Multi-fidelity surrogate-based methods play an important role in modern engineering design applications, aiming to improve model accuracy while reducing computational cost. One of the widely adopted approaches is the calibration-based method, which calibrates the low-fidelity model through a discrepancy model between low-fidelity and high-fidelity models. Since discrepancies between models exhibit varying characteristics across different problems, using a single surrogate for discrepancy approximation may lack stability. In practical engineering design problems, it is often hard for designers to select optimal surrogate models. To this end, a hybrid-surrogate-calibration-assisted multi-fidelity modeling (HSC-MFM) approach is proposed in this paper. Specifically, this approach integrates three representative surrogate models, including the polynomial response surface, Kriging model, and radial basis function, to comprehensively capture the discrepancy characteristics between different fidelity models. Furthermore, an adaptive weight calculation method is developed to improve the modeling accuracy. Testing results demonstrate that HSC-MFM achieves enhanced stability compared to most existing methods while maintaining good prediction accuracy. Finally, the proposed method is applied to predict the strength of the frame for a blended-wing-body underwater glider, which verifies its engineering applicability.

Rapid surrogate prediction of ducted propeller performance is challenging when only a limited number of independent geometries are available and operating points belonging to the same geometry are strongly correlated. This study proposes a sensitivity-informed physics-regularized backpropagation neural network (SIPR-BP) for simultaneous prediction of the thrust coefficient K_T and the scaled torque coefficient 10K_Q. A CFD database comprising 20 Ka4-70-derived parameterized geometries, each evaluated at five advance ratios, provides 100 observations and 20 complete performance curves. The framework combines three main strategies. First, two-component multi-output partial least-squares (PLS) curve surrogates are reconstructed exclusively from the training geometries of each outer fold to generate leakage-controlled conditional Sobol gate priors. Second, the operating coordinate J is separated from geometric gating and represented by five ordered curve nodes, which guarantee non-increasing K_T and 10K_Q responses over the investigated interval. Third, training-only physics-consistency reliability weighting and a three-member ensemble improve robustness to locally irregular CFD responses and initialization variability. Under a ten-round geometry-grouped holdout protocol, SIPR-BP achieves a geometry-balanced MAPE of 2.65%, RMSE of 0.0112, MAE of 0.00911, and pooled R² of 0.951. When evaluated under the same outer partitions, a two-component PLS baseline yields a MAPE of 4.38%. Across the evaluated PLS, Extra Trees, GPR, and SVR baselines, SIPR-BP reduces geometry-balanced MAPE by approximately 39.6–79.2%. The results indicate that the proposed framework improves unseen-geometry prediction while preserving the prescribed response-curve structure.