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The aero-structural design of bridges is mainly controlled by the deck cross-section design. Design modifications on bridge decks impact the deck aerodynamics and the deck mechanical contribution, which also affect the bridge aeroelastic responses. The nonlinear inherent nature of bluff body aerodynamics combined with the nonlinearities of multimodal aeroelastic analyses result in complex relationships between the full bridge aeroelastic responses and deck shape design variables. This fact impacts the design process as it may lead to the development of complex feasible design regions in the chosen design domain, including disjoint feasible regions that may cause local minima. Given the limitations of metaheuristic optimization methods to deal with optimization problems with large sets of design variables, as required in holistic bridge design problems, gradient-based optimization algorithms can be recast to address global optimization problems. In this study, we propose the use of tunneling optimization methods to address this challenge.
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Finding Better Local Optima in Topology Optimization via Tunneling
Topology optimization problems are typically non-convex, and as such, multiple local minima exist. Depending on the initial design, the type of optimization algorithm and the optimization parameters, gradient-based optimizers converge to one of those minima. Unfortunately, these minima can be highly suboptimal, particularly when the structural response is very non-linear or when multiple constraints are present. This issue is more pronounced in the topology optimization of geometric primitives, because the design representation is more compact and restricted than in free-form topology optimization. In this paper, we investigate the use of tunneling in topology optimization to move from a poor local minimum to a better one. The tunneling method used in this work is a gradient-based deterministic method that finds a better minimum than the previous one in a sequential manner. We demonstrate this approach via numerical examples and show that the coupling of the tunneling method with topology optimization leads to better designs.
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- Award ID(s):
- 1751211
- PAR ID:
- 10197571
- Date Published:
- Journal Name:
- ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Volume:
- 2B
- Issue:
- DETC2018-86116
- Page Range / eLocation ID:
- V02BT03A014
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2018-86116
