亚格子模型对泵喷推进器宽带非定常力预测的影响研究

为了研究亚格子模型对泵喷推进器非定常流动与宽带非定常力预报结果的影响,采用分块结构化网格建立了模型尺度下艇后泵喷推进器的计算模型,并进行了大涡模拟数值计算。从艇尾非定常流场特征量和推进器转子脉动载荷两个方面对比了三种不同亚格子模型计算结果的差异,并分析了泵喷内部流动与转子非定常力间的内在联系。研究结果表明：三种亚格子模型均能得到含有叶频宽带峰的轴向推力谱,且整体趋势相近。泵喷转子上游的湍流强度和尺度的分布对亚格子模型较敏感,其中Smargrinsky-Lilly模型得到的湍流强度较强,尺度较大,该模型下的湍流谱在非平衡区量级较大,但由低频向高频的衰减较快,并且预测到的分离区范围大,导叶尾缘脱落涡分散,导致转子上游来流空间分布不均程度较强。对于转子叶片上的载荷脉动,Smargrinsky-Lilly模型预测的推力谱中线谱成分明显,并且叶频处宽带谱峰“陡峭”,而WALE模型和KET模型的结果宽带特性较强,对比非定常推力测试结果可知,WALE模型和KET模型更适于该问题宽带非定常力预测。     

集主承力结构与大容量储箱支架于一体的卫星主承力筒结构研究

针对现代卫星结构的大型化、燃料储箱的大型化,提出了一种集主承力结构与大容量储箱支架一体化的卫星主承力筒结构形式,旨在降低卫星质心、提高基频;在满足储箱容积的前提下,又能满足主结构有效载荷往运载的力的传递作用．有限元数值计算表明,该结构形式能够满足卫星相关结构性能的要求,可以作为今后卫星结构设计的参考构型．     

基于慢特征密度聚类的气路异常检测方法

在结合核技巧、慢特征分析算法与密度聚类方法的基础上,提出了基于混合核慢特征分析和密度聚类的慢特征密度聚类算法,实现了基于民航发动机气路参数原始值的异常检测。核技巧的引入克服了慢特征分析法处理复杂数据时可能存在的维度爆炸问题,充分利用不同核函数的特点和慢特征分析的优势从气路参数原始值中提取出随时间变化最缓慢的特征作为密度聚类算法的输入,最终筛选出异常值。经实验对比发现,该方法针对某些异常拥有最好的聚类效果和最低的虚警率,尤其是检测可调放气活门系统异常时虚警数量不到样本总数量的0.5%,是一种有效的方法。      

平流层飞艇艇身外形研究

为了完成特定的任务,平流层飞艇需要克服风场保持长期定点,因此要求其阻力最小。飞艇总阻力中艇身阻力占60-70%,因此对飞艇而言,针对艇身外形进行研究得到阻力小而且实际可用的外形是非常重要。本文采用势流-边界层耦合方法与混合遗传算法对平流层飞艇艇身的外形进行了优化。外部势流采用在艇身表面分布点源的Hess\|Smith面元法求解,边界层计算采用积分边界层方法,阻力系数采用
Squire\|Young方法计算得到。最优外形通过由遗传算法和Nelder\|Mead单纯形法组成的混合遗传算法优化得到。通过优化分析得到了一种实际可用的优化外形,具有在湍流和层流两种流态下阻力系数都比较小的优点。     

船舶推进器叶梢部流动PIV测试

利用2D-PIV（particle image velocimetry）技术在空泡水筒中对螺旋桨叶梢部流场进行了详细的测量。选用松花粉为示踪粒子,通过驱动轴轴编码器输出相位信号,经同步控制器控制CCD摄像及两激光器出光,解决了螺旋桨相位、激光器与CCD三者的同步控制与连续采集问题。轴向平面每一相位下均测量获得100幅瞬时速度矢量场,采用相位平均方法获得了同一相位下的平均速度场。比对了叶梢附近桨盘面前后流动的差异以及沿径向叶梢内外流场的不同,利用测试数据研究了梢涡、尾涡的结构。     

