データ同化と数値流体解析を用いた太陽光型植物工場における細霧冷却効果の予測

抄録

In recent years, the use of plant factories has been attracting attention as a type of agricultural form. There are two types of plant factories, artificial light type, and sunlight type. The sunlight type has the advantage of using the sun and other natural resources, which reduces costs. The author controls the Vapor Pressure Deficit (VPD) using the fog cooling system in sunlight type plant factory. This is because controlling VPD can promote plant growth. The current problem is that the control model of the fog cooling system has not been clarified. The sunlight type is controlled in a semi-closed environment, so it is difficult to clarify the control model by measurement. In addition, since it is practically impossible to understand and model all the conditions that affect the fog cooling effect, numerical methods are also difficult. Therefore, we propose a method to reveal the control model using data assimilation. Data assimilation is the statistical combination of observed values and numerical results to obtain more plausible values close to the true value. The purpose of this study was to investigate whether the fog cooling effect can be predicted under the assumed conditions by using data assimilation. If predictions can be made, the control model can be estimated using past observation data and numerical analysis. As a result, we were able to predict the fog cooling effect on days with meteorological conditions close to those on which the observed data were obtained.