Numerical Simulation Of Gas-solid Flow Characteristics In Dense Phase Zone Of Fluidized Bed And Research On Inverse Problem Of Bed Temperature

The furnace temperature of the fluidized bed is a key factor related to the safe and stable operation of the fluidized bed boiler.Too high bed temperature can easily lead to coking and affect the safe and stable operation of the boiler.In the actual operation of the fluidized bed boiler,there are often cases where the temperature in most areas is normal but in local areas is abnormal.At this time,it is difficult to reflect the true temperature field of the bed through limited measurement information,and it is impossible to judge the occurrence of the areas where the temperature is too high.The location of the abnormal area makes it impossible to adjust the operating status of the boiler in a targeted manner.Therefore,finding a method that can determine the location of the local temperature abnormal area of the bed through limited measurement information is very important to better control the operating state of the fluidized bed boiler.In this paper,the software FLUENT is used to simulate the flow and combustion in the fluidized bed,and based on the temperature field obtained from the combustion simulation,this paper uses the genetic algorithm to explore the method of determining the location of the local temperature abnormal area of the bed with limited measurement information.Firstly,numerical simulation of the particle flow in the fluidized bed is carried out.The Euler-Euler two-fluid model is used to simulate the particle flow in the dense phase area of the fluidized bed.A small amount of discrete particles(the proportion of which can be ignored relative to the Euler phase particles)is added as a tracer particle to the bed through the DPM model.The trajectory of the particles in the bed is tracked,and the main factors that affect the particle flow in the dense phase zone of the fluidized bed,such as fluidizing gas velocity,jet velocity,and the height of jet orifice,are explored.The results show that for the fluidized bed studied in this subject,the increase in jet velocity will cause the accumulation of coarse particles on the wall and corners to become serious.This phenomenon can be reduced by increasing the height of the jet nozzle;however,the position of the nozzle is too high will shorten the time the fuel stay in the bed and cause the combustion is insufficient;in addition,through the analysis of the movement trajectory of the discrete particles,it is found that the particles with a diameter of 0.1mm are not directly carried out by the main air flow after being added to the furnace,it will move in the bed for a period of time under the action of the flow field,which will make the particles of small particle size stay in the bed for a longer time,and is conducive to a more complete reaction when the particles are burned.Select appropriate parameters according to the particle flow simulation,and perform combustion simulation on the fluidized bed to obtain the temperature field.Through reasonable simplification,the problem of finding the location of the local temperature abnormal area of the bed is transformed into the inverse problem of heat source's location searching,and the genetic algorithm is used to solve this problem.the applicability and reliability of the genetic algorithm in the inverse problem is verified firstly;then a numerical calculation method for the inverse problem of heat source's location searching is established;the program of inverse heat conduction problem program is composed with Python language and verify the system's reliability and accuracy is verified by calculated a example.To provide guidance on the selection of the number of measuring points and the placement of measuring points during the performance,this paper explored the influence of the number of measurement points,measurement error and measurement point distribution on the accuracy of the inversion results when using genetic algorithm to invert the location of the internal heat source.Finally,according to the investigation results of the number of measuring points,measuring error and measuring point distribution on the accuracy of the inversion results,the appropriate number of measuring points and measuring point positions are selected from the temperature field obtained by the combustion simulation,and the genetic algorithm is used to determine the location of the local temperature anomaly are;the bed temperature is reconstructed and compared with the fluent simulation result.It is found that the temperature field distribution reconstructed by the inversion is similar to the temperature field distribution obtained by fluent simulation,and the location of the high temperature region is also close.The results prove the effectiveness of genetic algorithm in bed temperature reconstruction and finding the location of local temperature anomalies in the bed.