Abstract: As there are many parameters in the design of dual throat fluidic thrust vectoring nozzle, a method of multivariate optimization ability is investigated, which includes nozzle parametric design, using uniform experimental design (UED) to spread samples in the design range and obtaining corresponding performance, training radial basis function (RBF) neural networks with samples, and then using particle swarm optimization (PSO) to find a better nozzle parameter combination based on trained neural networks. Computational results show that the thrust-vectoring angle of the optimized nozzle is improved obviously. Tests indicate that the method is of good optimization ability, and can be used on multivariate optimization in nozzle geometry design. 

Key words: Dual throat fluidic thrust vectoring nozzle; Vector optimization; Uniform experimental design; Radial basis function; Particle swarm optimization

摘要： 本文探索了一种能多变量综合优化的方法,即对喷管进行参数化设计后,用均匀试验设计(UED)将试验样本均匀散布在设计区间内,求出各性能参数后,利用径向基神经网络(RBF)对试验样本进行拟合,再用粒子群算法(PSO)对训练好的神经网络进行寻优,找出了更好的双喉道气动矢量喷管设计参数组合。数值模拟结果显示,优化后的双喉道气动矢量喷管的矢量角有了明显提高。试验表明这种优化方法具有很好的优化能力,可以用来对喷管几何外形进行参数优化。 

关键词: 双喉道气动矢量喷管;矢量优化;均匀试验设计;径向基神经网络;粒子群算法 