Risk-based design optimization under hybrid uncertainties
| Autor: | Congbo Li, Mi Xiao, Wei Li, Liang Gao |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Robust design optimization CVAR Computer science Monte Carlo method 0211 other engineering and technologies General Engineering 02 engineering and technology Computer Science Applications Engineering optimization Expected shortfall 020303 mechanical engineering & transports 0203 mechanical engineering Modeling and Simulation Compatibility (mechanics) Constraint functions Risk based design Software 021106 design practice & management |
| Zdroj: | Engineering with Computers. 38:2037-2049 |
| ISSN: | 1435-5663 0177-0667 |
| DOI: | 10.1007/s00366-020-01196-4 |
| Popis: | The rapidly changing requirements of engineering optimization problems require unprecedented levels of compatibility to integrate diverse uncertainty information to search optimum among design region. The sophisticated optimization methods tackling uncertainty involve reliability-based design optimization and robust design optimization. In this paper, a novel alternative approach called risk-based design optimization (RiDO) has been proposed to counterpoise design results and costs under hybrid uncertainties. In this approach, the conditional value at risk (CVaR) is adopted for quantification of the hybrid uncertainties. Then, a CVaR estimation method based on Monte Carlo simulation (MCS) scenario generation approach is derived to measure the risk levels of the objective and constraint functions. The RiDO under hybrid uncertainties is established and leveraged to determine the optimal scheme which satisfies the risk requirement. Three examples with different calculation complexity are provided to verify the developed approach. |
| Databáze: | OpenAIRE |
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