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Iterative reliable design space approach for efficient reliability-based design optimization

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Abstract

Reliability-based design optimization has gained much attention in many engineering design problems with the consideration of uncertainties. Nevertheless, the application is limited by the huge computational cost for the repeated reliability analysis in the optimization process. To address this issue, an iterative reliable design space approach is proposed with less number of reliability analysis required. Specifically, a sequential optimization strategy is proposed to identify the iterative reliable design space and perform the equivalent deterministic optimization in a serial loop. Thus, the identification of the reliable design space, which is time-consuming, is eliminated from the equivalent probabilistic constraints. Furthermore, an improved shifting vector strategy is put forward for the identification. In this strategy, an approximate shifting vector is first constructed utilizing the partial derivatives at the current design point, and it is then modified based on an increment of the shifting vector and a correction provided by the true reliability analysis. Besides, several mathematical examples and engineering problems are tested to validate the performance of the proposed method. In conclusion, the proposed method is able to deal with different kinds of nonlinear problems with relatively high accuracy and efficiency.

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References

  1. Aoues Y, Chateauneuf A (2010) Benchmark study of numerical methods for reliability-based design optimization. Struct Multidiscip Optim 41(2):277–294. https://doi.org/10.1007/s00158-009-0412-2

    Article  MathSciNet  MATH  Google Scholar 

  2. Yang X, Liu Y, Zhang Y, Yue Z (2015) Hybrid reliability analysis with both random and probability-box variables. Acta Mech 226(5):1341–1357. https://doi.org/10.1007/s00707-014-1252-8

    Article  MathSciNet  MATH  Google Scholar 

  3. Li X, Du J, Chen Z, Ming W, Cao Y, He W, Ma J (2018) Reliability-based NC milling parameters optimization using ensemble metamodel. Int J Adv Manuf Technol 97(9–12):3359–3369. https://doi.org/10.1007/s00170-018-2211-7

    Article  Google Scholar 

  4. Zhou Q, Wang Y, Choi S-K, Jiang P, Shao X, Hu J, Shu L (2018) A robust optimization approach based on multi-fidelity metamodel. Struct Multidiscip Optim 57(2):775–797. https://doi.org/10.1007/s00158-017-1783-4

    Article  Google Scholar 

  5. Keshtegar B (2017) Enriched FR conjugate search directions for robust and efficient structural reliability analysis. Eng Comput 34(1):117–128. https://doi.org/10.1007/s00366-017-0524-z

    Article  MathSciNet  Google Scholar 

  6. Keshtegar B (2016) Limited conjugate gradient method for structural reliability analysis. Eng Comput 33(3):621–629. https://doi.org/10.1007/s00366-016-0493-7

    Article  Google Scholar 

  7. Meng Z, Pu Y, Zhou H (2018) Adaptive stability transformation method of chaos control for first order reliability method. Eng Comput 34(4):671–683. https://doi.org/10.1007/s00366-017-0566-2

    Article  Google Scholar 

  8. Nikolaidis E, Burdisso R (1988) Reliability based optimization: a safety index approach. Comput Struct 28(6):781–788. https://doi.org/10.1016/0045-7949(88)90418-X

    Article  MATH  Google Scholar 

  9. Tu J, Choi KK, Park YH (1999) A new study on reliability-based design optimization. J Mech Des 121(4):557–564. https://doi.org/10.1115/1.2829499

    Article  Google Scholar 

  10. Lee J-O, Yang Y-S, Ruy W-S (2002) A comparative study on reliability-index and target-performance-based probabilistic structural design optimization. Comput Struct 80(3):257–269. https://doi.org/10.1016/S0045-7949(02)00006-8

    Article  Google Scholar 

  11. Wu YT, Millwater HR, Cruse TA (1990) Advanced probabilistic structural analysis method for implicit performance functions. AIAA J 28(9):1663–1669. https://doi.org/10.2514/3.25266

    Article  Google Scholar 

  12. Youn BD, Choi KK, Park YH (2003) Hybrid analysis method for reliability-based design optimization. J Mech Des 125(2):221. https://doi.org/10.1115/1.1561042

    Article  Google Scholar 

  13. Yang D, Yi P (2008) Chaos control of performance measure approach for evaluation of probabilistic constraints. Struct Multidiscip Optim 38(1):83. https://doi.org/10.1007/s00158-008-0270-3

    Article  Google Scholar 

  14. Meng Z, Li G, Wang BP, Hao P (2015) A hybrid chaos control approach of the performance measure functions for reliability-based design optimization. Comput Struct 146:32–43. https://doi.org/10.1016/j.compstruc.2014.08.011

    Article  Google Scholar 

  15. Yi P, Zhu Z (2016) Step length adjustment iterative algorithm for inverse reliability analysis. Struct Multidiscip Optim 54(4):999–1009. https://doi.org/10.1007/s00158-016-1464-8

    Article  MathSciNet  Google Scholar 

  16. Keshtegar B, Hao P (2018) Enriched self-adjusted performance measure approach for reliability-based design optimization of complex engineering problems. Appl Math Model 57:37–51. https://doi.org/10.1016/j.apm.2017.12.030

