六单元圆转方塞式喷管冷流试验与分析

对六单元圆转方塞式喷管的性能以及内喷管间隙与塞锥侧板的影响情况进行了冷流试验,利用纹影显示技术得到了地面、中空和高空三种工作高度下的流场结构。结合塞锥表面压强的计算结果和试验效率曲线,分析了内喷管单元间隙和塞锥侧板对性能的影响,单元之间有间隙和无塞锥侧板均会一定程度地降低喷管性能。结果表明,从地面到高空,六单元圆转方塞式喷管试验模型具有一定的高度补偿能力,其中无单元间隙有塞锥侧板的模型的推力系数效率在0.90~0.95之间变化。但设计高度下的效率低于理论预期值,其性能损失主要是圆转方内喷管型面、塞锥截短、多单元出口激波、底部不封闭等原因造成的。     

厚壁C/ C 复合材料分区CVI 制备工艺研究

以整体毡为增强体,丙烯和糠酮树脂为前驱体,采用分区CVI法和树脂浸渍/固化、炭化相结合的工艺制备了厚壁C/C复合材料,用CT机和偏光显微镜观察了材料的微观结构,并进行了热力学性能测试,结果与热梯度CVI法进行了对比.研究结果表明:分区CVI法制备的热解碳为光滑层结构,整体密度均匀,与热梯度CVI工艺相比,总CVI增密效率提高了近一半,特别是沉积后期,表现出明显的优势.同时,轴向压缩强度提高了28.5％;径向压缩强度提高了33.6％.线胀系数相当.通过研究认为,分区CVI法能很好的提高材料的增密效率和增密均匀性,降低成本,且材料最终具有优异的热力学性能.     

国外卫星测控地面站维护效率探讨

近年来,随着航天事业的迅猛发展,国外卫星测控地面站的数量越来越多,对国外地面站的维护也越来越频繁,本文通过对测控地面站的分析,试图找到一条提高国外地面站维护效率的途径,以降低对人力、物力的消耗。     

S型收缩流道对涡轮通流能力影响的研究

为进一步提高航空发动机涡轮通流能力，以小型跨声速涡轮为原型，研究了静子S型收缩流道对涡轮通流能力的影响。将S型流道直线段长度和收缩段内外圆半径比作为流道造型参数，分别针对原型涡轮静子内外端壁进行调整造型，得到一系列不同参数组合的S型内外端壁涡轮算例。保持膨胀比为设计值不变，利用CFD软件对原型和S型流道涡轮进行设计点模拟分析。结果表明，S型流道涡轮流量提升的原理在于增大的静子喉道面积。在相同造型参数下，S型外端壁涡轮的静子叶根损失被有效降低，流量提升明显，且流量高于S型内端壁涡轮1%左右，但由于最大外径增大使其质量通量提升效果减弱；与之相反，S型内端壁涡轮的质量通量提升明显，且高于S型外端壁涡轮3%左右。从提升涡轮质量通量并保证效率不低于原型的角度看，S型外端壁造型参数选取范围更广。     

Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.