Hypervolume scalarization for shape optimization to improve reliability and cost of ceramic components

Schultes, Johanna; Stiglmayr, Michael; Klamroth, Kathrin; Hahn, Camilla

doi:10.1007/s11081-020-09586-9

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Finally, in Schultes et al (2021) tackle a bi-objective shape optimization problem. The goal is to maximize the mechanical stability of a ceramic component under tensile load while minimizing its volume.…”

Section: Overview Of the Special Issue Articlesmentioning

confidence: 99%

Multiobjective optimization and decision making in engineering sciences

Hakanen

Allmendinger

2021

Optim Eng

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Real-world decision making problems in various fields including engineering sciences are becoming ever more challenging to address. The consideration of various competing criteria related to, for example, business, technical, workforce, safety and environmental aspects increases the complexity of decision making and leads to problems that feature multiple competing criteria. A key challenge in such problems is the identification of the most preferred trade-off solution(s) with respect to the competing criteria. Therefore, the effective combination of data, skills, and advanced engineering and management technologies is becoming a key asset to a company urging the need to rethink how to tackle modern decision making problems. This special issue focuses on the intersection between engineering, multiple criteria decision making, multiobjective optimization, and data science. The development of new models and algorithmic methods to solve such problems is in the focus as much as the application of these concepts to real problems. This special issue was motivated by the 25th International Conference on Multiple Criteria Decision Making (MCDM2019) held in Istanbul, Turkey, in 2019.

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Section: Overview Of the Special Issue Articlesmentioning

confidence: 99%

Multiobjective optimization and decision making in engineering sciences

Hakanen

Allmendinger

2021

Optim Eng

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“…The adjoint method for computing the shape derivatives for such objective functionals has been applied in [12,29,30]. In [21,49], numerical studies of multi-criteria shape optimization are presented and Pareto fronts are explored. While the objective functionals in these two works fit into our general framework, they do not (yet) include aerodynamic losses (see however [37]).…”

Section: Introductionmentioning

confidence: 99%

An Analytical Study in Multi-physics and Multi-criteria Shape Optimization

Gottschalk

Reese

2021

J Optim Theory Appl

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A simple multi-physical system for the potential flow of a fluid through a shroud, in which a mechanical component, like a turbine vane, is placed, is modeled mathematically. We then consider a multi-criteria shape optimization problem, where the shape of the component is allowed to vary under a certain set of second-order Hölder continuous differentiable transformations of a baseline shape with boundary of the same continuity class. As objective functions, we consider a simple loss model for the fluid dynamical efficiency and the probability of failure of the component due to repeated application of loads that stem from the fluid’s static pressure. For this multi-physical system, it is shown that, under certain conditions, the Pareto front is maximal in the sense that the Pareto front of the feasible set coincides with the Pareto front of its closure. We also show that the set of all optimal forms with respect to scalarization techniques deforms continuously (in the Hausdorff metric) with respect to preference parameters.

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