Optimization framework for multi-fidelity surrogate model based on adaptive addition strategy—A case study of self-excited oscillation cavity

This study proposes a multi-fidelity efficient global optimization framework for the structural optimization of self-excited oscillation cavity. To construct a high-precision multi-fidelity surrogate model to correlate the structural parameters of a self-excited oscillation cavity with the gas precipitation and energy consumption characteristics by effectively fuzing the information of different fidelity levels, choosing different correlation functions and hyper-parameter estimation methods, and learning the correlation between the data. The optimization framework determines various sampling methods and quantities by calculating the minimum Euclidean distance between sample points and sensitivity index. To enhance computational efficiency, a multi-fidelity sample library is established by utilizing both precise and coarse computational fluid dynamics grids. The expected improvement criterion-based algorithm for global optimization is employed as an additive strategy to incorporate additional data points into the model. This approach considers both local and global search of the model, thereby enhancing sample accuracy while reducing computation time. Moreover, the utilization of the highly generalized Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for identifying the Pareto optimal solution set enhances convergence speed. The proposed optimization framework in this study achieves a remarkable level of model accuracy and provides optimal solutions even with a limited sample size. It can be widely used in engineering optimization problems.