分数布朗运动的小波分析

给出了分数布朗运动的连续和离散小波分析，讨论了有关的性质，并将结果用于分维估值、分数布朗运动小波变换系数的相关性研究等方面．     

Interpolation of runoff applying objective methods

The paper treats the problem of interpolating annual runoff from regular streamflow measurements in a regional scale applying objective methods. These methods are adapted to point processes like temperature and precipitation. Modifications are needed to account for the fact that streamflow is an integrated process following the hierarchical structure of river systems. The most straightforward method is therefore to relate the interpolation to the existing river network. For theoretical reasons it is preferable to interpolate the lateral inflow rather than the flow in the river itself. Procedures for the interpolation with the different approaches are developed and discussed. Special attention is put on the question how the equation of continuity can be satisfied. The Laagen drainage basin in southern Norway is used as a test area. The data consist of annual observations of streamflow and digital map information on river networks and drainage basin boundaries.     

岩溶坝区透水参数的随机模型

岩溶坝区的渗透特性受高应力场的作用,使其固有的非均质各向异性更趋复杂,采用确定性的方法不能准确的描述其本质特性。本文在对岩溶坝区透水性随机场进行分析的基础上,提出了渗透随机性的数学描述。考察岩溶坝区主要的两场作用,即渗流与应力场的耦合。基于Taylor随机有限元建立了渗流应力耦合的随机微分方程格式,并对耦合随机模型反演求解渗透参数提出了思路。     

随机扩散水质模型研究

考虑湖泊中影响总磷沉积过程的众多生态因子的变化过程,假定湖泊中总磷沉积过程由标准布朗运动驱动,建立了一个总磷浓度的随机扩散方程,推广确定性富营养化Vollenweider水质模型为随机扩散模型,获得了总磷浓度过程的解析解,进而求出了总磷浓度过程的均值和方差,指出了一种依据沉积系数调控湖泊中总磷浓度的方法。模型被应用于巢湖总磷浓度过程的模拟。     

一种海面风场反演的快速风矢量搜索算法

本文以SeaWinds散射计为例，从最大似然估计的海面风场反演方法出发，根据其目标函数的一般分布特征，提出并实现了一种快速风矢量搜索算法。然后采用美国SeaWinds散射计的部分LA实测数据对该搜索算法进行了验证，并与常规算法对同一实验数据的反演偏差及运行时间进行了对比分析，结果表明该算法在能够保证反演精度的前提下，在较大程度上提高了运行效率。     

一种Delaunay三角网的快速生成算法

本文以Lawson提出的逐点插入法为基础，借鉴方向搜索的思想，在确定插入点的影响凸包时，采用递归的局部搜索策略，形成了一种Delaunay三角网的快速生成算法。实验证明，新算法构网时间与点数基本成线性增长关系，具有较高的效率。     

Rescue Operations and Demolition Works: Automating the Pneumatic Removal of Small Pieces of Rubble and Combination of Suction Plants with Demolition Machines

The main problem in the rescue of people buried alive in damaged buildings are pieces of rubble with a diameter less than 150 mm. Since they cover the whole site, it is neither possible to scrutinize the structure of the rubble nor to estimate the dimensions of beams, columns, and parts of ceilings. The central idea of a project at the University of Karlsruhe, Institute for Technology and Management in Construction, is the automatic removal of small pieces of rubble by a suction unit. The computer-controlled pipe which is fastened to the boom of an excavator automatically follows the structure of the heap of rubble. Therefore, it can also work in areas which are out of the driver’s visible range. The suction plant replaces the dangerous manual removal which is current standard. Furthermore, a pneumatic extraction system has been designed and constructed in combination with demolition machinery, e.g. crushers and dry wire-saw technologies, to immediately suck off the crushed material (reinforced concrete, masonry).     

A Large-Eddy Simulation and Lagrangian Stochastic Study of Heavy Particle Dispersion in the Convective Boundary Layer

Large-eddy simulation and Lagrangian stochastic dispersion models were used to study heavy particle dispersion in the convective boundary layer (CBL). The effects of various geostrophic winds, particle diameters, and subgrid-scale (SGS) turbulence were investigated. Results showed an obvious depression in the vertical dispersion of heavy particles in the CBL and major vertical stratification in the distribution of particle concentrations, relative to the passive dispersion. Stronger geostrophic winds tended to increase the dispersion of heavy particles in the lower CBL. The SGS turbulence, particularly near the surface, markedly influenced the dispersion of heavy particles in the CBL. For reference, simulations using passive particles were also conducted; these simulation results agreed well with results from previous convective tank experiments and numerical simulations.     

Turbulence Scale Dependence of the Richardson Constant in Lagrangian Stochastic Models

We investigate the relative dispersion properties of the well-mixed class of Lagrangian stochastic models. Dimensional analysis shows that, given a model in the class, its properties depend solely on a non-dimensional parameter, which measures the relative weight of Lagrangian-to-Eulerian scales. This parameter is formulated in terms of Kolmogorov constants, and model properties are then studied by modifying its value in a range that contains the experimental variability. Large variations are found for the quantity, g* = 2gC₀^{− 1}, where g is the Richardson constant.