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Item type:Publication, Access status: with files , Adjoint shape optimization of blood flow applications under uncertainties(2025); This thesis aims to advance traditional gradient-based shape optimization techniques by incorporating blood-specific features. The work can be broadly classified into four main categories, (a) computational blood flow modeling, (b) parameter-free shape optimization, (c) the continuous adjoint method and (d) robust shape optimization. As regards (a), this work presents key aspects related to the computational fluid dynamics (CFD) or fluid-structure interaction (FSI) simulations employed for the solution of the blood flow problem (primal problem). The underlying primal problem is enhanced to account for blood damage (hemolysis) by the numerical solution of an additional partial differential equation (PDE). The minimization of hemolysis is one of the main objectives of interest in this work. In order to account for the shear-thinning behavior of blood, several non-Newtonian viscosity models are also employed. The numerical solution of the primal problem is verified against analytical solutions and literature-reported results when deemed necessary. The shape optimization problem is formulated based on a parameter-free approach. To this end, the identification of the shape gradient or at least, a suitable descent direction, based on the shape sensitivity is addressed. Several methods that compute the descent direction based on the solution of one (or two) additional PDE(s) are presented. Auxiliary aspects related to the optimization problem are also discussed in the context of (b). The thesis presents the novel derivation of continuous adjoint equation systems dual to the blood-specific problems. Specifically, contributions to (c) relate to the development of the continuous adjoint to the steady-state Navier-Stokes equations for an incompressible fluid with hemolysis modeling and with non-Newtonian viscosity properties. The accuracy of the novel systems in estimating the shape sensitivity is assessed against second-order accurate finite difference (FD) studies. On the topic of (d), this work focuses on the necessary uncertainty quantification techniques able to drive a robust shape optimization procedure. The problem is considered uncertain by means of the employed parameters or the boundary conditions thus rendering the objective functional a statistical quantity. The optimization targets the minimization of the statistical moments of the objective functional through the use of methods based on sampling or the method of moments. The biomedical applications studied in this thesis refer to an idealized medical device and an idealized bypass-graft.Publicationtype: Doctoral Thesis121 144 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , CAD-free adjoint shape optimization of floating vessels(2024); This thesis aims to improve fluid-dynamic shape optimization methods for non-parameterized (CAD-free) shapes. The focus is computing shape transformations for shapes with Lipschitz boundary and approximating the direction of the steepest descent in Banach spaces with the help of shape sensitivity information, which is calculated using an adjoint-based method. Further, considering geometric constraints and their algorithmic treatment is a central aspect of the thesis. The work investigates a p-Laplace relaxation to approximate Lipschitz transformations with the help of W-1-p functions. Further attempts are made to improve the approximation of the steepest descent direction in W-1-∞ using an approach based on the ADMM algorithm. The suggested approaches are applied in multiple numerical experiments, which successively deform a domain by point-wise movement of the computational grids.Publicationtype: Doctoral Thesis135 242 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Adjoint-based shape optimization constraint by turbulent two-phase Navier-Stokes systems(2021-10); The thesis aims to advance gradient-based optimization methods for non-parametrized shapes exposed to immiscible two-phase flows using an adjoint Reynolds-Averaged Navier-Stokes (RANS) approach. Attention is given to the trade-off between adjoint consistency and industrial process capability. The efforts can be structured into four building blocks (I-IV): Compatible fully turbulent (I) primal (physical) and (II) dual (mathematical, adjoint) relationships together with appropriate objective functional formulations (III) are analysed with curiosity and scepticism at the same time, in order to attain the required accuracy, robustness and efficiency. The geometry engine (IV), which translates the computed sensitivities into shape deformations and grid adjustments, is crucial for the efficiency of the process, the technical usability of the result, and the HPC capability. Different aspects of the simulation-driven shape optimization process are addressed. This research’s common ground is to analyse potential issues in greater depth rather than to formulate ad-hoc measures. The guiding principle frequently follows the concept of ”Learning from the Adjoints”. It is seen that potential weaknesses displayed by an adjoint approach –e.g. when developing (continuous) analytical solutions, in conjunction with (discrete) convergence problems– are often attributable to weaknesses of the primal formulation and a twist of the research question can lead to fruitful insights. Contributions to (I) refer to the development of an efficient engineering Cahn-Hilliard (CH) Volume-of-Fluid (VoF) branch. In line with analytical considerations for a model problem, a nonlinear Equation of State (EoS) is derived to relate an indicator function with the fluid properties. Building block (II) covers the derivation of discrete adjoint VoF formulations and the implementation of an adjoint VoF sub-cycling strategy. The suggestion of a discretely differentiable EoS, together with a novel combination of an inconsistent adjoint VoF method and the CH-VoF approach, allows for a robust and (flexible-) consistent adjoint two-phase formulation. Another major part is concerned with a continuous adjoint complement to 2D, incompressible, first-order boundary-layer equations. The findings support the heuristic neglect of the adjoint transposed convection term and offer analytical expressions for adjoint laminar boundary-layer parameters. The thesis is also concerned with improving adjoint investigations of turbulent flows using mixing-length arguments for the frozen turbulence strategy and a Law of the Wall consistent approach. An algebraic expression provides a consistent closure of the adjoint momentum equation in the logarithmic layer. Spatial decoupling of the control from the objective (III) affects the formulation of boundary conditions and reduces iterative efforts when the design surface does not cover the entire wetted surface. Additionally, an implicit surface metric approach is presented to extract the inherently smooth gradient (IV) out of the possible rough sensitivity derivative. Attention is devoted to compliance with geometrical constraints, e.g. constant volume or maximum outer dimensions. Finally, an adaptive floatation module is added to the gradient-based optimization procedure, which is not differentiated and considered frozen during the adjoint simulation. Examples included underline the capability of the frozen floatation approach and provide partially drastically improved ship hull shapes. It is demonstrated that fixed floatation can lead to optimization losses when the final shape is released. Almost without exception, applications relate to maritime two-phase flows at the industrial level. Some of them are conducted even in full-scale. They refer to a Kriso container ship, a generic submarine, a double-ended ferry, and an offshore-supply vessel.Publicationtype: Doctoral Thesis565 2432 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Fluid dynamic optimization of HVAC-components with adjoint methods(2018); This thesis is concerned with the theory and application of continuous adjoint shape and topology optimization for heating, ventilation and air-conditioning components of aircrafts. The focal point is the applicability of adjoint theory to industrial practice, referring to step size computation for the gradient descent, design constraints and a correct computation of the gradient from the sensitivity. The implemented approaches are validated and verified via simulation of proper fluid dynamic test-cases. The arising framework is applied to industrial cases of aircraft components.Publicationtype: Doctoral Thesis520 1044 - Some of the metrics are blocked by yourconsent settings
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