is still challenging, especially for complex aircraft configurations. The main difficulty is not only associated with the extremely large computational cost, but also related to the complicated design space with many local optima and a large number...
Reduced-order modeling is evaluated as a means to speed up unsteady computational fluid dynamics (CFD) simulations while maintaining the desired level of accuracy. In the reduced order modeling approach, proper orthogonal decomposition (POD) is applied to some computed response time history from a compressible, unsteady CFD solver to compute a set...
Im DLR-Projekt VicToria werden die Grundlagen für die vollständige digitale Entwicklung und Beschreibung von Flugzeugen und Helikoptern gelegt. In diesem Übersichtsbeitrag werden neben der Zielsetzung des Projektes erste Ergebnisse hinsichtlich der weiterentwickelten numerischen und experimentellen Werkzeuge, der Weiterentwicklung der MDO-Fähigkeit...
In this talk we present a robust design optimization framework for aircraft design and show results for robust aerodynamic design. As a first step, we focus on quantifying uncertainties in the drag coefficient and formulate and investigate two measures of robustness, a worst-case scenario and a formulation based on expectation measure and mean-risk...
We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approximately isometric to the manifold that is assumed to b...
This work gives an overview of the nonlinear reduced order modeling (ROM) approach for unsteady aerodynamics developed by DLR, and its application to gust simulations. Here, physics-based ROMs based on high-fidelity CFD are used to predict approximate aerodynamic solutions in terms of the pressure distribution for different gust wave lengths and am...
DLR's work on developing a distributed collaborative MDO environment is presented. A multi-level Approach combining high-fidelity MDA for aerodynamics and structures with conceptual aircraft design methods is employed. Configuration-specific sizing loads are evaluated and used for sizing the structure. A gradient-free optimization algorithm is used...
The AVT-191 Task Group was organized to evaluate the maturity and suitability of a number of new and established sensitivity analysis and uncertainty methods on realistic problems of interest to NATO. The Task Group evaluated several uncertainty methods on four representative problems: external aerodynamics, aeroelasticity, hydrodynamics, and inter...
This paper summarizes the results of international collaborative work on applying numerical methods for uncertainty quantification to a generic missile configuration. This work was conducted as part of the NATO STO Task Group AVT-191 on Application of Sensitivity Analysis and Uncertainty Quantification to Military Vehicle Design. Three different CF...
DLR’s work on developing a distributed collaborative MDO environment is presented. A multi-level approach combining high-fidelity MDA for aerodynamics and structures with conceptual aircraft design methods is employed. Configuration-specific critical loads are evaluated and used for sizing the structure. A gradient-free optimization algorithm is us...
This work gives an overview of Reduced Order Model (ROM) applications employed within the Digital-X project. Parametric aerodynamic ROMs for both steady and unsteady aerodynamic applications combining POD and Isomap with interpolation methods are presented as well as a ROM of corrected Aerodynamic Influence Coefficients (AICs). The ROMs are used to...
DLR has developed a distributed collaborative MDO environment. A multi-level approach combining highly-fidelity MDA for aerodynamics and structures with conceptual aircraft design methods is employed. Configuration-specific critical loads are used for sizing the structure. It is used to optimize the fuel burn of a long-range wide-body transonic tra...
We present our framework for Robust Design Optimization (RDO) and Reliability-Based Design Optimization (RBDO) and apply it to the UMRIDA BC-02 test case. Two different measures of robustness are defined and evaluated considering both operational uncertainties and a large number of geometrical uncertainties. Here the quantity of interest is the dra...
This paper deals with developing an efficient Robust Design Optimization (RDO) framework. The goal is to obtain an aerodynamic shape that is less sensitive to small random geometry perturbations and to uncertain operational conditions. The initial shape is the RAE2822 airfoil which is parameterized with 10 design variables. The robust design formul...
We will present surrogate-based robust shape optimization of transonic airfoils. A large number of geometrical uncertainties on the airfoil is modeled by a truncated Karhunen-Loève expansion to achieve a dramatic reduction of the large number of parameters. The combination of quasi Monte Carlo sampling and gradient-enhanced Kriging enables us to ef...
The advent and development of large-scale high-fidelity computational fluid dynamics (CFD) in aircraft design is requiring, more and more, procedures and techniques aimed at reducing its computational cost in order to afford accurate but fast simulations of, e.g., the aerodynamic loads. The adoption of reduced order modeling techniques in CFD repre...
The advent and development of large-scale high-fidelity computational fluid dynamics (CFD) in aircraft design is requiring, more and more, procedures and techniques aimed at reducing its computational cost in order to afford accurate but fast simulations of, e.g., the aerodynamic loads. The adoption of reduced order modeling techniques in CFD repre...
Numerical simulation is already an important
cornerstone for aircraft design, although the application of highly accurate methods is mainly limited to the design point. To meet future technical, economic and social challenges in aviation, it is essential to simulate a real aircraft at an early stage, including all multidisciplinary interactions cov...
In this article, we propose a strategy for speeding-up the computation of the aerodynamics of industrial high-lift configurations using a residual-based reduced-order model (ROM). The ROM is based on the proper orthogonal decomposition (POD) of a set of solutions to the Navier-Stokes equations governing fluid flow at different parameter values, fro...
In aerodynamic applications, many model reduction methods use proper orthogonal decomposition (POD). In this work, a POD-based method, called missing point estimation (MPE), is modified and applied to steady-state flows with variation of the angle of attack. The main idea of MPE is to select a subset of the computational grid points (control volume...
The quantification of aerodynamic uncertainties due to random variations of aircraft geometry often involves large number of variables which necessitate model reduction techniques, e.g. by truncated Karhunen-Loeve expansion (KLE). This, however, comes along with some additional uncertainty in a twofold sense, i.e. a loss in variance caused by the m...