摘要
温室是一个封闭的农业生态系统,它内部的光照、温度、湿度及CO2浓度等环境条件是影响作物生长发育最重要因素。本文以南方单栋塑料温室为研究对象,在全面对比分析单膜和双膜覆盖状况下南方塑料温室的小气候环境特征基础上,充分考虑作物对环境的热力和动力作用,利用多种模型开展南方塑料温室不同覆盖状况和不同管理方式下各环境要素的数值模拟研究。主要研究内容和结果如下：1.两种覆盖状况下,除CO2浓度外,其他要素的室内值均与对应高度室外值有相似的日变化规律,太阳辐射、气温和地温表现为日出后先增大,正午达到峰值,然后逐渐减小；相对湿度与之相反,日出后先减小后增大。室外CO2浓度在一天中仅有微小的波动,而室内CO2浓度随着日出后太阳辐射和作物光合作用的由强到弱,呈现显著减小至下午15：30之后逐渐增大的日变化规律。温室内外对应要素值的差异在一天中呈现波动趋势,其规律与要素本身的日变化特征类似。与单膜覆盖相比,双膜覆盖能够更为显著提高温室内的气温和地温,平均差异分别约为5.0℃和4.0℃,在冬季有利于室内作物的正常生长。但双膜覆盖同时也减少了进入温室的太阳辐射,提高了室内的相对湿度。因此在使用多层覆盖温室时,针对其室内环境特点采取合理的调控措施十分必要。2.无论是室内温湿环境因子的平均值,还是其极值,都与室外气象要素具有显著的相关关系,多数关系均通过95%的显著性检验。表明基于室外气象要素,通过统计方法开展对室内环境因子的模拟和预测具有合理性和可行性。充分利用室内温湿要素的历史数据,提出将ARIMA模型(自回归移动平均模型)与多元回归模型相结合对温室内温湿条件尤其是极端温度进行模拟预测的方法。通过分析10分钟、1小时和逐日不同时间尺度上的效果发现,其精度在整体上超过传统的多元回归和神经网络方法,并且避免了神经网络方法稳定性差的缺点,在实际生产预报中可以推广。3.依据温室内外环境的能量平衡和质量守恒,分层构建单膜和双膜覆盖下的室内温湿环境物理模型。与单膜模型的差异在于双膜模型还必须考虑外膜与内膜之间的能量交换,外膜和内膜分别同两膜之间夹层空气的能量交换。利用实测资料对模型效果进行验证发现不同覆盖状况下,气温预测值与实测值的平均误差均低于7.0%,相对湿度预测值与实测值的平均误差均未超过8.0%,地温预测值与实测值的平均误差均小于5.0%,表明物理模型的预测效果较好。三种天气状况下,阴天的精度最高,多云天次之,晴天相对精度较低。4.利用室内外实测数据分别进行通风和不通风状况下温室内温湿环境的CFD(计算流体力学)数值模拟和验证。不通风状况下,利用CFD方法得到的气温模拟值与实测值之间的平均相对误差和均方根误差分别为4.47%和1.34℃,模拟结果与实测结果吻合良好。通风状况下,气温模拟值与实测值之间的平均相对误差和均方根误差分别为4.68%和1.54℃,精度略低于不通风时段；相对湿度模拟值与实测值之间的平均相对误差和均方根误差分别为5.22%和3.54%。CFD模拟的室内气温和相对湿度的空间分布规律与实测情况基本一致,说明该方法在温室环境模拟中的应用兼具可行性和合理性。其结果充分展示了不同垂直高度和不同方位的温湿差异,为温室环境的局部调控和精细化管理提供数据基础。5.采用CFD技术探讨了相同假设初始条件和边界条件下,不同通风时间、不同通风风速和不同通风口大小对温室通风效果的影响。随着通风时间的增加、风速的增大和通风口面积的扩大,降温效果在温室内部由北(进风口)向南(出风口),由低到高逐渐发挥作用,降温范围不断扩大。但气温下降的幅度差异并非随三个参数等梯度的变化而保持不变,表明当室内外空气的混合趋于均匀稳定时,三个参数改变带来的降温效应会逐渐减弱。实际生产实践中的自然通风状况比CFD模拟试验要复杂很多,虽然CFD模拟并不能完全真实反映现实情况,但其结果符合物理规律和理论预期,与人们的现实认知也比较一致,并且能够在三维空间上提供较为精细的技术数据,可以为实际生产中的温室环境尤其是局部区域的环境调控和优化提供一定的理论依据。
The internal environmental conditions including solar radiation, temperature, relative humidity and CO2 concentration are important to crops growth because the greenhouse looks like a closed agro-ecosystem.In this paper, the spatial and temporal characteristics of interior microclimate of single-span plastic greenhouse for South China in autumn and winter were analyzed. Taking full account of the thermal effect and dynamic effect of plants to indoor environment, the numerical simulations of environmental factors for plastic greenhouses with diverse covering layers and management methods were carried out using statistical models, physical models and computational fluid dynamics models. The main research contents and conclusions are as follows:1. The spatial and temporal characteristics of internal environmental conditions including solar radiation, air temperature, relative humidity, soil temperature and CO2 concentration of greenhouses with two cover types indicated that the diurnal variations of indoor meteorological factors except CO2 concentration were similar to that of outdoor meteorological factors in same height. Solar radiation, air temperature and soil temperature increased after sunrise, peaked in noon, and then decreased. While relative humidity showed the opposite pattern that first decreased after sunrise, and then gradually increased. Outdoor CO2 concentration presented only small fluctuations during a day. However, indoor CO2 concentration showed a significant diurnal variation that declined after sunrise and gradually rose until from 15:30 pm with the solar radiation and crop photosynthesis becoming strong to weak. The differences between indoor factors and corresponding outdoor factors also fluctuated during a day and the variation patterns were similar to that of the own factors. It was found that indoor air temperature and relative humidity differed from north to south. The air temperature of south part was greater than that of the north part, while the relative humidity of south part was smaller than that of the north part. The difference between east part and west part was not obvious. Double-layer covering greenhouse can offer higher air temperature and soil temperature compared to single-layer covering greenhouse, and was beneficial to crops growth in winter. But the solar radiation was smaller and the relative humidity was greater in double-layer covering greenhouse than in single-layer covering greenhouse. Therefore, it