Research On Temperature Homogeneity Optimization Of Edible Fungus Factory Based On CFD Model

The artificial environment of mushroom house in edible fungus factory is the key factor in affecting the yield and quality of mushroom,and the temperature uniformity in mushroom house is an important indicator of environmental parameters.The traditional environmental control of mushroom house adjusts the artificial environment based on the environmental parameters of a certain point in the mushroom house.Although the implementation is simple and the control precision is also constantly improved,it is difficult to achieve the same environmental parameters in the entire area of the mushroom house,which can affect the mushroom quality and yield.This paper takes a single mushroom house in the edible fungus factory as the research object,and establishes a model by combining computational fluid dynamics(CFD)numerical simulation with neural network.The model is optimized by genetic algorithm to obtain a set of design parameters which make the temperature uniformity index in mushroom house best.This provides a reference scheme for reforming the control equipment and adjusting the control parameters to improve the temperature uniformity of mushroom house.Firstly,based on the theory of computational fluid dynamics,the control equations and corresponding resolution methods of the mushroom house are determined.The temperature uniformity index is proposed and the influencing factors of the temperature distribution are discussed.Design variables are determined as the distance between the air supply port and the top of the mushroom house,the air supply temperature,the air supply velocity and the air supply angle.Secondly,the three-dimensional steady-state CFD model of mushroom house is established.The numerical simulation of temperature field in mushroom house is carried out by using standard k-? turbulence model and SIMPLEC algorithm.The monitoring system of temperature and humidity is constructed combining ZigBee wireless transmission technology and Labview acquisition platform to achieve the real-time monitoring of temperature and humidity of multi-sensor nodes in mushroom house.The simulation results are verified by comparison with the experimental data.Thirdly,on the basis of analyzing the influence trend of single design variable on the temperature uniformity of mushroom house,the orthogonal test method is used to design the different simulation conditions of four design variables.The data of temperature uniformity of mushroom house under all working conditions are obtained by CFD numerical simulation.In order to describe the relationship between temperature uniformity and design variables,the group method of data handling(GMDH)type neural network model is established by using 72 sets of working conditions and the corresponding temperature uniformity index data.The best combination of design variables is obtained by genetic algorithm optimization.The comparative analysis shows that the temperature uniformity index of mushroom house is increased by 21.2% and the distribution uniformity of temperature field is significantly improved after optimization of working conditions.