A Bi-stage Multi-objective Reliability-based Design Optimization Using Surrogate Model for Reusable Thrust Chambers

Yaqun Qi, Ping Jin, Guobiao Cai and Ruizhi Li

Reliability Engineering and System Safety, 2022, vol. 221, issue C

Abstract: To reduce the computational burden of Multi-Objective Reliability-Based Design Optimization (MORBDO) and promote its application in complex practical systems, a bi-stage MORBDO procedure using surrogate models is proposed in this paper. The novel process consists of two stages: in the first stage, a narrowed solution space named as MORBDO design space is obtained by a pre-multi-objective deterministic design optimization in the whole original design space; In the second stage, the MORBDO design space is extended to form an augmented space by considering uncertainties, and then the MORBDO is executed with the double-loop strategy over the augmented space. Besides, the multiple response surface (MRS) is adopted in the bi-stage MORBDO procedure to replace the time-consuming finite element analysis. The proposed method is validated by conducting MORBDO on a lab-scale reusable LOX/H2 thrust chamber aiming at maximum residual strength and cyclic life. Compared with the traditional MORBDO implemented in the whole original design space, the bi-stage MORBDO can significantly reduce the computational burden of the surrogate model by 49.33% and only one-quarter of the optimization population is required in NSGA II to search for the optimal solution. Finally, a sensitivity analysis is performed to quantify the importance ranking of design variables of thrust chambers. The results indicate that the geometry of cooling channels near the throat plays the most important role in the thrust chamber's residual strength and cyclic life. The proposed method is easy to be conducted and holds great potentials in the MORBDO of other complex practical systems.

Keywords: reusable rocket engine thrust chamber; multi-objective reliability-based design optimization; bi-stage MORBDO procedure; surrogate model (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000412

DOI: 10.1016/j.ress.2022.108362

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