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An integrated methodology applied for reliability based multi-disciplinary design optimization in EPFE with LOX/kerosene

Author

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  • Alimohammadi, H.R.
  • Naseh, H.
  • Ommi, F.

Abstract

Early approaches are based on classical Response Surface Methodology (RSM) using systematic designs of experiments (DoE) and polynomial regression models. In order to improve the approximation quality around the optimum, adaptive methods are very efficient. This work proposes an Adaptive Response Surface Method-Directional Sampling (ARSM-DS) to compute the probability of failure. The method first samples a set of parameters by Latin Hypercube Sampling (LHS). Due to its efficiency, the ARSM is the method of choice for optimization problems. Then for optimal design, a preceding sensitivity analysis as start design for the ARSM has been used. The probability of failure is computed by DS. Directional Sampling yields points on the failure surface of the surrogate model. Hence in the subsequent steps, these points are updated by passing the input values to the solver and computing the respective responses. Finally, Electro-Pump-Fed Engine (EPFE) is studied to illustrate the effectiveness of the proposed method. The computational efficiency and accuracy of the proposed method is elucidated by its comparison with some existing methods. The results show that the proposed method can effectively and accurately evaluate the Reliability-Based Multidisciplinary Optimization (RBMDO) of complex systems and can be used for other engineering applications as well.

Suggested Citation

  • Alimohammadi, H.R. & Naseh, H. & Ommi, F., 2023. "An integrated methodology applied for reliability based multi-disciplinary design optimization in EPFE with LOX/kerosene," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004842
    DOI: 10.1016/j.ress.2023.109570
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    References listed on IDEAS

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    1. Song, Kunling & Zhang, Yugang & Shen, Linjie & Zhao, Qingyan & Song, Bifeng, 2021. "A failure boundary exploration and exploitation framework combining adaptive Kriging model and sample space partitioning strategy for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Jung, Yongsu & Lee, Ikjin, 2021. "Optimal design of experiments for optimization-based model calibration using Fisher information matrix," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Ameryan, Ala & Ghalehnovi, Mansour & Rashki, Mohsen, 2022. "AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    Full references (including those not matched with items on IDEAS)

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