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- Title
Multi-Objective Bayesian Optimization Design of Elliptical Double Serpentine Nozzle.
- Authors
Zhang, Saile; Yang, Qingzhen; Wang, Rui; Wang, Xufei
- Abstract
The use of traditional optimization methods in engineering design problems, specifically in aerodynamic and infrared stealth optimization for engine nozzles, requires a large number of objective function evaluations, therefore introducing a considerable challenge in terms of time constraints. In this paper, this limitation is addressed by using a sample-efficient multi-objective Bayesian optimization that takes Kriging as a surrogate model and Expected Hypervolume Improvement as the infill criterion. Using this approach, the probabilistic model is continuously established and updated, and the approximate Pareto front is obtained at a relatively small computational budget. The objective of this work is to evaluate the applicability of employing a multi-objective Bayesian optimization framework for the aerodynamic-infrared shape optimization of an elliptical double serpentine nozzle at 6 km flight condition, where the objective functions are evaluated by means of high-fidelity computational fluid dynamics and reversed Monte Carlo ray tracing simulations. We achieve good results in both infrared radiation signature reduction and aerodynamic performance improvement with a reasonable number of evaluations, indicating that the proposed method is effective and efficient for tackling the computationally intensive optimization challenges in the aircraft design.
- Subjects
SERPENTINE; COMPUTATIONAL fluid dynamics; INFRARED radiation; STRUCTURAL optimization; RAY tracing
- Publication
Aerospace (MDPI Publishing), 2024, Vol 11, Issue 1, p48
- ISSN
2226-4310
- Publication type
Article
- DOI
10.3390/aerospace11010048