    Article  MathSciNet  Google Scholar 

  17. Keshtegar B, Hao P (2018) A hybrid descent mean value for accurate and efficient performance measure approach of reliability-based design optimization. Comput Methods Appl Mech Eng 336:237–259. https://doi.org/10.1016/j.cma.2018.03.006

    Article  MathSciNet  Google Scholar 

  18. Keshtegar B, Chakraborty S (2018) Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints. Reliab Eng Syst Saf 178:69–83. https://doi.org/10.1016/j.ress.2018.05.015

    Article  Google Scholar 

  19. Forrester AIJ, Keane AJ (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1–3):50–79. https://doi.org/10.1016/j.paerosci.2008.11.001

    Article  Google Scholar 

  20. Zhang D, Han X, Jiang C, Liu J, Li Q (2017) Time-dependent reliability analysis through response surface method. J Mech Des 139(4):041404–041404. https://doi.org/10.1115/1.4035860

    Article  Google Scholar 

  21. Zhou Q, Jiang P, Huang X, Zhang F, Zhou T (2018) A multi-objective robust optimization approach based on Gaussian process model. Struct Multidiscip Optim 57(1):213–233. https://doi.org/10.1007/s00158-017-1746-9

    Article  MathSciNet  Google Scholar 

  22. Jiang C, Qiu H, Yang Z, Chen L, Gao L, Li P (2019) A general failure-pursuing sampling framework for surrogate-based reliability analysis. Reliab Eng Syst Saf 183:47–59. https://doi.org/10.1016/j.ress.2018.11.002

    Article  Google Scholar 

  23. Jiang C, Cai X, Qiu H, Gao L, Li P (2018) A two-stage support vector regression assisted sequential sampling approach for global metamodeling. Struct Multidiscip Optim 58(4):1657–1672. https://doi.org/10.1007/s00158-018-1992-5

    Article  Google Scholar 

  24. Zhou Q, Rong Y, Shao X, Jiang P, Gao Z, Cao L (2018) Optimization of laser brazing onto galvanized steel based on ensemble of metamodels. J Intell Manuf 29(7):1417–1431. https://doi.org/10.1007/s10845-015-1187-5

    Article  Google Scholar 

  25. Tamimi S, Amadei B, Frangopol DM (1989) Monte Carlo simulation of rock slope reliability. Comput Struct 33(6):1495–1505. https://doi.org/10.1016/0045-7949(89)90489-6

    Article  MATH  Google Scholar 

  26. Echard B, Gayton N, Lemaire M, Relun N (2013) A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models. Reliab Eng Syst Saf 111 (Supplement C):232–240. https://doi.org/10.1016/j.ress.2012.10.008

    Article  Google Scholar 

  27. Song S, Lu Z, Qiao H (2009) Subset simulation for structural reliability sensitivity analysis. Reliab Eng Syst Saf 94(2):658–665. https://doi.org/10.1016/j.ress.2008.07.006

    Article  Google Scholar 

  28. Li X, Qiu H, Chen Z, Gao L, Shao X (2016) A local Kriging approximation method using MPP for reliability-based design optimization. Comput Struct 162:102–115. https://doi.org/10.1016/j.compstruc.2015.09.004

    Article  Google Scholar 

  29. Li X, Qiu H, Jiang Z, Gao L, Shao X (2017) A VF-SLP framework using least squares hybrid scaling for RBDO. Struct Multidiscip Optim 55(5):1629–1640. https://doi.org/10.1007/s00158-016-1588-x

    Article  MathSciNet  Google Scholar 

  30. Meng Z, Zhang D, Liu Z, Li G (2018) An adaptive directional boundary sampling technique for efficient reliability-based design optimization. J Mech Des. https://doi.org/10.1115/1.4040883

    Article  Google Scholar 

  31. Yang IT, Hsieh Y-H (2012) Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization. Eng Comput 29(2):151–163. https://doi.org/10.1007/s00366-011-0251-9

    Article  Google Scholar 

  32. Du X, Chen W (2004) Sequential optimization and reliability assessment method for efficient probabilistic design. J Mech Des 126(2):225–233. https://doi.org/10.1115/1.1649968

    Article  Google Scholar 

  33. Cheng G, Xu L, Jiang L (2006) A sequential approximate programming strategy for reliability-based structural optimization. Comput Struct 84(21):1353–1367. https://doi.org/10.1016/j.compstruc.2006.03.006

    Article  Google Scholar 

  34. Meng Z, Zhou H, Hu H, Keshtegar B (2018) Enhanced sequential approximate programming using second order reliability method for accurate and efficient structural reliability-based design optimization. Appl Math Model 62:562–579. https://doi.org/10.1016/j.apm.2018.06.018

    Article  MathSciNet  Google Scholar 

  35. Zou T, Mahadevan S (2006) A direct decoupling approach for efficient reliability-based design optimization. Struct Multidiscip Optim 31(3):190–200. https://doi.org/10.1007/s00158-005-0572-7

