Date and Place: Thursdays and hybrid (live in 32349/online via Zoom). For detailed dates see below!
Content
In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everbody interested in the topics is welcome.
List of Talks

Thu18Apr2019
11:30SC Seminar Room 32349
Patrick Mischke, TU Kaiserslautern
Title:
Implementation of a POD Based Surrogate Model for Aerodynamic Shape OptimizationAbstract:
Computational Fluid Dynamics (CFD) simulations are often used with aerodynamic shape optimization in mind. There exist several approaches to perform optimization, for example employing one shot methods or using adjoint computations by utilizing algorithmic differentiation techniques while running the flow solver. However, it may sometimes be preferred to inspect the flow fields across the whole design space, for instance if those strategies seem to operate too local for the problem on hand. This issue can be targeted by the use of surrogate models. A surrogate model predicts the flow field for a given design input in a computationally cheaper way, but also with less accuracy than the full fluid dynamics simulation. The surrogate model uses a set of training data to find reasonable model parameters for future design inputs. The goal of my bachelor thesis, that I present in this talk, was the implementation of an surrogate model for CFD using Proper Orthogonal Decomposition (POD) and Kriging regression. The challenges encountered are discussed, and the NACA 0012 airfoil at transonic flow conditions is presented as test case. The characteristic shock front and the high number of design parameters describing the airfoil geometry of its flow field lead to a rather expensive training process for the surrogate model. However, the discussed approach may still be valuable for other geometries or as foundation for surrogate models using other techniques.

Thu02May2019
11:30SC Seminar Room 32349
Dr.Ing. Bernhard Eisfeld, DLR Braunschweig, Institut für Aerodynamik und Strömungstechnik, C²A²S²E Center for Computer Applications in AeroSpace Science and Engineering
Title:
ReynoldsStress Modelling – Concepts, Advances and ChallengesAbstract:
The ReynoldsAveraged NavierStokes (RANS) equations are still the backbone of numerical flow simulations in industrial applications. Hence, a turbulence model is required for closure, which decides about the accuracy of the predictions.
Many models are based on the assumption of a flow dependent eddy viscosity added to the molecular viscosity of the fluid. While agreeing with the observation of enhanced momentum transfer due to turbulent fluctuations, this is a significant simplification of the physics of turbulent flow, limiting the predictive accuracy in complex flow situations.
Improvement is expected by Reynoldsstress modelling based on the transport equation for the individual components of the Reynoldsstress tensor and for an additional lengthscale providing variable. In this case, the modelling is restricted to the different terms of the Reynoldsstress transport equation and the lengthscale equation that is usually taken over from corresponding eddyviscosity models and considered the weakest link of the approach.
The presentation will introduce the Reynoldsstress transport equation, explain the physical significance of its terms and outline the corresponding modelling approaches.
Recent advances have been achieved by developing a lengthscale correction. The underlying idea will be presented and its improvement on the prediction of separated flows will be demonstrated.
Turbulence modelling is challenged by the variety of flow phenomena that need to be treated. This will be underlined by a theoretical analysis of selfsimilar freeshear flows, predicting a layer of constant Reynoldsstress anisotropy. Experimental data confirm its existence, revealing differences in the turbulence structure between different flows. Hence, a selfadaptive modelling strategy is required, applying tailored models to automatically identified regions of the flow field. An example will be given, demonstrating the potential of such tailored modelling. 
Thu16May2019
11:30SC Seminar Room 32349
Matthias Freimuth, TU Kaiserslautern/MTU Aero Engines
Title:
The multiphysics coupling tool preCICE in the context of adjointbased aeroelastic designsAbstract:
In the emerging field of coupled numerical simulations including two or more physical fields the capabilities of the multiphysics coupling tool preCICE are discussed with respect to aeroelastic design in this talk. With an adapter for the structural solver within the multiphysics solver SU2 a new link between SU2 and preCICE is presented. The development process and the key ingredients for multiphysics coupling are explained and the result is shown with a simple testcase. A smaller aspect also addressed in this talk is the discrete adjoint method for the gradient computation to efficiently optimize in a fluid structure interaction framework. The talk is based on the content of my masters thesis at MTU Aero Engines in Munich and will be given in english.

