Prediction the Spatial Air Temperature Distribution of an Experimental Greenhouse Using Geostatistical Methods

Concerning the greenhouse environment, the ultimate goal of an investigation would be to determine the climatic parameters for all locations in the study area. Objective of the present study is to analyse the distribution of air temperature and air velocity of an experimental greenhouse with tomato crop, equipped with fan and pad evaporative cooling system, using geostatistical methods. The main aspects of geostatistics in terms of theoretical background for understanding spatial correlation models and kriging applications are presented. The most common variogram models were fitted to the experimental data sets obtained during summer period from an experimental greenhouse equipped with fan and pad evaporative cooling system. The Kriging approach was applied using the semivariograms corresponded to these data sets. Finally, the prediction maps of air temperature and air velocity were produced in different height levels inside the tomato crop canopy showing a great variability. Geostatistic analysis may be applied to determine not just optimal spatial predictions but also probabilities associated with risk-based analysis in order to improve the suitability and efficiency of climatic controls systems in greenhouses

Numerical and Experimental Study of Fan and Pad Evaporative Cooling System in a Greenhouse with Tomato Crop

An experimental greenhouse equipped with fan and pad evaporative cooling is simulated numerically using a commercial CFD code. The main aspects of evaporative cooling systems, in terms of heat and mass transfer and both the external and internal climatic conditions were integrated to set up the numerical model. The crop (tomato) was simulated using the equivalent porous medium approach by the addition of a momentum and energy source term. Preliminary calculations were carried out and validated by experimental measurements, in order the pressure drop occurred in crop model due to air flow, to be determined as a function of leaf area, stage of crop growth and cultivation technique. The temperature and humidity of incoming air and the operational characteristics of exhaust fans were specified to set up the CFD model. The numerical analysis was based on the Reynolds-averaged Navier-Stokes equations in conjunction with the RNG k- turbulence model. The finite-volume method (FVM) was used to solve the governing equations. The 3D full scale model was solved in several differencing schemes of various orders in order to examine its accuracy. The simulation results were validated with experimental measurements obtained at a height level of 1.2 m above the ground in the middle of the crop canopy at 23 and 8 points, concerning air temperature and air humidity respectively. The correlation coefficient between computational results and experimental data was at the order of 0.7419 for air temperature and 0.8082 for air relative humidity. The results showing that the evaporative cooling system for greenhouses could be effectively parameterized in numerical terms, providing a useful tool in order to improve system’s efficiency

Fan and pad evaporative cooling system for greenhouses: Evaluation of a numerical and analytical model

An experimental greenhouse equipped with fan and pad evaporative cooling is analysed using two different models. The first one consists of a numerical simulation approach applying a commercial CFD code. The main aspects of evaporative cooling systems, in terms of heat and mass transfer and both the external and internal climatic conditions were integrated to set up the numerical model. The crop (tomato) was simulated using the equivalent porous medium approach by the addition of a momentum and energy source term. The temperature and humidity of incoming air, the operational characteristics of exhaust fans and the pressure drop occuring in the pad, were specified to set up the CFD model. The second model considers the greenhouse as a heat exchanger. Based on greenhouse structural characteristics, external climatic conditions, pad efficiency and ventilation rate, the air temperature distribution is predicted. The results, concerning the air temperature, provided both by numerical and analytical model, were validated by experimental measurements obtained at a height level of 1.2 m above the ground in the middle of the crop canopy. The correlation coefficient (R2) between computational results and experimental data was at the order of 0.96 for the numerical model and 0.77 for the analytical one, with average percentage error of 3.5% and 7.6%, respectively. The analytical model proved to be a useful simple evaluation tool, but the numerical one provides a more accurate overview of the air flow in the greenhouse showing that fan and pad evaporative cooling system could be effectively parameterized in numerical terms, in order to improve system's efficiency

Analysis of airflow through experimental rural buildings: Sensitivity to turbulence models

Full-scale experimental data and computational fluid dynamics (CFD) methods are used to determine the accuracy of four different turbulence models [standard k-ε, k-ε renormalisation group (RNG), k-ε realisable, Reynolds stress model (RSM)], which are used to describe the turbulent part of air in problems concerning the natural ventilation of buildings. Ventilation rates were measured in a livestock building using the decay tracer gas (CO2) technique. Airflow and temperature patterns were mapped out in a greenhouse with a tomato crop using a three-dimensional sonic anemometer and a fast-response temperature sensor. A commercially available CFD code was used to evaluate the different turbulence models. Average values from experiments were used for boundary conditions. The numerical results are compared with the experimental data, and they showed a good agreement, especially when the k-ε RNG turbulence model was used. The computations of the flow field using the different turbulence models showed noticeable differences for computed ventilation rate, air velocity and air temperature confirming the importance of the choice of the closure model for turbulence modelling. © 2007 IAgrE