cae update of cae models on actual manufactured shapes

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Adaptive parameterization for Aerodynamic Shape Optimization in Aeronautical Applications

Cílem mé disertační práce je analyzovat a vyvinout parametrizační metodu pro 2D a 3D tvarové optimalizace v kontextu průmyslového aerodynamického návrhu letounu založeném na CFD simulacích. Aerodynamická tvarová optimalizace je efektivní nástroj, který si klade za cíl snížení nákladů na návrh letounů. Nástroj založený na automatickém hledání optimálního tvaru. Klíčovou částí úspěšného optimalizačního procesu je použití vhodné parametrizační metody, metody schopné garantovat možnost dosažení optimálního tvaru. Parametrizační metody obecně používané v oblasti aerodynamické tvarové optimalizace momentálně nejsou připravený na komplikované průmyslové aplikace vyskytující se u moderních dopravních letounů, které mají šípová zalomená křídla s winglety a motorovými gondolami, přechodové prvky spojující např. trup s křídlem atd.. Existuje tedy potřeba nalezení obecné parametrizační metody, která bude aplikovatelná na širokou škálu různých geometrických tvarů. Free-Form Deformation (FFD[1]) parametrizace může, vzhledem ke svým schopnostem při zacházení s geometrií, být odpovědí na tuto potřebu. Adaptivní parametrizace by se měla být schopna automaticky přizpůsobit danému tvaru tak, aby byly její kontrolní body vhodně rozmístěny. Což umožní dostatečnou kontrolu deformací objektu, která zaručí možnost vytvoření optimálního tvaru objektu a splnění geometrických omezení. Primární aplikací takové parametrizační metody je deformace tvaru objektu. Dalším navrhovaným cílem je modifikace FFD parametrizační metody pro současné deformace tvaru objektu a CFD výpočetní sítě, umožnující velké deformace objektu při zachování kvality výpočetní sítě.The goal of this doctoral thesis is to analyze and develop parameterization algorithms for 2D and 3D shape optimization in the context of industrial aircraft aerodynamic design based on simulations with CFD. Aerodynamic shape optimization is an efficient tool that aims at reducing the cost of the process of aircraft design. A tool that is based on automatization of the search for the optimum shape. Key part of successful aerodynamic shape optimization is the use of appropriate parameterization method, a method that should guarantee the possibility of reaching optimum shape. The parameterization methods used in aerodynamic shape optimizations are still not ready for complex industrial applications, which are present on modern passenger aircrafts with swept cranked wings with winglets and engine pylons, fuselage-wing interactions etc. So there is a need for general parameterization method that applies on wide variety of different geometries.The Free-Form Deformation (FFD[1]) parameterization can, thanks to its geometry handling qualities, be the answer to this need. Adaptive parameterization should automatically modify parameterization grid (lattice) to get appropriate lattice in regions of interest. Such that will allow sufficient control of deformations of the object with respect to reaching optimum shape and fulfilling optimization constraints. First application is in the surface deformation. The other proposed goal is development of the FFD parameterization that can do both surface deformations and CFD mesh deformations, while enabling large object deformations and preserving the level of mesh quality during the process.

Fluid structure interaction analysis: vortex shedding induced vibrations

Abstract Fluid Structure Interaction (FSI) numerical modelling requires an efficient workflow to properly capture the physics involved. Computational Structural Mechanics (CSM) and Computation Fluid Dynamics (CFD) have to be coupled and at the moment there is a lack of monolithic solvers capable to tackle industrial applications that involves high fidelity models which mesh can be comprised of hundred millions of cells. This paper shows an efficient approach based on standard commercial tools. The FEM solver ANSYS® Mechanical™ is used to extract a given number of eigenmodes. Then the modal shapes are imported in the CFD solver Fluent® using the Add On RBF Morph™. Updating the modal coordinates it is possible to adapt the shape of the model by taking into account the elasticity of the CFD model. Transient analysis is faced using a time marching solution by updating the shape of the mesh at each time step (weak coupling, evaluated as single DOF systems and integrating modal forces over the CFD grid). Numerical performances and solution accuracy of this approach are analyzed on a practical application (NACA0009 Hydrofoil) for which experimental data are available. A comparison between proposed method and experiment is provided. Transient coupled solver is used for the computation of eigenvalues in water by post processing the free vibration response in calm fluid

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods

In this chapter we introduce a combined parameter and model reduction methodology and present its application to the efficient numerical estimation of a pressure drop in a set of deformed carotids. The aim is to simulate a wide range of possible occlusions after the bifurcation of the carotid. A parametric description of the admissible deformations, based on radial basis functions interpolation, is introduced. Since the parameter space may be very large, the first step in the combined reduction technique is to look for active subspaces in order to reduce the parameter space dimension. Then, we rely on model order reduction methods over the lower dimensional parameter subspace, based on a POD-Galerkin approach, to further reduce the required computational effort and enhance computational efficiency

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability

When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. While classical regression techniques prove effective for parameterized geometries, practical scenarios often involve the absence of shape parametrization during the inference stage, leaving us with only mesh discretizations as available data. Learning simulations from such mesh-based representations poses significant challenges, with recent advances relying heavily on deep graph neural networks to overcome the limitations of conventional machine learning approaches. Despite their promising results, graph neural networks exhibit certain drawbacks, including their dependency on extensive datasets and limitations in providing built-in predictive uncertainties or handling large meshes. In this work, we propose a machine learning method that do not rely on graph neural networks. Complex geometrical shapes and variations with fixed topology are dealt with using well-known mesh morphing onto a common support, combined with classical dimensionality reduction techniques and Gaussian processes. The proposed methodology can easily deal with large meshes without the need for explicit shape parameterization and provides crucial predictive uncertainties, which are essential for informed decision-making. In the considered numerical experiments, the proposed method is competitive with respect to existing graph neural networks, regarding training efficiency and accuracy of the predictions