A simple hierarchical procedure for parameter identification in robust topology optimization

详细信息    查看全文

• 作者：Giuliana S. Venter ; André T. Beck…
• 关键词：Variability ; Topology optimization ; Robust design ; Components of variation ; Design of experiments
• 刊名：Journal of the Brazilian Society of Mechanical Sciences and Engineering
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：38
• 期：2
• 页码：679-689
• 全文大小：1,208 KB
• 参考文献：1.Rozvany G, Zhou M, Birker T (1992) Generalized shape optimization without homogenization. Struct Multidiscip Optim 4(34):250–252CrossRef
  2.Rozvany G (2009) A critical review of established methods of structural topology optimization. Struct Multidiscip Optim 37:217–237MATH MathSciNet CrossRef
  3.Bendsøe MP, Kikichi N (1988) Generating optimal topologies in structural design using a homogenization method. Comput Methods Appl Mech Eng 71:197–224CrossRef
  4.Rao SS (1984) Multiobjective optimization in structural design under uncertain parameters and stochastic processes. AIAA J 22:1670–1678MATH CrossRef
  5.Tai K, Akhtar S (2005) Structural topology optimization using a genetic algorithm with a morphological geometric representation scheme. Struct Multidiscip Optim 30(2):113–137CrossRef
  6.Garcia-Lopez NP, Sanchez-Silva M, Medaglia AL, Chateauneuf A (2013) An improved robust topology optimization approach using multiobjective evolutionary algorithms. Comput Struct 125:1–10CrossRef
  7.Garcia-Lopez NP, Sanchez-Silva M, Medaglia AL, Chateauneuf A (2011) A hybrid topology optimization methodology combining simulated annealing and SIMP. Comput Struct 89:1512–1522CrossRef
  8.Choi SK, Canfield RA, Ramana VG (2007) Reliability-based structural design. Springer, New York (ISBN 978-1-84628-444-1)
  9.Aoues Y, Chateauneuf A (2010) Benchmark study of numerical methods for reliability-based design optimization. Struct Multidiscip Optim 41:277–294MATH MathSciNet CrossRef
  10.Lopez RH, Beck AT (2012) Reliability-based design optimization strategies based on FORM: a review. J Braz Soc Mech Sci 34:506–514CrossRef
  11.Beyer H-G, Sendhoff B (2006) Robust optimization—a comprehensive review. Comput Method Appl M 196:3190–3218MathSciNet
  12.Schuëller GI, Jensen HA (2009) Computational methods in optimization considering uncertainties—an overview. Comput Method Appl M 198:2–13
  13.Kharnanda G, Olhoff N, Mohamed A, Lemaire M (2004) Reliability-based topology optimization. Struct Multidiscip Optim 26:295–307CrossRef
  14.Kim SR, Park JY, Lee WG, Yu JS, Han SY (2007) Reliability-based topology optimization based on evolutionary structural optimization. Proc World Acad Sci Eng Technol 26:168–172
  15.Kim C, Wang S, Rae K, Moon H, Choi K (2006) Reliability-based topology optimization with uncertainties. J Mech Sci Technol 20(4):494–504CrossRef
  16.Assadpoure A, Tootkaboni M, Guest JK (2011) Robust topology optimization of structures with uncertainties in stiffness—application to truss structures. Comput Struct 89:1131–1141CrossRef
  17.Chen S, Chen W, Lee S (2010) Level set based robust shape and topology optimization under random field uncertainties. Struct Multidiscip Optim 41:507–524MATH MathSciNet CrossRef
  18.Tootkaboni M, Asadpoure A, Guest JK (2012) Topology optimization of continuum structures under uncertainty—a polynomial Chaos approach. Comput Method Appl M 201:263–275MathSciNet CrossRef
  19.Dunning PD, Kim HA (2013) Robust topology optimization: minimization of expected and variance of compliance. AIAA J 51(11):2656–2664CrossRef
  20.Sigmund O (2001) A 99 line topology optimization code written in Matlab. Struct Multidiscip Optim 21:120–127CrossRef
  21.Yaman A (2012) Components of variation (COV). In: Technical white paper. http://​www.​biopharmadvice.​com/​web_​documents/​Components%20​of%20​Variation.​pdf . Accessed 16 November 2012
  22.Montgomery DC (2008) Design and analysis of experiments, 7th edn. Wiley, New York
  23.Addelman S (1962) Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics 4(1):21–46MATH MathSciNet CrossRef
  24.Allemang RJ (2003) The modal assurance criterion—twenty years of use and abuse. Sound Vib
  25.Lam HF, Ko JM, Wong CW (1998) Localization of damaged structural connections based on experimental modal and sensitivity analysis. J Sound Vib 210(1):91–115CrossRef
  26.Pandey AK, Biswas M, Samman MM (1991) Damage detection from changes in curvature mode shapes. J Sound Vib 145(2):321–332CrossRef
  27.Bendsøe MP (1995) Optimization of structural topology, shape and material. Springer, BerlinCrossRef
  28.Venter GS, Silva MM (2013) Variability study in topology optimization using statistical tools. In: Proceedings of the 22nd international congress of mechanical engineering (COBEM 2013), Ribeirão Preto, 3–7 Nov 2013, pp 1–10
  29.Associação Brasileira de Normas Técnicas (2003) NBR 6118:2003. Projeto de estruturas de concreto—Procedimento, Rio de Janeiro
  30.Pinto GA, Gonçalves RG (2010) Comparação entre os coeficientes de Poisson do concreto obtidos por ensaio de ultrassom e por ensaio de compressão. In: XVIII Congresso Interno de Iniciação Científica da Unicamp
  
• 作者单位：Giuliana S. Venter (1)
  André T. Beck (1)
  Maíra M. da Silva (1)
  
  1. São Carlos School of Engineering, Av. Trabalhador Sancarlense, 400, São Carlos, São Paulo, 13566-590, Brazil
  
• 刊物主题：Mechanical Engineering;
• 出版者：Springer Berlin Heidelberg
• ISSN：1806-3691

文摘

Identifying the relevant sources of uncertainty is a relevant issue in different problems, including topology optimization, as too many sources of uncertainty investigated at too many levels can make the computational effort prohibitive. Hence, it is fundamental to develop schemes to identify those parameters and parameter interactions which cause the most change in the final problem’s results. In this article, a simple hierarchical procedure is proposed for parameter identification and it is illustrated in a robust topology optimization example. The scheme is composed of a pre-screening of candidate parameters through a component of variation analysis. Only the most relevant parameters, identified in the initial screening, are forwarded to a design of experiments (DOE) analysis. In the DOE, a subset of most relevant parameters and parameters’ interactions are identified, and forwarded to the last stage: the robust optimization. Having identified the most relevant contributions, in the robust optimization one can afford to increase the number of levels for the most relevant parameters, hence also capturing non-linear effects. The example application problem is presented to illustrate the proposed scheme and to investigate the potential gain in terms of computational effort reduction.