is necessary to take reasonable adjustment measures for its indoor environmental characteristics when using multi-layer covering greenhouse.2. The average values and the extreme values of indoor air temperature and relative humidity were both significantly related to the outdoor meteorological elements at 5% significance level. So it is reasonable and feasible to use statistical methods for simulating and predicting the indoor environmental factors based on the outdoor weather elements. Making full use of the historical indoor temperature and relative humidity data, multiple-regression method combined with autoregressive integrated moving average model (ARIMA model) was used to simulate and predict the indoor temperature and relative humidity, especially the extreme values of these factors. The accuracy of new method was higher than the traditional multiple-regression method and the BP neural network by analyzing the effect of three time scales including ten minute, one hour, and one day. This new method avoiding the short come of poor stability of the BP neural network method can be extended in the actual production weather forecast.3. The plastic greenhouse was divided into covering layer, indoor air, plant canopy and soil layer. Based on the mass and energy balance between indoor and outdoor environments, the physical models were established to estimate the air temperature and relative humidity for single-layer covering greenhouse and double-layer covering greenhouse respectively and were verified by measured meteorological data. The mean relative errors between simulated values and measured values of air temperature, relative humidity and soil temperature were less than 7.0%,8.0% and 5.0% respectively, indicating that the physical models exhibited high accuracy. The highest accuracy was found for overcast sky, followed by cloudy day.4. Based on the indoor and outdoor observed data, the simulation and verification of air temperature and relative humidity were carried out under ventilation and non-ventilation conditions by the use of computational fluid dynamics (CFD) technology. The mean relative errors and the root mean square error between simulated values and measured values of air temperature were 4.47% and 1.34℃under non-ventilation conditions. The mean relative errors and the root mean square error between simulated values and measured values of air temperature were 4.68% and 1.54℃, and were 5.22% and 3.54% for relative humidity under ventilation conditions. The spatial distributions of air temperature and relative humidity simulated by CFD models were consistent with the observed distributions, showing that this method is feasible and reasonable in the greenhouse environment simulation. The results fully demonstrating the differences of temperature and relative humidity between diverse vertical heights and diverse directions provided a data base for local control and refine management of greenhouse environment.5. The ventilation effects under different ventilation time, different ventilation velocities and different ventilation sizes were discussed based on the computational fluid dynamics (CFD) technology. With the increase of ventilation time, ventilation velocities and ventilation sizes, the cooling effect of ventilation played a role gradually from the north part to the south part, from lower part to higher part in the plastic greenhouse. However, the difference between decreasing magnitudes of temperature varied with the constant gradient of the three parameters, indicating that the cooling effect caused by the variation of these three parameters will be gradually weakened when the mixture of indoor and outdoor air was steady. Actual natural ventilation condition was more complex than the CFD experiment. Although the CFD simulation can not completely reflect the reality, the results were consistent with the physical laws and theoretical expectations and able to provide more refined data in three-dimensional space. It can be used to offer a theoretical basis for the control and optimization of greenhouse environment.
引文
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