高超声速舵面热气动弹性不确定性及全局灵敏度分析

鉴于高超声速中气动热预测的不确定性影响热气动弹性分析的可靠性,提出一种温度分布参数化模型,基于此模型,对高超声速舵面热气动弹性中气动热的不确定性及全局灵敏度进行分析,分析方法:求解N-S方程得到物面的温度分布,对此温度分布进行参数化,分别采用蒙特卡罗模拟(Monte Carlo simulation,MCS)方法和稀疏网格数值积分(spare grid numerical integration,SGNI)方法生成不确定性及全局灵敏度分析所需样本,对所有样本都进行热气动弹性分析,热气动弹性分析过程为:由样本得到温度分布,基于此温度分布,考虑热应力和材料属性的影响,对结构进行模态分析,将结构模态插值到气动网格,采用基于CFD的当地流活塞理论进行了气动弹性分析.分别在两种飞行状态下进行分析,计算结果表明:(1)M=5,H=15 km,结构固有频率和颤振分析结果的变异系数约为5.83%;(2)M=6,H=15 km,结构和颤振分析结果的变异系数约为8.84%.两种状态下,两个不确定参数的全局灵敏度都在50%左右,两者耦合作用很小,约为0.与MCS方法相比,SGNI方法显著的提高了不确定性分析效率.

UNCERTAINTY AND GLOBAL SENSITIVITY ANALYSIS OF HYPERSONIC CONTROL SURFACE AEROTHERMOELASTIC

Considering that the uncertainty of hypersonic aerothermodynamics prediction affects the reliability of aerothermoelastic analysis, a parameterized model for temperature distribution is therefore proposed.Based on this model, uncertainty and global sensitivity analysis on aerothermodynamics of hypersonic control surface aerothermoelastic are conducted.In the present analysis method, temperature distribution of the control surface is first obtained by solving NS equation and then parameterized.Using Monte Carlo simulation(MCS) method and spare grid numerical integration(SGNI) method to generate samples for analyzing uncertainly and global sensitivity and then analyzing all the samples, aerothermoelastic analysis is carried out as following:To get temperature distribution by the sample, then to analyze structural modal under the effect of structure thermal stress and material property, interpolate structural mode to the aerodynamic grid, and then to analyze aeroelasticity of the control surface in state space based on CFD local piston theory.Under two fly conditions, the calculation results show that:(1) With M=5 and H=15 km, the variation coefficient of natural frequency and flutter analysis is 5.83%,(2) With M=6 and H=15 km, variation coefficient of natural frequency of the structure and flutter analysis is 8.84%, and the global sensitivity of the two uncertainty parameters is about 50% under the two conditions.And the coupling of two parameters is about 0%, which is very small.Comparing with MCS method, SGNI method can be used to improve the efficiency of uncertainty analysis significantly.