Large-scale level set topology optimization for elasticity and heat conduction
| Autor: | Sandilya Kambampati, Ken Museth, H. Alicia Kim, Carolina Jauregui |
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| Rok vydání: | 2019 |
| Předmět: |
Level set (data structures)
Control and Optimization Computer science Topology optimization 0211 other engineering and technologies Scale (descriptive set theory) 02 engineering and technology Grid Data structure Computer Graphics and Computer-Aided Design Finite element method Computer Science Applications Computational science 020303 mechanical engineering & transports 0203 mechanical engineering Control and Systems Engineering Memory footprint Engineering design process Software 021106 design practice & management |
| Zdroj: | Structural and Multidisciplinary Optimization. 61:19-38 |
| ISSN: | 1615-1488 1615-147X |
| Popis: | We present a numerical study of a new large-scale level set topology optimization (LSTO) method for engineering design. Large-scale LSTO suffers from challenges in both slow convergence and high memory consumption. We address these shortcomings by adopting the spatially adaptive and temporally dynamic Volumetric Dynamic B+ (VDB) tree data structure, open sourced as OpenVDB, which is tailored to minimize the computational cost and memory footprint by not carrying high fidelity data outside the narrow band. This enables an efficient level set topology optimization method and it is demonstrated on common types of heat conduction and structural design problems. A domain decomposition–based finite element method is used to compute the sensitivities. We implemented a typical state-of-the-art LSTO algorithm based on a dense grid data structure and used it as the reference for comparison. Our studies demonstrate the level set operations in the VDB algorithm to be up to an order of magnitude faster. |
| Databáze: | OpenAIRE |
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