喷灌分布均匀系数研究

Research on Distribution Uniformity Coefficient for Sprinkler Irrigation Systems

为突出局部灌溉不足或灌溉过量对均匀性的影响程度,提出了基于几何平均数分布均匀系数的概念,将其定义为部分测点水深几何平均值与所有测点算术平均值的比值。并根据部分测点水深数据的提取方法不同,分为1/4低值、1/4高值、1/2低值和1/2高值分布均匀系数。用MATLAB和VC^++语言编制了可以实现上述分布均匀系数计算的软件"SIUEW1.0"。结果初步证明:基于几何平均数的乘法模型要比基于算术平均数的加法模型更加突出了部分低(或高)于平均值的测点水深数据对均匀系数的影响程度,因此更适用于时局部灌溉不足或过量灌溉有严格控制要求地块的灌溉均匀性评价;无论高值和低值,取点数越少,均匀性的评价结果越差。

In order to emphasize the influence of over-irrigation or insufficient irrigation on irrigation uniformity, this paper put forward the concept of distribution uniformity coefficient based on geometric average, which is defined as the ratio between geometric average of the depth of partial measuring points and arithmetic average of all points. According to the different method for gaining the depth of partial measuring points, the distribution uniformity coefficient is defined as four categories, i.e. Low Quarter Distribution Uniformity, High Quarter Distribution Uniformity, Low Half Distribution Uniformity, and High Half Distribution Uniformity. The SIUEW1.0 software for the calculation of above coefficients is programmed through using VC＋＋ and MATALAB software. Taking the water quantity distribution data of single RAINBIRD 30PSH sprinkler under the windy condition and 300kPa work pressure as example, which is measured through six rain gauges with spokewise layout, the distribution uniformity coefficients based on geometric average and arithmetical average under the condition that the sprinklers are foursquare or equilateral triangular layout and the layout coefficient is 1.004 1.40, are calculated. The results show that the multiplication model based on geometric average emphasizes the influence of partial data, which are lower than the average value, on the uniformity coefficients more than the addition model based on arithmetical average. So it is more suitable for the uniformity evaluation of irrigation systems, in which over irrigation and insufficient irrigation are prevented strictly. No matter the value is high or low, less the quantity of measuring point is, worse the result of uniformity evaluation is.