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Scalarization methods are a category of multiobjective optimization (MOO) methods. These methods allow the usage of conventional single objective optimization algorithms, as scalarization methods reformulate the MOO problem into a single objective optimization problem. The scalarization methods analysed within this thesis are the Weighted Sum (WS), the Epsilon-Constraint (EC), and the MinMax (MM) method. After explaining the approach of each method, the WS, EC and MM are applied, a-posteriori, to three different examples: to the Kursawe function; to the ten bar truss, a common benchmark problem in structural optimization; and to the metamodel of an aero engine exit module.
The aim is to evaluate and compare the performance of each scalarization method that is examined within this thesis. The evaluation is conducted using performance metrics, such as the hypervolume and the generational distance, as well as using visual comparison.
The application to the three examples gives insight into the advantages and disadvantages of each method, and provides further understanding of an adequate application of the methods concerning high dimensional optimization problems.
We present an extended finite element formulation for dynamic fracture of piezo-electric materials. The method is developed in the context of linear elastic fracture mechanics. It is applied to mode I and mixed mode-fracture for quasi-steady cracks. An implicit time integration scheme is exploited. The results are compared to results obtained with the boundary element method and show excellent agreement.
This study contributes to the identification of coupled THM constitutive model parameters via back analysis against information-rich experiments. A sampling based back analysis approach is proposed comprising both the model parameter identification and the assessment of the reliability of identified model parameters. The results obtained in the context of buffer elements indicate that sensitive parameter estimates generally obey the normal distribution. According to the sensitivity of the parameters and the probability distribution of the samples we can provide confidence intervals for the estimated parameters and thus allow a qualitative estimation on the identified parameters which are in future work used as inputs for prognosis computations of buffer elements. These elements play e.g. an important role in the design of nuclear waste repositories.