大型水洞中螺旋桨尾流场PIV测试研究

以标准桨DTMB-P4119为试验对象,开展了螺旋桨尾流场测试分析。试验捕捉到了清晰的螺旋桨梢涡和尾涡的流动结构以及梢涡、尾涡片在尾流中的演化。分析比较了三种载况下的尾流特征,利用捕捉到的梢涡涡心坐标计算了部分涡线的螺距角。文章将测试结果与LDV测试数据进行了比较分析,结果表明,PIV与LDV对微观流动结构的测量取得了比较一致的结果。     

翼型高低速性能优化及其对缝翼设计影响研究

采用Hicks-Henne方法参数化翼型,以巡航升阻比、低速最大升力系数为目标进行优化设计.利用试验设计方法对总体设计空间内目标函数的分布进行初步评估,得到目标函数的初始Pareto前沿面,以前沿面上的点为基准,把总体设计空间分割成独立的局部设计空间进行并行寻优,逐步更新直至得到收敛的Pareto前沿面.对前沿面上的典...     

卫星肼瓶系统减振优化设计

由于布局和安装条件的限制,某卫星肼瓶支架采用了“十”字型截面设计,这导致在发射过程中某方向肼瓶振动幅度过大,发生较大变形甚至断裂破坏。为减小肼瓶系统的过度振动,采用结构优化方法进行了减振动力学设计。考虑以下方案:1)固有频率约束下,以肼瓶系统重量为目标函数的尺寸优化;2)频率响应约束下,以肼瓶系统重量为目标函数的尺寸优化;3)以频率响应极小化为目标函数的肼瓶系统尺寸优化;4)频率响应约束下阻尼材料拓扑布局优化,以肼瓶系统重量为目标函数。对优化模型1)~3)进行直接线性化处理,采用序列二次规划方法求解。在阻尼材料拓扑布局优化中,采用了作者提出的基于阻尼拓扑敏度综合评价的阻尼材料拓扑优化准则。综合比较上述方案优化效果,阻尼拓扑布局优化设计的减振效果是最佳的。文中对动力响应优化设计问题的优化模型选取也进行了探讨。     

NEEMO 15: Evaluation of human exploration systems for near-Earth asteroids

The NASA Extreme Environment Mission Operations (NEEMO) 15 mission was focused on evaluating techniques for exploring near-Earth asteroids (NEAs). It began with a University of Delaware autonomous underwater vehicle (AUV) systematically mapping the coral reef for hundreds of meters surrounding the Aquarius habitat. This activity is akin to the type of “far-field survey” approach that may be used by a robotic precursor in advance of a human mission to a NEA. Data from the far-field survey were then examined by the NEEMO science team and follow-up exploration traverses were planned, which used Deepworker single-person submersibles. Science traverses at NEEMO 15 were planned according to a prioritized list of objectives developed by the science team. These objectives were based on review and discussion of previous related marine science research, including previous marine science saturation missions conducted at the Aquarius habitat. AUV data were used to select several areas of scientific interest. The Deepworker science traverses were then executed at these areas of interest during 4 days of the NEEMO 15 mission and provided higher resolution data such as coral species distribution and mortality. These traverses are analogous to the “near-field survey” approach that is expected to be performed by a Multi-Mission Space Exploration Vehicle (MMSEV) during a human mission to a NEA before extravehicular activities (EVAs) are conducted. In addition to the science objectives that were pursued, the NEEMO 15 traverses provided an opportunity to test newly developed software and techniques. Sample collection and instrument deployment on the NEA surface by EVA crew would follow the “near-field survey” in a human NEA mission. Sample collection was not necessary for the purposes of the NEEMO science objectives; however, the engineering and operations objectives during NEEMO 15 were to evaluate different combinations of vehicles, crew members, tools, and equipment that could be used to perform these science objectives on a NEA. Specifically, the productivity and acceptability of simulated NEA exploration activities were systematically quantified and compared when operating with different combinations of crew sizes and exploration systems including MMSEVs, EVA jet packs, and EVA translation devices. Data from NEEMO 15 will be used in conjunction with data from software simulations, parametric analysis, other analog field tests, anchoring models, and integrated testing at Johnson Space Center to inform the evolving architectures and exploration systems being developed by the Human Spaceflight Architecture Team.