Thu06Jun2019
14:30SC Seminar Room 32349
Prof. Andrea Walther, Institut für Mathematik, Universität Paderborn
Title:
Minimization by Successive AbsLinearization: Recent DevelopmentsAbstract:
For finite dimensional problems that are unconstrained and piecewise smooth the optimization based on successive abslinearisation is well analysed yielding for example linear or even quadratic convergence under reasonable assumptions on the function to be optimised. In this talk we discuss the extension of this approach to the more general class of nonsmooth but still Lipschitz continuous functions covering also the Euclidean norm. For this purpose, we introduce the socalled clipped root linearisation and present first numerical results.
Furthermore, we sketch the extansion of this approach to the infinite dimensional setting. 
Fri14Jun2019
11:30SC Seminar Room 32349
Manfred Schneider, retired senior confirmed advisor FlightPhysics, Airbus Defense & Space Deutschland GmbH
Title:
Noise Simulation at FTEG highlift airfoil using hybrid RANS/LES ModelAbstract:
This study focuses on the development, validation and application of the interdisciplinary computational fluid dynamics/computational aeroacoustics (CFD/CAA) method with the name FlightPhysics Simulator AEOLus (FPSAEOLus). FPSAEOLus is based on enhanced conservative, anisotropic, hybrid Reynoldsaveraged NavierStokes/ LargeEddy Simulation (RANS/LES) techniques to solve an aerodynamic flow field by applying the unsteady, compressible, hyperbolic Navier–Stokes equations of second order. The twolayer SSG/LRRω differential Reynolds stress turbulence model presented, combining the LaunderReeceRodi (LRR) model near walls with the SpezialeSarkarGatski (SSG) model further apart by applying Menter’s blending function F1. Herein, Menter’s baseline ωequation is exploited for supplying the length scale. Another emphasis is put on the anisotropic description of dissipation at close distance to the solid wall or in wake area for describing the frictioninduced surfaceroughness behaviour in viscous fluid physics and swirling wake effects. For that purpose, the SSG/LRRω sevenequations Reynolds stress turbulence model with anisotropic extension was realized, therefor the theory is described in general. Beyond that, a modified delayed detachededdy simulation (MDDES) and a scale adaptive simulation (SAS) correction to capture the stochastic character of a largeeddytype unsteady flow with massive flow separations in the broad band is implemented. To demonstrate the timedependent noise propagation having wave interference a linearized Euler equation (LEE) model using a combined Momentum and Lambvector source have been applied into the CFD/CAA – method.
The DLR 15 wing, a HighLift device in landing configuration having a deployed slat and landing flap is studied experimentally and numerically. The first part of the application deals with the steady flow investigation; however, the same turbulence model is used for the unsteady flow case without the enclosed time derivatives. The second part concentrates on unsteady modelling for the Navier–Stokes and Linearized Euler field. With this new combined CFD/CAA – method, steady and unsteady numerical studies for the highlift wing configuration for discovering the aerodynamic and –acoustic propagation effects are shown, discussed and when experimental data were available validated. The HighLift wing has a constant sweep angle of Λ=30° to investigate possible crossflow; to realize this, periodic boundary conditions were set in spanwise direction.

Thu04Jul2019
11:30SC Seminar Room 32349
Prof. Xun Huan, Mechanical Engineering, University of Michigan
Title:
Optimal Experimental Design and Bayesian Neural Networks for PhysicsBased ModelsAbstract:
Models and data are two critical components of scientific research: models provide predictions of what data we might observe, and data in turn help refine and advance our models. In this talk, we focus on two important interactions between models and data for complex physical systems: (1) optimal experimental design (OED) for finding the most useful data, and (2) Bayesian neural network (BNN) datadriven models for accelerating expensive predictions with uncertainty quantification.
First, we present the OED framework that systematically quantifies and maximizes the value of experiments. Indeed, some experiments produce more useful data than others, and wellchosen experiments can provide substantial resource savings. We describe a general mathematical framework that accommodates nonlinear and computationally intensive (e.g., ODE and PDEbased) models. The formalism employs Bayesian statistics and an informationtheoretic objective, and we develop tractable numerical methods with demonstrations on designing combustion kinetic experiments and sensor placement for contaminant source inversion.
Next, we introduce BNNs as datadriven surrogate models capable of rapid predictions while quantifying uncertainty, which are highly useful for realtime decisionmaking scenarios. We focus on their use for inflight detection of rotorcraft blade icing using acoustic signals. With a database of computational fluid dynamic and computational aeroacoustic simulations produced from the opensource SU2 software, a BNN is constructed to directly map the acoustic signals to aerodynamic performance metrics, thus bypassing the expensive inverse problems. The prediction uncertainty is quantified by treating BNN weight parameters as random variables, thus revealing a distribution of predictions instead of a singlevalue output. This uncertainty distribution reflects the quality and confidence of the BNN prediction, information that is critical for pilot decisionmaking under potentially dangerous icing flight conditions.