    Article  Google Scholar 

  36. Cho TM, Lee BC (2011) Reliability-based design optimization using convex linearization and sequential optimization and reliability assessment method. Struct Saf 33(1):42–50. https://doi.org/10.1016/j.strusafe.2010.05.003

    Article  MathSciNet  Google Scholar 

  37. Yi P, Zhu Z, Gong J (2016) An approximate sequential optimization and reliability assessment method for reliability-based design optimization. Struct Multidiscip Optim 54(6):1367–1378. https://doi.org/10.1007/s00158-016-1478-2

    Article  MathSciNet  Google Scholar 

  38. Chen Z, Li X, Chen G, Gao L, Qiu H, Wang S (2018) A probabilistic feasible region approach for reliability-based design optimization. Struct Multidiscip Optim 57(1):359–372. https://doi.org/10.1007/s00158-017-1759-4

    Article  MathSciNet  Google Scholar 

  39. Chen Z, Wu Z, Li X, Chen G, Gao L, Gan X, Chen G, Wang S (2018) A multiple-design-point approach for reliability-based design optimization. Eng Optim. https://doi.org/10.1080/0305215x.2018.1500561

    Article  Google Scholar 

  40. Chen Z, Qiu H, Gao L, Li P (2013) An optimal shifting vector approach for efficient probabilistic design. Struct Multidiscip Optim 47(6):905–920. https://doi.org/10.1007/s00158-012-0873-6

    Article  Google Scholar 

  41. Huang ZL, Jiang C, Zhou YS, Luo Z, Zhang Z (2015) An incremental shifting vector approach for reliability-based design optimization. Struct Multidiscip Optim 53(3):523–543. https://doi.org/10.1007/s00158-015-1352-7

    Article  MathSciNet  Google Scholar 

  42. Torii AJ, Lopez RH, Miguel F LF (2016) A general RBDO decoupling approach for different reliability analysis methods. Struct Multidiscip Optim 54(2):317–332. https://doi.org/10.1007/s00158-016-1408-3

    Article  MathSciNet  Google Scholar 

  43. Chen X, Hasselman T, Neill D, Chen X, Hasselman T, Neill D (1997) Reliability based structural design optimization for practical applications. https://doi.org/10.2514/6.1997-1403

  44. Liang J, Mourelatos ZP, Tu J (2004) A single-loop method for reliability-based design optimization. (46946):419–430. https://doi.org/10.1115/DETC2004-57255

  45. Jiang C, Qiu H, Gao L, Cai X, Li P (2017) An adaptive hybrid single-loop method for reliability-based design optimization using iterative control strategy. Struct Multidiscip Optim 56(6):1271–1286. https://doi.org/10.1007/s00158-017-1719-z

    Article  MathSciNet  Google Scholar 

  46. Keshtegar B, Hao P (2018) Enhanced single-loop method for efficient reliability-based design optimization with complex constraints. Struct Multidiscip Optim 57(4):1731–1747. https://doi.org/10.1007/s00158-017-1842-x

    Article  MathSciNet  Google Scholar 

  47. Jeong S-B, Park G-J (2016) Single loop single vector approach using the conjugate gradient in reliability based design optimization. Struct Multidiscip Optim 55(4):1329–1344. https://doi.org/10.1007/s00158-016-1580-5

    Article  MathSciNet  Google Scholar 

  48. Meng Z, Yang D, Zhou H, Wang BP (2018) Convergence control of single loop approach for reliability-based design optimization. Struct Multidiscip Optim 57(3):1079–1091. https://doi.org/10.1007/s00158-017-1796-z

    Article  MathSciNet  Google Scholar 

  49. Meng Z, Keshtegar B (2019) Adaptive conjugate single-loop method for efficient reliability-based design and topology optimization. Comput Methods Appl Mech Eng 344:95–119. https://doi.org/10.1016/j.cma.2018.10.009

    Article  MathSciNet  Google Scholar 

  50. Shan S, Wang GG (2008) Reliable design space and complete single-loop reliability-based design optimization. Reliab Eng Syst Saf 93(8):1218–1230. https://doi.org/10.1016/j.ress.2007.07.006

    Article  Google Scholar 

  51. Youn BD, Choi KK, Yang RJ, Gu L (2004) Reliability-based design optimization for crashworthiness of vehicle side impact. Struct Multidiscip Optim 26(3–4):272–283. https://doi.org/10.1007/s00158-003-0345-0

    Article  Google Scholar 

  52. Sun G, Li G, Stone M, Li Q (2010) A two-stage multi-fidelity optimization procedure for honeycomb-type cellular materials. Comput Mater Sci 49(3):500–511. https://doi.org/10.1016/j.commatsci.2010.05.041

    Article  Google Scholar 

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grant Nos. 51675198, 51721092, the National Natural Science Foundation for Distinguished Young Scholars of China under Grant No. 51825502, and the Program for HUST Academic Frontier Youth Team.

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Correspondence to Haobo Qiu.

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Jiang, C., Qiu, H., Li, X. et al. Iterative reliable design space approach for efficient reliability-based design optimization. Engineering with Computers 36, 151–169 (2020). https://doi.org/10.1007/s00366-018-00691-z

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