SolidWorks在复杂零件建模和设计分析中应用

介绍了SolidWorks在复杂零件建模和设计分析中应用,示例说明了用放样、弯曲等高级命令进行复杂零件建模的方法,示例说明了COSMOSXpress进行分析的具体操作方法。     

船舶结构多学科设计优化近似方法的比较

随着船舶结构设计日益复杂,使用统计学方法为复杂的计算机分析建立近似模型已经逐渐成为一种趋势.文章简要地介绍了三种多学科设计优化中常用的建立近似模型的方法;响应面、Kriging和径向基神经网络,并通过两个算例对其性能进行了比较.建立近似模型所需的样本点由一系列随机正交表提供,三种近似方法的性能则通过误差分析来进行比较.除了常用的二阶响应面模型,文中也构造了三阶响应面模型来进行比较.研究结果表明:Kriging方法以其准确性和稳定性,比其它方法更加适合船舶结构的多学科设计优化.     

舰艇作战系统功能划分方法

舰艇作战系统的集成设计方向是系统功能优化划分。通过剖析美、俄、欧洲风格的作战系统功能划分思路,系统地研究分析作战系统功能划分方法,认为系统建模应指导功能划分。概括作战系统最基本的核心功能,提出依照四条主干线划分功能的方法,建议重点关注新研制功能(如协同交战能力)的划分与实现。     

挖泥船绞刀的三维建模方法

分析绞吸式挖泥船绞刀的结构及工作原理,介绍在Pro／E环境下生成三维模型中的内外轮廓线方法,缩短绞刀设计周期,为后续分析绞刀切削性能提供方便。     

港口开发建设对田湾核电站取水工程的影响

建立了基于不规则三角形网格潮流泥沙数学模型,在对模型进行充分验证的基础上,对连云港港口扩建(旗台港区建设)规划方案和埒子口港区规划对田湾核电站已建取水工程的影响进行了数值模拟研究,计算了取水口附近滩面的冲淤变化和等深线位移。研究结果表明:连云港港口扩建及埒子口港区规划实施后,已建的田湾核电站取水明渠口门附近水域变成泥沙淤积环境,滩面将会淤高,设计的取水口门水深等深线将会外移,核电站正常取水将会受到影响。     

Estimating near-road pollutant dispersion: A model inter-comparison

A model inter-comparison study to assess the abilities of steady-state Gaussian dispersion models to capture near-road pollutant dispersion has been carried out with four models (AERMOD, run with both the area-source and volume-source options to represent roadways, CALINE, versions 3 and 4, ADMS and RLINE). Two field tracer studies are used: the Idaho Falls tracer study and the Caltrans Highway 99 tracer study. Model performance measures are calculated using concentrations (observed and estimated) that are paired in time and space, since many of the health related questions involve outcomes associated with spatially and temporally distributed human activities. All four models showed an ability to estimate the majority of downwind concentrations within a factor of two of the observations. RLINE, AERMOD-V, and ADMS, also have the capability to predict concentrations upwind of the roadway that result from low-speed meandering of the plume. Generally, RLINE, ADMS, and AERMOD (both source types) had overall performance statistics that were broadly similar, while CALINE 3 and 4 both produced a larger degree of scatter in their concentration estimates. The models performed best for near-neutral conditions in both tracer studies, but had mixed results under convective and stable conditions.     

论第二语言学习中输出语的功能

本文讨论了第二语言学习过程中输出语的三个功能，第一个是注意触发功能，第二个是假设验证功能，第三个是元语言功能，我们可以将输出语的的第三个功能作为教育的手段而促使另外两个功能产生作用，并探讨了输出语的功能在外语教学中对培养学生语言交际能力的作用。     

Showcase软件在客车造型方案评审中的应用

通过对Showcase软件的介绍，阐述了Showcase软件在客车造型方案评审中的应用。     

水面舰艇规避自导鱼雷的建模计算与分析

通过对水面舰艇规避来袭自导鱼雷过程的分析,建立了水面舰艇规避鱼雷的战术基础模型,对水面舰艇规避来袭自导鱼雷效果进行了建模计算,给出了水面舰艇防御鱼雷的组织方法,对研究水面舰艇规避鱼雷战术具有一定的参考价值.     

Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.