Realization of a Framework for Simulation-Based Large-Scale Shape Optimization Using Vertex Morphing

There is a significant tendency in the industry for automation of the engineering design process. This requires the capability of analyzing an existing design and proposing or ideally generating an optimal design using numerical optimization. In this context, efficient and robust realization of such...

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Vydáno v:Journal of optimization theory and applications Ročník 189; číslo 1; s. 164 - 189
Hlavní autoři: Ghantasala, Aditya, Najian Asl, Reza, Geiser, Armin, Brodie, Andrew, Papoutsis, Efthymios, Bletzinger, Kai-Uwe
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2021
Springer Nature B.V
Témata:
Applications of Mathematics
Calculus of Variations and Optimal Control; Optimization
Design engineering
Design optimization
Electrons
Engineering
Mathematics
Mathematics and Statistics
Modularity
Morphing
Operations Research/Decision Theory
Optimization
Parameterization
Physics
Robustness (mathematics)
Shape optimization
Theory of Computation
Multi-physics optimization
Additive manufacturing
Vertex morphing
Geometric constraints
Shape optimization
ISSN:0022-3239, 1573-2878
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Popis
Shrnutí:There is a significant tendency in the industry for automation of the engineering design process. This requires the capability of analyzing an existing design and proposing or ideally generating an optimal design using numerical optimization. In this context, efficient and robust realization of such a framework for numerical shape optimization is of prime importance. Another requirement of such a framework is modularity, such that the shape optimization can involve different physics. This requires that different physics solvers should be handled in black-box nature. The current contribution discusses the conceptualization and applications of a general framework for numerical shape optimization using the vertex morphing parametrization technique. We deal with both 2D and 3D shape optimization problems, of which 3D problems usually tend to be expensive and are candidates for special attention in terms of efficient and high-performance computing. The paper demonstrates the different aspects of the framework, together with the challenges in realizing them. Several numerical examples involving different physics and constraints are presented to show the flexibility and extendability of the framework.
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ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-021-01826-x