Tue09Jul2019
11:00SC Seminar Room 32349
Steffen Schotthöfer, TU Kaiserslautern
Title:
Sensitivity analysis in the presence of limit cycle oscillations – Regularizing methodsAbstract:
Many unsteady problems equilibrate to periodic behavior. For these problems the sensitivity of periodic outputs to system parameters are often desired and must be estimated from a finite time span. Sensitivities computed in the time domain over a finite time span can take excessive time to converge or fail to do so. In this presentation two approaches will be discussed to overcome these difficulties.
First, the so called windowing approach uses weighting functions to improve convergence behavior. We will consider longtime and shorttime windowing as two aspects of this approach. The idea of a long time window is to average over a big, noninteger number of periods of the weighted output, to archive convergence. On the other hand, the idea of short time windowing is to average over a small, integer multiple of a period. Convergence is archived by refining the period approximation.
Second, an analytic approach is discussed. Here we set up an additional boundary value problem to exactly compute the influence of parameter changes on amplitude, period and relative phase.
We will discuss the efficiency of both approaches and their usability in SU2 and aerodynamic applications. 
Thu25Jul2019
11:30SC Seminar Room 32349
Marc Schwalbach, von Karman Institute for Fluid Dynamics/TU Kaiserslautern
Title:
CADBased Adjoint Multidisciplinary Optimization Framework for Turbomachinery DesignAbstract:
Most adjointbased optimization frameworks for turbomachinery only consider aerodynamic performance and constraints, leading to designs that need to pass through revisions by structural requirements. Only in recent years, adjoint optimization frameworks have been extended to include structural constraints.
In this work, a CADbased parametrization is used for defining the shape freedom, from which the fluid and solid grids are generated. The aerodynamic efficiency is computed using a ReynoldsAveraged NavierStokes solver based on the finite volume method. The maximum von Mises stress and eigenfrequencies are computed using a linear stress and vibration solver based on the finite element method. The CFD, stress, and vibration solvers each have adjoint capabilities, allowing an efficient evaluation of the gradients at a cost independent of the size of the design space. The fluid and structural domains are coupled with the CAD kernel to form a CADbased adjoint multidisciplinary optimization framework (MDO).
In this presentation, the CADbased adjoint MDO framework is introduced. In particular, the differentiation of the FEM solver, using the AD tool CoDiPack, is discussed. This includes the differentiation of both the linear stress solver for the maximum von Mises stress gradients, which results in one additional linear system solve, and the vibration solver for eigenvalue gradients, which results in one additional outer product per eigenvalue. Performance results for gradient calculations are presented, as well as aerodynamic shape optimizations of a radial turbine with both maximum von Mises stress and vibrational resonancereducing constraints.

Fri02Aug2019
11:00SC Seminar Room 32349
Alessandro Gastaldi, Airbus Defence and Space
Title:
Highfidelity airframe shape and sizing optimisation for maximum aircraft performanceAbstract:
The objective of the aircraft development process is to formulate a design which provides the highest possible performance at specific operating conditions, while satisfying many, often competing requirements emerging from the various engineering disciplines involved. Multidisciplinary Design Optimization (MDO) can be used to systematically identify and efficiently resolve complex tradeoffs at every stage of the aircraft design process. In this context, the performance of an aircraft is measured in terms of e.g. range or endurance, for a given mission or at specific flight regimes. To guarantee the feasibility of each solution, it is essential to define a complete criteria model including the designdriving constraints emerging from the various engineering disciplines involved. Allowing simultaneous variation of the internal structural layout and the external aerodynamic shape enables improved solutions which satisfy such structural, aerodynamic and aeroelastic criteria, beyond what is achievable by a sequential approach. Naturally, the analysis models employed in the optimisation must capture the phenomena and interactions which determine these quantities of interest. The ultimate objective of the present work is to combine the above elements in a robust and flexible software framework for aircraft performance optimisation through simultaneous shape and sizing optimisation, using fully coupled highfidelity aerodynamic and structural solvers. This task includes the investigation of best practices for the integration of such a demanding multidisciplinary analysis in an optimisation process, in order to ensure manageable computational cost and robust, automated model evaluations.