IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v56y2013i1p231-251.html
   My bibliography  Save this article

A simplex-based numerical framework for simple and efficient robust design optimization

Author

Listed:
  • Pietro Congedo
  • Jeroen Witteveen
  • Gianluca Iaccarino

Abstract

The Simplex Stochastic Collocation (SSC) method is an efficient algorithm for uncertainty quantification (UQ) in computational problems with random inputs. In this work, we show how its formulation based on simplex tessellation, high degree polynomial interpolation and adaptive refinements can be employed in problems involving optimization under uncertainty. The optimization approach used is the Nelder-Mead algorithm (NM), also known as Downhill Simplex Method. The resulting SSC/NM method, called Simplex 2 , is based on (i) a coupled stopping criterion and (ii) the use of an high-degree polynomial interpolation in the optimization space for accelerating some NM operators. Numerical results show that this method is very efficient for mono-objective optimization and minimizes the global number of deterministic evaluations to determine a robust design. This method is applied to some analytical test cases and a realistic problem of robust optimization of a multi-component airfoil. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Pietro Congedo & Jeroen Witteveen & Gianluca Iaccarino, 2013. "A simplex-based numerical framework for simple and efficient robust design optimization," Computational Optimization and Applications, Springer, vol. 56(1), pages 231-251, September.
    • Handle: RePEc:spr:coopap:v:56:y:2013:i:1:p:231-251
    DOI: 10.1007/s10589-013-9569-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-013-9569-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-013-9569-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eldred, M.S. & Swiler, L.P. & Tang, G., 2011. "Mixed aleatory-epistemic uncertainty quantification with stochastic expansions and optimization-based interval estimation," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1092-1113.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Helton, Jon C. & Brooks, Dusty M. & Sallaberry, Cédric J., 2020. "Property values associated with the failure of individual links in a system with multiple weak and strong links," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    2. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Lv, Y. & Yan, X.D. & Sun, W. & Gao, Z.Y., 2015. "A risk-based method for planning of bus–subway corridor evacuation under hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 188-199.
    4. Shah, Harsheel & Hosder, Serhat & Winter, Tyler, 2015. "Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 59-72.
    5. Rahman, S. & Karanki, D.R. & Epiney, A. & Wicaksono, D. & Zerkak, O. & Dang, V.N., 2018. "Deterministic sampling for propagating epistemic and aleatory uncertainty in dynamic event tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 62-78.
    6. Helton, Jon C. & Brooks, Dusty M. & Sallaberry, Cédric J., 2020. "Margins associated with loss of assured safety for systems with multiple weak links and strong links," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. Palash Dutta, 2019. "Structural Reliability Analysis with Inverse Credibility Distributions," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 47-63, March.
    8. Wang, Chong & Matthies, Hermann G., 2019. "Novel model calibration method via non-probabilistic interval characterization and Bayesian theory," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 84-92.
    9. Rockafellar, R.T. & Royset, J.O. & Miranda, S.I., 2014. "Superquantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk," European Journal of Operational Research, Elsevier, vol. 234(1), pages 140-154.
    10. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    11. Yoo, Yeongmin & Jung, Ui-Jin & Han, Yong Ha & Lee, Jongsoo, 2021. "Data Augmentation-Based Prediction of System Level Performance under Model and Parameter Uncertainties: Role of Designable Generative Adversarial Networks (DGAN)," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    12. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    13. Liu, H.B. & Jiang, C. & Jia, X.Y. & Long, X.Y. & Zhang, Z. & Guan, F.J., 2018. "A new uncertainty propagation method for problems with parameterized probability-boxes," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 64-73.
    14. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2021. "A Kriging-assisted sampling method for reliability analysis of structures with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    15. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    18. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2020. "A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models," Applied Energy, Elsevier, vol. 275(C).
    19. Riley, Matthew E., 2015. "Evidence-based quantification of uncertainties induced via simulation-based modeling," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 79-86.
    20. Michael J. Pennock & William B. Rouse, 2016. "The Epistemology of Enterprises," Systems Engineering, John Wiley & Sons, vol. 19(1), pages 24-43, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:56:y:2013:i:1:p:231-251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.