Temperature Field-Wind Velocity Field Optimum Control of Greenhouse Environment Based on CFD Model

The computational fluid dynamics technology is applied as the environmental control model, which can include the greenhouse space. Basic environmental factors are set to be the control objects, the field information is achieved via the division of layers by height, and numerical characteristics of each layer are used to describe the field information. Under the natural ventilation condition, real-time requirements, energy consumption, and distribution difference are selected as index functions. The optimization algorithm of adaptive simulated annealing is used to obtain optimal control outputs. A comparison with full-open ventilation shows that the whole index can be reduced at 44.21% and found that a certain mutual exclusiveness exists between the temperature and velocity field in the optimal course. All the results indicate that the application of CFD model has great advantages to improve the control accuracy of greenhouse.

[1]  Baoming Li,et al.  Numerical modelling of temperature variations in a Chinese solar greenhouse , 2009 .

[2]  N. Bennis,et al.  Greenhouse climate modelling and robust control , 2008 .

[3]  Thierry Boulard,et al.  Measurement and CFD simulation of microclimate characteristics and transpiration of an Impatiens pot plant crop in a greenhouse , 2012 .

[4]  Savvas A. Tassou,et al.  Effectiveness of CFD simulation for the performance prediction of phase change building boards in the thermal environment control of indoor spaces , 2013 .

[5]  Abdelkader Mami,et al.  Pseudo bond graph model of coupled heat and mass transfers in a plastic tunnel greenhouse , 2010, Simul. Model. Pract. Theory.

[6]  Diego L. Valera,et al.  Sonic anemometry to evaluate airflow characteristics and temperature distribution in empty Mediterranean greenhouses equipped with pad–fan and fog systems , 2012 .

[7]  Ignacio E. Grossmann,et al.  Computers and Chemical Engineering , 2014 .

[8]  Shengwei Wang,et al.  A CFD-based test method for control of indoor environment and space ventilation , 2010 .

[9]  Louis D. Albright,et al.  Application of pseudo-derivative-feedback algorithm in greenhouse air temperature control , 2000 .

[10]  Thierry Boulard,et al.  AIRFLOW AND MICROCLIMATE PATTERNS IN A ONE-HECTARE CANARY TYPE GREENHOUSE: AN EXPERIMENTAL AND CFD ASSISTED STUDY , 2008 .

[11]  Juan Ignacio Montero,et al.  A CFD greenhouse night-time condensation model , 2012 .

[12]  Limi Okushima,et al.  A SUPPORT SYSTEM FOR NATURAL VENTILATION DESIGN OF GREENHOUSES BASED ON COMPUTATIONAL AERODYNAMICS , 1989 .

[13]  C. Moresoli,et al.  Optimal control and CFD modeling for heat flux estimation of a baking process , 2012, Comput. Chem. Eng..

[14]  J. C. Roy,et al.  Dynamic simulation of the distributed radiative and convective climate within a cropped greenhouse , 2012 .

[15]  Carlos Ricardo Bojacá,et al.  Original papers: Use of geostatistical and crop growth modelling to assess the variability of greenhouse tomato yield caused by spatial temperature variations , 2009 .

[16]  Jung Eek Son,et al.  3-D CFD analysis of relative humidity distribution in greenhouse with a fog cooling system and refrigerative dehumidifiers , 2008 .

[17]  Ryozo Ooka,et al.  Optimum design for smoke-control system in buildings considering robustness using CFD and Genetic Algorithms , 2009 .

[18]  Philip Davies,et al.  The seawater greenhouse in the United Arab Emirates: Thermal modelling and evaluation of design options , 2005 .

[19]  Fathi Fourati,et al.  A greenhouse control with feed-forward and recurrent neural networks , 2007, Simul. Model. Pract. Theory.