潜艇指挥台围壳对阻力和伴流场影响数值研究

采用Reynolds平均Navier-Stokes(RANS)方法计算潜艇三维粘性流场,分析潜艇指挥台围壳对潜艇水动力性能的影响.采用全附体SUBOFF模型验证了CFD方法,通过将螺旋桨盘面处的实效伴流场、艇体表面压力分布以及模型总阻力的模拟结果与Taylor船池的实验结果进行对比.比较结果显示CFD计算结果与实验数据具有很好的一致性,表明CFD方法可以用于潜艇指挥台围壳设计的水动力计算.通过数值计算研究指挥台围壳的高度和在艇上的分布位置对其后方的流场、螺旋桨盘面处的伴流场和阻力的影响.     

动态矿区DEM生成方法及其在土地复垦中的应用研究

首先根据我国矿区土地复垦工作中存在的不足,通过研究提出"动态土地复垦"的概念,并确定了动态土地复垦方案制定的数据基础——动态矿区数字高程模型。动态矿区数字高程模型应该包含煤矿生产对地表的累积影响结果数据和地表的原始地形信息,开采沉陷预计结果可以提供前者,地面测量可以提供后者,经过数据处理可以按时间间隔生成描述塌陷区的一系列数字高程模型(DEM)(准动态),然后运用DEM的空间分析技术提取塌陷区在不同时间段的土地破坏类型、范围和位置数据,并根据这些数据进行动态土地复垦方案决策数据的提取,经实例验证该方法可行。     

抗差卡尔曼滤波在聊城古建筑动态数据处理中的应用

古建筑在日照、风压等因素的影响下,易产生老化变形,一般会采用及时补救的方法,有时并没有分析其根本原因,本文在布设动态监控网,获得监控数据后,用抗差卡尔曼滤波对监控数据进行分析的方法,对变形方向进行预测,预测值与平差值的误差均在限差范围内,且变化方向大致相同,可以将它作为古建筑变形方向的预报,做到防患于未然。     

用曲面插值方法建立海洋局部地磁场模型

地磁场模型是地磁场的数学表达式,国内外已有多种建立地磁场模型的方法,对于局部地磁场模型来说,应用比较多的是多项式方法和曲面样条函数方法。讨论了基于曲面样条函数建立局部地磁场模型的原理和方法,并在实测数据基础上对模型的精度进行了验证。     

内蒙孪井灌区土壤盐分运移规律分析

研究了孪井灌区典型地段包气带土层可溶盐分在灌溉前后的变化,分析了灌溉水和土壤水盐分动态特征,并在室内进行了不同类型土壤的淋滤试验。结果表明引黄灌溉是使土壤脱盐的过程,尤其是灌溉初期,土壤脱盐非常明显。灌溉洗盐与包气带岩性和灌水量有关,砂壤土较黏砂土易脱盐,土壤初始含盐量越大,灌溉量越大,土壤脱盐量就越大。     

计算机串口记时延迟的测量与分析

动态测量中,需要记录观测量的观测时刻,时刻作为测量的基本观测量,需要较高的计时精度,通过串口利用计算机计时是一种便捷可行的方法,但串口的时延是必须要考虑的因素。本文介绍了计算机串口计时的原理及影响其时延的相关因素,阐述了进行时延测量的方法,并通过实验对影响串口时延的5种因素进行了相应的测试和分析,实验结果表明,通常情况下串口时延在毫秒级,通过提高计时程序优先级的方法可成功将时延减小至0.35m s以内;低动态条件下串口时延对动态测量的影响较小,在某些检测设备中甚至可以忽略。     

低轨卫星初始状态误差对反演地球重力场的影响

基于动力学法反演地球重力场的基本理论,研究了卫星初始状态向量误差对应用低轨卫星精密轨道数据反演地球重力场的影响。在仅考虑低轨卫星初始状态误差的情况下进行了模拟计算,结果表明:在利用低轨卫星精密轨道数据反演地球重力场时,卫星初始状态向量误差需要重新进行估计;在目前的轨道精度水平下,若不顾及误差方程二次项的影响,反演弧长不宜过长;卫星初始状态速度误差(约1.5mm/s)的影响要大于位置误差(约10 cm)的影响。     

Calibrating the GOCE accelerations with star sensor data and a global gravity field model

A reliable and accurate gradiometer calibration is essential for the scientific return of the gravity field and steady-state ocean circulation explorer (GOCE) mission. This paper describes a new method for external calibration of the GOCE gradiometer accelerations. A global gravity field model in combination with star sensor quaternions is used to compute reference differential accelerations, which may be used to estimate various combinations of gradiometer scale factors, internal gradiometer misalignments and misalignments between star sensor and gradiometer. In many aspects, the new method is complementary to the GOCE in-flight calibration. In contrast to the in-flight calibration, which requires a satellite-shaking phase, the new method uses data from the nominal measurement phases. The results of a simulation study show that gradiometer scale factors can be estimated on a weekly basis with accuracies better than 2 × 10⁻³ for the ultrasensitive and 10⁻² for the less sensitive axes, which is compatible with the requirements of the gravity gradient error. Based on a 58-day data set, scale factors are found that can reduce the errors of the in-flight-calibrated measurements. The elements of the complete inverse calibration matrix, representing both the internal gradiometer misalignments and scale factors, can be estimated with accuracies in general better than 10⁻³.     

Non-stationary covariance function modelling in 2D least-squares collocation

Standard least-squares collocation (LSC) assumes 2D stationarity and 3D isotropy, and relies on a covariance function to account for spatial dependence in the observed data. However, the assumption that the spatial dependence is constant throughout the region of interest may sometimes be violated. Assuming a stationary covariance structure can result in over-smoothing of, e.g., the gravity field in mountains and under-smoothing in great plains. We introduce the kernel convolution method from spatial statistics for non-stationary covariance structures, and demonstrate its advantage for dealing with non-stationarity in geodetic data. We then compared stationary and non- stationary covariance functions in 2D LSC to the empirical example of gravity anomaly interpolation near the Darling Fault, Western Australia, where the field is anisotropic and non-stationary. The results with non-stationary covariance functions are better than standard LSC in terms of formal errors and cross-validation against data not used in the interpolation, demonstrating that the use of non-stationary covariance functions can improve upon standard (stationary) LSC.