基于CFD的温室气温时空变化预测模型及通风调控措施

Prediction model on temporal and spatial variation of air temperature in greenhouse and ventilation control measures based on CFD

夏季温室高温湿热,对作物生长产生重大危害,制定合理的夏季温室气温调控方案,是提高温室生产效益,降低温室气温调控能耗的关键问题。该文基于计算流体力学(computational fluid dynamics,CFD)方法,结合气象预报信息,针对苏南地区大型连栋温室,建立了夏季温室气温时空变化预测模型,通过设置边界参数,对不同通风条件下温室气温的时空变化进行了预测,并通过试验验证了模型的有效性。试验结果表明,预测值与实测值吻合良好,均方根误差在1.2℃以内,最大相对误差在6%以内,平均相对误差在4%以内。不同通风降温条件下的试验结果显示,温室气温空间分布存在明显差异,湿帘-风机系统较自然通风降温效果显著,降温幅度在5℃左右,持续的湿帘-风机降温措施可将温室高温控制在较低水平。基于该文模型的预测结果和温室调控目标,选取合适的时间点、时间长度和不同类型的通风降温措施,可有效提高温室气温调控效率和效益。同时,该研究还可为优化传感器布局提供依据。

Hot and humid environment inside greenhouse in summer is a major threat to crop growth. How to make reasonable control schemes of greenhouse air temperature in summer is the key to improve the greenhouse production efficiency and reduce the energy consumption. In this study, we established a prediction model on temporal and spatial variation of air temperature in greenhouse based on the computational fluid dynamics（CFD） method and meteorology forecast information. The prediction model was adapted to the large multi-span plastic greenhouse in the southern regions of Jiangsu under the natural ventilation and fan-pad cooling system. It can predict the spatial and temporal distribution of greenhouse air temperature under different ventilation cooling conditions. The numerical computation of this prediction model was based theoretically on mass conservation equation, momentum conservation equation and energy conservation equation of fluid. The turbulence transfer was described by the turbulence model, and dealt with the near-wall region using the standard wall function. The DO（discrete ordinates） radiation model was used to simulate the radiation heat transfer of the greenhouse. The meteorology forecast information was mainly used for changing the boundary conditions of prediction model by introducing the influence of outdoor air temperature, the fan-pad cooling system and solar radiation to improve the energy conservation equation of the original model. Among them,the variable temperature boundary conditions under the natural ventilation condition were based on the changes of outdoor air temperature predicted values over time, while the variable temperature boundary conditions under the fan-pad cooling system were based on the changes of air temperature through the evaporative pad over time. Values of air temperature through the evaporative pad were calculated by the evaporative pad cooling model. The solar radiation values were calculated by Solar Ray Tracing Method. Variable external solar radiation boundary conditions can be set by setting each update interval of solar radiation calculation. In order to validate the effectiveness of the prediction model, the spatial and temporal distribution values of air temperature were measured at the key positions in the greenhouse and compared with the simulated data. The results showed that the predicted values matched the measured values. The root mean square error was within 1.2℃. The maximum relative error was less than 6%. The average relative error was within 4%. The model developed can accurately predict the spatial and temporal variation of indoor air temperature under different ventilation conditions. Experimental results under the different conditions of ventilation showed that the spatial distribution of indoor air temperature exited obvious gradient phenomenon. The air temperature rose with the increase of the height in the vertical direction and the direction of horizontal flow. It was low in the central region of the greenhouse while it was higher around the wall region. According to the results of unsteady-state calculation, the indoor air temperature had the same changing tendency with the outdoor air temperature during the whole prediction period time under the natural ventilation condition. The cooling effect of the fan-pad cooling system was better than the natural ventilation, the air temperature dropped 5℃ under the fan-pad cooling system, the indoor air temperature can be kept at a lower level by implementing continued fan-pad cooling measure. Based on the simulated results of the model and the target of greenhouse control, the high point of air temperature or the period time for rapid air temperature change were determined in this study. Following by this, appropriate point and length of time to implement different types of ventilation cooling measures were chosen for improving the efficiency of the greenhouse temperature control. This study can provide a reference for the proper implementation of ventilation control strategy and optimizing the layout of the sensors. Compared with the past research, the simulation model established in the paper can predict not only the spatial distribution of indoor air temperature under different ventilation cooling conditions, but also the temporal distribution of indoor air temperature for a long time. Thus, the calculation results of the model were able to provide the basis for greenhouse intelligent control. Further research is needed to optimize the parameter settings of the prediction model to improve the accuracy of computation results. As such, it can help to avoid the lagging response, passive control and inharmonious regulation in conventional control systems by combining the prediction results and intelligent optimization algorithms to reduce the greenhouse energy consumption.