基于遗传算法与响应面模型的压气机叶片气动优化设计

1引言由于叶轮机械内部复杂的三维流动,各种波涡结构的广泛存在,吸引了众多学者对其进行了积极的探索与研究。CFD作为可靠省时的设计手段之一,在叶轮机械气动设计的过程中显示了其卓越的性能[1~3]。June Chung和Lee K i D[4]采用基于梯度法的优化程序,以绝热效率为目标函数对NA     

N2O单组元微推进系统及其喷管流场的初步研究

针对利用N2O单组元催化分解产生热气、用于精确控制飞行器姿态的微推进系统,提出了初步的系统方案和主要设计参数.通过数值仿真,对设计推力为1N的微推进器喷管在高空工况的内流场和真空羽流场进行了研究.计算得到喷管的推力为1.041N,比冲为1922.7N·s/kg,从而验证了喷管相关设计参数的合理性.理论分析为后续的地面试验件的设计加工及其热试车提供了有益的参考.     

风扇转子的气动优化设计研究

风扇转子气动优化设计是结合CFD、遗传算法和风扇特性的跨学科自动设计方法,这种设计较少依赖经验,能实现多目标和多设计参数组合的优化设计。先研究了四个关键技术：具有耗时少、计算较准确的叶片通道流场数值计算方法;具有良好寻优能力的数值最优化方法;三维叶片造型的参数化方法;以气动性能为目标的多目标函数构建方法。再将上述四个关键技术模块整合,构建简易的气动优化设计软件。三维叶片优化算例证明了该软件的设计能力,其设计结果较满意、设计效率较高、设计周期短。     

榫连结构几何参数对接触应力的影响

以某燕尾形榫连结构作为研究对象,采用有限元法开展了接触应力分析,用以研究榫连结构几何参数对其接触应力的影响,并应用于工程中榫连结构的强度分析。计算表明榫齿/榫槽接触区边缘存在较高的应力梯度;在此基础上,建立了不同的几何模型以考查关键几何参数对接触应力的影响,例如接触面角度、长度、接触区边缘圆角半径以及接触面几何形式,重点研究了圆弧/直线几何形式下的接触应力分布,并讨论了其几何参数对接触区应力改善的作用;研究结果表明合理选择几何参数可以改善接触区应力分布,提高榫连结构寿命。     

复合材料格栅结构稳定性与优化分析

阐述了复合材料格栅结构稳定性及优化分析方法研究概况。指出在格栅结构初级设计阶段可利用拟膜分析法对等效的格栅平板、圆筒及锥筒进行整体屈曲计算即可,而对于复杂构型格栅结构,由于不对称性及拉-压-扭耦合效应存在很难获得解析解,需借助有限法对结构进行非线性求解而求得其真实力学响应,同时还介绍了其他算法,如遗传算法。最后介绍了复合材料格栅结构破坏机理的一些试验方法。     

面向在轨靠泊加注的管路力学分析与优化设计

面向在轨伸出式靠泊加注任务,针对对接过程中加注管路因同步外伸而产生附加力的问题,提出了一种偏置椭圆螺旋构型的加注管路。首先,推导了管路的轴向刚度和应力数学表达式,通过有限元仿真和实验校验了上述两个表达式的准确性。之后,将偏置椭圆螺旋管路与其他三种适用管路在同一设计空间下分别进行了基于最小轴向刚度的构型优化设计,得到了上述四种管路各自的最优构型与最小附加力,形成了该类在轨加注螺旋管路的完整设计方法。与仿真和实验结果对比,所推导的管路轴向刚度与应力数学表达式的相对误差均小于5%,可用于该类管路的设计分析。与无偏圆螺旋管路相比,所提出的偏置椭圆螺旋管路将靠泊加注对接过程中的管路附加力降低了约83.5%,更适用于在轨靠泊加注任务。     

Selecting scale factor of Bayesian multi-fidelity surrogate by minimizing posterior variance

The Bayesian Multi-Fidelity Surrogate(MFS) proposed by Kennedy and O’Hagan(KOH model) has been widely employed in engineering design, which builds the approximation by decomposing the high-fidelity function into a scaled low-fidelity model plus a discrepancy function. The scale factor before the low-fidelity function, ρ, plays a crucial role in the KOH model. This scale factor is always tuned by the Maximum Likelihood Estimation(MLE). However, recent studies reported that the MLE may sometimes r...     

Aerodynamic optimization of a high-lift system with adaptive dropped hinge flap

The Adaptive Dropped Hinge Flap(ADHF) is a novel trailing edge high-lift device characterized by the integration of downward deflection spoiler and simple hinge flap, with excellent aerodynamic and mechanism performance. In this paper, aerodynamic optimization design of an ADHF high-lift system is conducted considering the mechanism performance. Shape and settings of both takeoff and landing configurations are optimized and analyzed, with considering the kinematic constraints of ADHF mechanism, ...     

一类广义凸规划的最优性条件

最优性条件是数学规划理论中最基本和核心的内容之一。本文讨论了由一类（ｈ,φ）－凸函数所构成的广义凸规划的最优性条件。利用文〔２〕中定义的Ｂｅｎ－Ｔａｌ广义代数运算,本文得到了这类广义凸规划的Ｋｕｈｎ－Ｔｕｃｋｅｒ充分条件和必要条件,所得结果推广了通常凸规划相应的结论     

An aero-engine life-cycle maintenance policy optimization algorithm: Reinforcement learning approach

An aero-engine maintenance policy plays a crucial role in reasonably reducing maintenance cost. An aero-engine is a type of complex equipment with long service-life. In engineering,a hybrid maintenance strategy is adopted to improve the aero-engine operational reliability. Thus,the long service-life and the hybrid maintenance strategy should be considered synchronously in aero-engine maintenance policy optimization. This paper proposes an aero-engine life-cycle maintenance policy optimization algorithm that synchronously considers the long service-life and the hybrid maintenance strategy. The reinforcement learning approach was adopted to illustrate the optimization framework, in which maintenance policy optimization was formulated as a Markov decision process. In the reinforcement learning framework, the Gauss–Seidel value iteration algorithm was adopted to optimize the maintenance policy. Compared with traditional aero-engine maintenance policy optimization methods, the long service-life and the hybrid maintenance strategy could be addressed synchronously by the proposed algorithm. Two numerical experiments and algorithm analyses were performed to illustrate the optimization algorithm in detail.