IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v232y2018i1p52-64.html
   My bibliography  Save this article

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

Author

Listed:
  • Fan Yang
  • Zhufeng Yue
  • Lei Li
  • Dong Guan

Abstract

This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

Suggested Citation

  • Fan Yang & Zhufeng Yue & Lei Li & Dong Guan, 2018. "Hybrid reliability-based multidisciplinary design optimization with random and interval variables," Journal of Risk and Reliability, , vol. 232(1), pages 52-64, February.
    • Handle: RePEc:sae:risrel:v:232:y:2018:i:1:p:52-64
    DOI: 10.1177/1748006X17736639
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X17736639
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X17736639?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
    ---><---

    References listed on IDEAS

    as
    1. Durga Rao Karanki & Hari Shankar Kushwaha & Ajit Kumar Verma & Srividya Ajit, 2009. "Uncertainty Analysis Based on Probability Bounds (P‐Box) Approach in Probabilistic Safety Assessment," Risk Analysis, John Wiley & Sons, vol. 29(5), pages 662-675, May.
    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. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.
    2. Roger Flage & Piero Baraldi & Enrico Zio & Terje Aven, 2013. "Probability and Possibility‐Based Representations of Uncertainty in Fault Tree Analysis," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 121-133, January.
    3. Roger Flage & Terje Aven & Enrico Zio & Piero Baraldi, 2014. "Concerns, Challenges, and Directions of Development for the Issue of Representing Uncertainty in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1196-1207, July.
    4. McKeand, Austin M. & Gorguluarslan, Recep M. & Choi, Seung-Kyum, 2021. "Stochastic analysis and validation under aleatory and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    5. Daniel J. Rozell & Sheldon J. Reaven, 2012. "Water Pollution Risk Associated with Natural Gas Extraction from the Marcellus Shale," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1382-1393, August.
    6. He, Rui & Zhu, Jingyu & Chen, Guoming & Tian, Zhigang, 2022. "A real-time probabilistic risk assessment method for the petrochemical industry based on data monitoring," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Hu, Lunhu & Kang, Rui & Pan, Xing & Zuo, Dujun, 2020. "Risk assessment of uncertain random system—Level-1 and level-2 joint propagation of uncertainty and probability in fault tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    8. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Post-Print hal-01652230, HAL.
    9. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A Critical Discussion and Practical Recommendations on Some Issues Relevant to the Nonprobabilistic Treatment of Uncertainty in Engineering Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1315-1340, July.
    10. Yakov Ben‐Haim, 2012. "Doing Our Best: Optimization and the Management of Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1326-1332, August.

    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:sae:risrel:v:232:y:2018:i:1:p:52-64. 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: SAGE Publications (email available below). General contact details of provider: .

    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.