The microenvironment of the crop area in a greenhouse is the main factor that affects its growth, quality, and pest control. In this study, we propose a double-layer film solar greenhouse microenvironment testing system based on computational fluid dynamics simulations of a celery canopy with a porous medium. A real greenhouse was examined with a sensor system for soil, air, radiation, and carbon dioxide detection to verify the simulation results. By monitoring the internal environment of celery canopies with heights of 0.8 and 1 m during a period of temperature fluctuations, we found the temperature and humidity of the canopy interior changed spatially and differed greatly from the those in the greenhouse under solar radiation conditions. The temperature and humidity of the celery canopy were 4–14°C lower and 10%–30% higher than those of the surroundings. As the canopy grew, the differences in temperature and humidity between the canopy and other parts of the greenhouse increased. The root mean square errors of the temperature and humidity with the 0.8 m high celery canopy were found to be 0.56 and 2.86 during the day and 0.24 and 0.81 at night, respectively; the corresponding values for the 1 m high celery canopy were found to be 0.51 and 2.26 during the day and 0.26 and 0.78 at night. The porous medium model expressed the temperature and humidity characteristics of the celery crop appropriately, and the simulation method was shown to be effective and feasible. With the simulation method proposed in this study, the production of crops in complex microenvironments in greenhouses can be modeled and digitized.

中文翻译：

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基于计算流体动力学的双膜日光温室作物冠层温湿度模拟

温室中作物区域的微环境是影响其生长，质量和害虫控制的主要因素。在这项研究中，我们基于具有多孔介质的芹菜冠层的计算流体动力学模拟，提出了一种双层薄膜日光温室微环境测试系统。使用用于土壤，空气，辐射和二氧化碳检测的传感器系统检查了真实的温室，以验证模拟结果。通过在温度波动期间监测高度为0.8和1 m的芹菜冠层的内部环境，我们发现冠层内部的温度和湿度在空间上发生了变化，并且与日光照射条件下温室中的温度和湿度有很大差异。芹菜冠层的温度和湿度比周围环境低4–14°C，比周围高10％–30％。随着冠层的生长，冠层与温室其他部分之间的温度和湿度差异也增加。高度为0.8 m的芹菜冠层，温度和湿度的均方根误差在白天和晚上分别为0.56和2.86和晚上为0.24和0.81；白天，高1 m的芹菜冠层的相应值为0.51和2.26，晚上则为0.26和0.78。多孔介质模型恰当地表达了芹菜的温度和湿度特性，表明该方法是有效可行的。利用本研究提出的仿真方法，