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Abstract Background: Climate change is increasing the vulnerability of horticultural crop cultivation and production. It is urgent to study such extreme weather phenomena (heatwave, drought, etc.), and in particular, to evaluate crop productivity according to temperature change. For this purpose, the crop physiological response to temperature change in simulated weather conditions was studied. However, there is a limitation in artificial light wavelength, which requires experiments to be carried out in protected facilities or open fields. In this study, we simulated temperature differences with computational fluid dynamics (CFD) in tunnel-type greenhouses. They can create temperature gradients and improve the accuracy of CFD with vertically and horizontally measured temperature profiles. The growth and physiological response of Kimchi cabbage were examined and validated using a temperature gradient within a semi-closed plastic tunnel.Results: Correlation coefficients of measured heights were: 1.120, 0.597, and 0.459. Root mean square error was below 0.1025, which means the CFD simulation values were highly accurate. The error analysis showed that it was possible to accurately predict temperature gradients change within a semi-closed tunnel-type greenhouse using CFD techniques. CFD results showed an average error of 0.597°C compared to field monitoring results. The maximum temperature difference of GTG was 5.7°C, suggesting a well-controlled set point (6°C difference between outside conditions and inside conditions of GTG). In a cloudy day, the gradient temperature of GTG was well maintained by the set differential temperature (dT), which suggests that the set dT was not precisely and accurately performed in GTG of a sunny day. There was a significant difference in the growth, net photosynthetic rate, transpiration rate, and Ci concentration along with temperature differences in GTG. Conclusions: CFD can simulate temperature gradient distribution in a tunnel-type greenhouse and predict the temperature difference for equipment with different specifications. These facilities can be used in climate change-related studies, such as assessment of crop production area optimization, crop physiological response to temperature, vulnerability assessment of crop production under increasing temperatures, or extreme weather.

Background: Climate change is increasing the vulnerability of horticultural crop cultivation and production. It is urgent to study such extreme weather phenomena (heatwave, drought, etc.), and in particular, to evaluate crop productivity according to temperature change. For this purpose, the crop physiological response to temperature change in simulated weather conditions was studied. However, there is a limitation in artificial light wavelength, which requires experiments to be carried out in protected facilities or open fields. In this study, we simulated temperature differences with computational fluid dynamics (CFD) in tunnel-type greenhouses. They can create temperature gradients and improve the accuracy of CFD with vertically and horizontally measured temperature profiles. The growth and physiological response of Kimchi cabbage were examined and validated using a temperature gradient within a semi-closed plastic tunnel.Results: Correlation coefficients of measured heights were: 1.120, 0.597, and 0.459. Root mean square error was below 0.1025, which means the CFD simulation values were highly accurate. The error analysis showed that it was possible to accurately predict temperature gradients change within a semi-closed tunnel-type greenhouse using CFD techniques. CFD results showed an average error of 0.597°C compared to field monitoring results. The maximum temperature difference of GTG was 5.7°C, suggesting a well-controlled set point (6°C difference between outside conditions and inside conditions of GTG). In a cloudy day, the gradient temperature of GTG was well maintained by the set differential temperature (dT), which suggests that the set dT was not precisely and accurately performed in GTG of a sunny day. There was a significant difference in the growth, net photosynthetic rate, transpiration rate, and Ci concentration along with temperature differences in GTG.Conclusions: CFD can simulate temperature gradient distribution in a tunnel-type greenhouse and predict the temperature difference for equipment with different specifications. These facilities can be used in climate change-related studies, such as assessment of crop production area optimization, crop physiological response to temperature, vulnerability assessment of crop production under increasing temperatures, or extreme weather.