涡轴发动机控制系统综合评价方法

为综合评价涡轴发动机的全权限数字式电子控制系统,在参考GJB 4078的基础上,结合涡轴发动机及控制系统自身特点,基于模糊层次分析法提出了适用的评价指标体系。通过专家打分确定评价指标体系中定性指标和定量指标的权重,建立评语集合,利用模糊层次分析法,解决了定性和定量指标混合评价时模糊量明显化的问题,有利于更加科学地综合出评价结果。同时,分别用某一定性指标和定量指标作为算例,验证了模糊层次分析法的合理性和可行性。     

A novel robust aerodynamic optimization technique coupled with adjoint solvers and polynomial chaos expansion

Uncertainty is common in the life cycle of an aircraft, and Robust Aerodynamic Optimization (RAO) that considers uncertainty is important in aircraft design. To avoid the curse of dimensionality in surrogate-based optimization, this study proposes an adjoint RAO technique called “R-Opt”. Polynomial Chaos Expansion (PCE) is coupled with the R-Opt technique to quantify uncertainty in the responses of the target (including its mean and standard deviation). Only one process of PCE model construction is required in each iteration, and the gradients of uncertainty can be inferred via chain rules. The proposed method is more efficient than prevalent methods, and avoids the problem of a disagreement over the best PCE basis from among a number of PCE models (especially in case of sparse PCE). It also supports the application of sparse PCE. Two benchmark tests and two airfoil cases were used to verify R-Opt, and the optimal solutions were deemed to be robust. It improved the mean aerodynamic performance and reduced the standard deviation of the target.