设计空间降维的整体优化的功能表面:最近的发展和前进的道路

IF 1.4 Q3 ENGINEERING, MARINE
Matteo Diez, Andrea Serani
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引用次数: 0

摘要

在复杂工业产品的形状优化(如但不限于船体形状、舵及附属物、螺旋桨)中,全局优化(GO)与不确定性量化(UQ)存在内在的相似性:它们分别依赖于对设计空间和操作空间的广泛探索;通常,它们需要局部改进,以确保分别准确识别最优解或概率密度区域(如分布尾);随着问题维数的增加,GO和UQ算法的复杂度,特别是计算成本迅速增加,这两种算法都受到维数诅咒的显著影响。因此,存在着将降维方法从UQ转移到GO的自然基础。这使得在形状优化中有效地探索大型设计空间,从而实现全局优化(可能在多学科和随机设置中)。本文回顾和讨论了基于karhunen - lo展开(相当于适当的正交分解,在离散水平上,主成分分析)的形状优化中设计空间降维的最新技术。最后给出了船体水动力优化的实例并进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design-space dimensionality reduction in global optimization of functional surfaces: recent developments and way forward
In shape optimization of complex industrial products (such as, but not limited to, hull forms, rudder and appendages, propellers), there exists an inherent similarity between global optimization (GO) and uncertainty quantification (UQ): they rely on an extensive exploration of the design and operational spaces, respectively; often, they need local refinements to ensure accurate identification of optimal solutions or probability density regions (such as distribution tails), respectively; they both are dramatically affected by the curse of dimensionality as GO and UQ algorithms' complexity and especially computational cost rapidly increase with the problem dimension. Therefore, there exists a natural ground for transferring dimensionality reduction methods from UQ to GO. These enable the efficient exploration of large design spaces in shape optimization, which, in turn, enable global optimization (possibly in a multidisciplinary and stochastic setting). The paper reviews and discusses recent techniques for design-space dimensionality reduction in shape optimization, based on the Karhunen-Loève expansion (equivalent to proper orthogonal decomposition and, at the discrete level, principal component analysis). An example is shown and discussed for the hydrodynamic optimization of a ship hull.
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来源期刊
Ship Technology Research
Ship Technology Research ENGINEERING, MARINE-
CiteScore
4.90
自引率
4.50%
发文量
10
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