Optimization of Radiative Heat Transfer inside Greenhouses

This paper presents a multi-objective procedure based on a fully elitist genetic algorithm, GA, able to optimize the radiative heat transfer inside greenhouses used to grow crops. Details of the genetic algorithm used is presented herein but the focus is on the heat transfer. The decision variables are the tube temperature and tube radius, that affects directly the entalphy heating flow, and the tube location. In some studies, the cavity aspect ratio (height to width) is also considered as another decision variable. The influence of the genetic algorithm parameters, such as the size of the initial and following generations, and heat transfer parameters such as cavity aspect ratio and the surface emissivities are discussed herein. A new filter, based on a local gradient, is introduced in order to generate more efficient populations. The results show clearly that with proper consideration, very good results may be obtained with reasonable computational effort.