Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-level accuracy. The framework couples a low-fidelity-informed Gaussian process regression transfer model with uncertainty-triggered sampling and a synchronized elitism rule embedded in a hybrid genetic algorithm. Low-fidelity XFOIL evaluations provide inexpensive features, while sparse RANS simulations are adaptively allocated when predictive uncertainty exceeds a threshold; elite candidates are mandatorily validated at high fidelity, and the population is re-evaluated to prevent evolutionary selection based on outdated fitness values produced by earlier surrogate states. The method is demonstrated for a two-point problem at $Re=6\times10^6$ with cruise at $α=2^\circ$ (maximize $E=L/D$) and take-off at $α=10^\circ$ (maximize $C_L$) using a 12-parameter CST representation. Independent multi-fidelity surrogates per flight condition enable decoupled refinement. The optimized design improves cruise efficiency by 41.05% and take-off lift by 20.75% relative to the best first-generation individual. Over the full campaign, only 14.78% (cruise) and 9.5% (take-off) of evaluated individuals require RANS, indicating a substantial reduction in high-fidelity usage while maintaining consistent multi-point performance.

翻译：主动多保真代理建模方法被开发用于多工况翼型形状优化，以在保持RANS级精度的同时降低高保真CFD成本。该框架将低保真信息高斯过程回归传递模型与不确定性触发采样以及嵌入混合遗传算法的同步精英规则相结合。低保真XFOIL评估提供了廉价特征，而稀疏RANS模拟在预测不确定性超过阈值时自适应地分配；精英候选个体被强制在高保真度下验证，并通过重新评估种群来避免基于早期代理状态产生的过时适应度值进行进化选择。该方法在$Re=6\times10^6$条件下的两点问题中得到验证，其中巡航状态（$α=2^\circ$，最大化$E=L/D$）和起飞状态（$α=10^\circ$，最大化$C_L$）使用12参数CST表征。每个飞行条件的独立多保真代理实现了解耦细化。优化后的设计相对于最佳的第一代个体，巡航效率提高了41.05%，起飞升力提高了20.75%。在整个优化过程中，仅14.78%（巡航）和9.5%（起飞）的评估个体需要RANS计算，表明在保持一致多点性能的同时，大幅减少了高保真度使用。