Sequential ensemble optimization based on general surrogate model prediction variance and its application on engine acceleration schedule design

The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.In this paper,a Sequential Ensemble Optimization(SEO)algorithm based on the ensemble model is proposed.In the proposed algorithm,there is no limitation on the selection of an individual surrogate model.Specifically,the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model.Also,a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator(GUE)is proposed.The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions.The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate.Further,the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.

Aerodynamic optimization design of general parameters for cycloidal propeller in hover based on surrogate model

A surrogate-model-based aerodynamic optimization design method for cycloidal propeller in hover was proposed,in order to improve its aerodynamic efficiency,and analyze the basic criteria for its aerodynamic optimization design.The reliability and applicability of overset mesh method were verified.An optimization method based on Kriging surrogate model was proposed to optimize the geometric parameters for cycloidal propeller in hover with the use of genetic algorithm.The optimization results showed that the thrust coefficient was increased by 3.56%,the torque coefficient reduced by 12.05%,and the figure of merit(FM)increased by 19.93%.The optimization results verified the feasibility of this design idea.Although the optimization was only carried out at a single rotation speed,the aerodynamic efficiency was also significantly improved over a wide range of rotation speeds.The optimal configuration characteristics for micro and small-sized cycloidal propeller were:solidity of 0.2-0.22,maximum pitch angle of 25°-35°,pitch axis locating at 35%-45% of the blade chord length.

Adaptive optimization methodology based on Kriging modeling and a trust region method

Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for complicated optimization problems with a large design space, many design variables, and strong nonlinearity, SBO converges slowly and shows imperfection in local exploitation. This paper proposes a trust region method within the framework of an SBO process based on the Kriging model. In each refinement cycle, new samples are selected by a certain design of experiment method within a variable design space, which is sequentially updated by the trust region method. A multi-dimensional trust-region radius is proposed to improve the adaptability of the developed methodology. Further, the scale factor and the limit factor of the trust region are studied to evaluate their effects on the optimization process. Thereafter, different SBO methods using error-based exploration, prediction-based exploitation, refinement based on the expected improvement function, a hybrid refinement strategy, and the developed trust-regionbased refinement are utilized in four analytical tests. Further, the developed optimization methodology is employed in the drag minimization of an RAE2822 airfoil. Results indicate that it has better robustness and local exploitation capability in comparison with those of other SBO

基于新型高维代理模型的气动外形设计方法

随着现代飞行器性能需求的不断提高,飞行器精细化气动优化设计要求更高可信度的CFD数值分析及更多的独立设计变量,使得基于代理模型的全局优化算法在超过一定的设计变量后显著降低了效率,难以满足复杂工程的设计需求。而目前的高维代理模型过程复杂、时间花费高,缺乏对工程问题的广泛适应性。针对以上难题,提出了利用监督式非线性降维代理建模方法来缓解代理优化过程中的高维变量设计难题。该方法将核主成分分析(非线性)降维与高斯回归过程模型统一训练,自适应构建新型高维代理模型,并随着优化过程不断学习改进模型,建立了从高维输入到输出的准确映射,有效解决了传统高维代理模型训练时间花费高和适应性差等难题。然后基于该新型代理模型发展了适用于飞行器复杂气动设计的高维全局优化设计方法,并将其应用到美国航空航天学会(AIAA)优化小组发布的2个复杂跨声速优化算例中。通过与传统代理优化方法全面比较,验证了所提的方法能大幅提高飞行器高维变量全局优化效率和全局寻优能力。