基于耦合声场建模的膨胀腔消声器声学性能分析及试验

针对圆柱形膨胀腔消声器三维建模及声学性能分析问题, 提出一种基于切比雪夫变分原理的耦合声场建模方法, 建立三维圆柱形膨胀腔消声器理论模型并搭建试验台架, 传递损失试验结果验证了理论模型的准确性. 将膨胀腔消声器内部声场分解为多个子声场, 基于子声场间压力与质点振速连续性条件, 推导声场耦合变分公式, 构建子声场拉格朗日泛函. 将子声场声压函数展开为切比雪夫-傅里叶级数形式, 通过瑞利-里兹法求解膨胀腔消声器频率、声压响应及传递损失. 计算并对比分析扩张比、扩张腔长度、进出口管偏置对膨胀腔消声器消声性能的影响. 结果表明: 扩张比增大会有效提高消声器在低频段的消声性能, 进出口管的偏置对消声器消声性能影响很小.     

一种基于GIS的选址问题及算法

本文考虑了选址区域内存在地理阻断情况下的一种基于GIS的选址问题.对单配送中心选址模型,以GIS返回的任意两点间的最短可行路径的长度作为修正距离函数,分析了目标函数在凸形选址区域上非凸非连续的性质.进一步,采用给出了一种近似搜索算法并通过一个实例计算与重心法进行了比较.     

幅相一致行波管高频电路CAD研究

 在行波管的设计和装配过程中，各部件的尺寸必须严格控制，高频电路参数的离散对行波管色散特性有极大的影响。使用螺旋导电面模型，模拟计算了高频结构各主要参数离散对色散特性和轴向互作用耦合阻抗的影响。分析计算了夹持杆宽度、翼片高度、螺旋线平均半径、螺距、夹持杆介电常数等离散时对色散特性和轴向互作用耦合阻抗的影响，为新型幅相一致行波管的设计和生产提供了很有价值的参考建议。     

水反常热膨胀特性的简易实验

通过改变带细玻璃管的烧瓶中水的温度导致水位变化来粗略演示水的收缩情况.为较准确测定水最大密度时的温度,将2个温度计放在水下较浅处和较深处,分别测量当温度经由4℃升高时和由4℃降低时两温度计的示数,进而验证水的反常热膨胀特性.     

Study of an identity

We solve the word problem of the identity x(yz) = (xy)(yz) by investigating a certain group describing the geometry of that identity. We also construct a concrete realization of the one-generated free algebra relative to the above identity. Received March 23, 2001; accepted in final form July 6, 2002.     

软X光辐射烧蚀实验方法研究

 用强激光辐照金箔靶，以金箔背侧软X光辐射为基础，提出了软X光辐射烧蚀研究的新靶型、新方法，做了C₈H₈样品辐射烧蚀初步实验。     

一次教学实验仪器更新的尝试

探讨了教学实验仪器所面临的更新换代的新形势，结合视频光学投影比工主的研制论述更新的原则和方法。     

Orders of Indescribable Sets

We extract some properties of Mahlo’s operation and show that some other very natural operations share these properties. The weakly compact sets form a similar hierarchy as the stationary sets. The height of this hierarchy is a large cardinal property connected to saturation properties of the weakly compact ideal.     

粗糙度与气体稀薄性对微尺度流动特性的影响

对氮气和氦气在粗糙微通道以及光滑微通道内流动进行了阻力特性实验研究。实验结果表明,即使在较小的相对粗糙度高度下,由于微通道中的粗糙度分布密集,会极大地增加流动阻力,这是导致文献中微通道流动阻力系数实验值相互偏差的主要原因之一;而对于滑移区的气体流动,气体稀薄性使流动阻力明显减小而导致流量增加。     

Temperature effect on deposition rate of silicon nitride films

Temperature effects on deposition rate of silicon nitride films were characterized by building a neural network prediction model. The silicon nitride films were deposited by using a plasma enhanced chemical vapor deposition system and process parameter effects were systematically characterized by 2⁶⁻¹ fractional factorial experiment. The process parameters involved include a radio frequency power, pressure, temperature, SiH₄, N₂, and NH₃ flow rates. The prediction performance of generalized regression neural network was drastically improved by optimizing multi-valued training factors using a genetic algorithm. Several 3D plots were generated to investigate parameter effects at various temperatures. Predicted variations were experimentally validated. The temperature effect on the deposition rate was a complex function of parameters but N₂ flow rate. Larger decreases in the deposition rate with the temperature were only noticed at lower SiH₄ (or higher NH₃) flow rates. Typical effects of SiH₄ or NH₃ flow rate were only observed at higher or lower temperatures. A comparison with the refractive index model facilitated a selective choice of either SiH₄ or NH₃ for process optimization.