一种固体发动机光线投射体绘制加速方法

固体发动机ICT断层图像构成的体数据场规模大、空体素比例小,现有的光线投射加速方法效果不明显,并且在其三维可视化故障诊断中,不是体数据场中的所有信息都对内部故障检测有意义,三维重建出所有体数据大大增加了计算量.为此,提出一种在同体发动机体数据分割基础上的光线投射加速方法.该方法利用分割结果,建立分割信息体数据场,结合现...     

小型太阳能无人机超大展弦比轻质主梁设计

太阳能无人机表面需要铺设太阳能电池,这就要求太阳能无人机的结构要同时满足不影响太阳能电池效率的光学特性和要达到足够强度、刚度的力学特性。文章针对小型太阳能飞机超大展弦比机翼进行轻质化结构设计,过程中充分考虑超大展弦比机翼的柔性变形,对比不同的设计方案并提出合理设计方案。     

TAPS燃烧室燃油喷嘴设计特点分析

创新的、清洁燃烧的TAPS燃烧室由美国GE公司研发,在CFM56-7B发动机上得到验证后,成功地应用到新一代商用涡扇发动机GEnx上。对TAPS燃烧室做了简要介绍,对其燃油喷嘴的结构特点进行了概括性分析,并提出了启迪与思考。     

复合材料气瓶铝合金内衬泄漏失效分析北大核心CSCD

复合材料气瓶在自紧过程中发生泄漏。通过断口、金相、有限元分析及综合分析得出:气瓶瓶嘴根部各向异性的粗晶组织降低了气瓶瓶嘴根部的韧、塑性和强度是气瓶内衬自紧过程中发生低压力开裂泄漏的主要原因,通过改进和控制收口加热与旋压工艺,细化了气瓶瓶嘴根部的组织,最终解决了其自紧过程中泄漏问题,稳定复合材料气瓶的质量。     

碳纤维与芳纶纤维复合材料机械加工刀具选用

通过对复合材料特性和加工机理的分析,论述了复合材料机械加工对刀具材质、结构和几何参数的要求,介绍了几种适合于碳纤维和芳纶纤维复合材料机械加工的钻削和铣削刀具.     

高温环境下板壳结构局部屈曲理论研究

传统高温板壳结构的热屈曲分析方法,在解决热-机械载荷耦合作用下复杂板壳结构的屈曲问题时存在较大的局限性,合理的局部热屈曲理论分析方法有助于提高板壳热结构设计水平。因此,根据高温板壳的结构特征和力学特征,在"机械载荷等效成局部边界压应力效应"假设前提下,提出了四种典型的应力等效的局部热屈曲模型,基于初始后屈曲渐近分析理论,建立了一套有效的局部热屈曲理论分析方法。采用上述方法,研究了完善和具有初始缺陷板壳的弹性热后屈曲性态,具体分析了带预载的四边简支模型,带预载的四边固支模型,带预载的三边简支、一边自由模型以及带预载的三边固支、一边自由模型,给出了板壳长度尺寸、厚度等参数对热屈曲载荷的影响规律,并将其推广到高阶热屈曲问题中。分析结果表明:板体现为分叉式屈曲,壳体现为跳跃式屈曲;在长宽比一定的情况下,长度越长,屈曲临界载荷越小,厚度越厚,屈曲临界载荷越高。     

空间柔性网—膜结构分析的数学规划算法

 提出了以参数变分原理为基础的数学规划算法, 以计算分析太空用品中网- 膜结构起皱现象。针对网状柔性结构分析, 算法的原理是将结构化成多个具有一定非线性本构关系的杆单元, 并在此基础上进行杆系结构非线性分析的规划算法构造, 可以较好地模拟网状柔性结构的起皱性质。而对于一般的膜结构, 则构造了用于直接分析的膜单元相对应的本构模型, 并在此基础上构造问题的求解算法。不同于常规的数值计算方法, 提出的算法不依赖于应力的迭代过程。参数变分原理和数学规划算法的应用, 使本算法可以很有效地分析预测网- 膜起皱的区域、模式和应力分布, 从而为解决网- 膜结构起皱问题提供依据。给出的数值算例说明了方法正确与有效性。     

Geometry and size optimization of stiffener layout for three-dimensional box structures with maximization of natural frequencies

Based on the growth mechanism of natural biological branching systems and inspiration from the morphology of plant root tips, a bionic design method called Improved Adaptive Growth Method (IAGM) has been proposed in the authors’ previous research and successfully applied to the reinforcement optimization of three-dimensional box structures with respect to natural frequencies. However, as a kind of ground structure methods, the final layout patterns of stiffeners obtained by using the IAGM are highly subjected to their ground structures, which restricts the optimization effect and freedom to further improve the dynamic performance of structures. To solve this problem, a novel post-processing geometry and size optimization approach is proposed in this article. This method takes the former layout optimization result as start, and iteratively finds the optimal layout angles, locations, and lengths of stiffeners with a few design variables by optimizing the positions of some specific node lines called active node lines. At the same time, thicknesses of stiffeners are also optimized to further improve natural frequencies of three-dimensional box structures. Using this method, stiffeners can be successfully separated from their ground structures and further effectively improve natural frequencies of three-dimensional box structures with less material consumption. Typical numerical examples are illustrated to validate the effectiveness and advantages of the suggested method.     

基于认知结构SOAR的机器人路径规划

针对环境未知、目标位置已知情况下机器人路径规划问题,提出了一种基于认知结构SOAR的探索方法。首先,结合箱子移动问题介绍了SOAR的基本概念及求解机制;其次,在讨论了人的行为模型和SOAR的决策过程基础上,设计了基于SOAR-Agent的行为建模方法;最后,利用本文提出的方法,设计长期知识,使机器人能够模拟人在未知动态环境下向目标搜索的行为。通过仿真实验,对比分析了长期知识的完备程度对机器人顺利完成任务的影响,同时也验证了该方法的有效性。     

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.