Research On Wing Flutter Control And Optimization With Uncertainty

One of the most important steps in airplane design process is aerodynamic analysis and optimization on wing structures. Now most of work which had been researched is based on traditional deterministic optimization methods. But in real engineering projects, uncertainty exists everywhere inevitably. This directly leads to the performance of products are far from design. The aeroelastic design schemes of aeroelastic systems should have robust stability, and all kinds of uncertainties should be taken into consideration from the beginning of optimization design process.Based on systematic review of the traditional optimization design methods for aeroelastic design, research and exploration on the optimization methods about aeroelastic design containing uncertain parameters is performed in this thesis. The research work on optimization methods for aeroelasic problems of wing structures which contain interval uncertainties and random uncertainties is performed separately. Numerical examples show the feasibility and efficiency of those methods. The main work of this thesis is listed as follows:1. Fuzzy controller has good robustness, which is not sensitive to uncertainty of disturbance, without the requirement of the exact mathematic model. Based on such characteristics, a novel controller based on fuzzy control strategy is designed to suppress the LCO vibration of typical wing section with control surface freeplay. Simulation results indicate that this method is valid and the fuzzy controller has good performance.2. Considering various random uncertainties exist in aeroelastic system, a novel reliability-based method, using the response surface method, is developed to perform optimization of wing structures with the object of maximum flutter speed. An example of plane composite material wing structure is presented. The results show the validity and correctness of this method. This method can complete the optimization of wing flutter problem containing uncertain parameters efficiently.3. A novel robust optimization method is developed which is based on neural network surrogate model for flutter optimization problem including random uncertain parameters. The method can solve the wing structure flutter optimization problem with high efficiency and economizes computational cost.4. For engineering problems which lack of statistics information of uncertainty and without explicit formulation of design objects, a novel robust design optimization method using interval analysis is present based on Kriging surrogate model of wing structures. The robust optimization model is built based on the method of non probability robust optimization theory, and is changed to the deterministic form which is suitable for using the traditional optimization methods. This method has the advantages that it has less requirements on uncertainty and mathematical. This method is very convenient for engineering analysist to show their preference whether on objective performance or constains performances.At last, the research work of this dissertation is summarized and the prospective of further research is discussed.