Study On The Fission Gas Release Mechanism And Inverse Method In Pressurized Water Reactor

During the reactor operation,large amounts of fission products are generated in the fuel pellet.The fuel cladding,which contains the fission products,may be defective under influence of GTRF and debris.The radioactive fission products release through the defect site and cause the increase of coolant activity.Even the fission products release into the containment and the environment,which will increase the risk of exposure for public people.Thus,there are strict laws to determine whether the reactor can operate when the cladding is defective.If the defect size is larger than the permissive value,the reactor must be shut down in a given time.Otherwise,the reactor can keep on operation.Wrongly stopping or keeping on operation will harm the economical efficiency or safety.Then it is necessary to study the fission products release mechanisms,especially fission gas,develop the method for fuel failure detection.In this paper,the research is aimed at physical process of fission gas release and fuel failure detection when the fuel cladding is defective.The fission gas release mechanisms are studied by numerical simulation and experiment.The influence of some parameters,such as defect size,temperature,pressure,and so on are studied.Based on the fission gas release model,a fuel failure detection method using neural network is proposed.The main research contents and conclusions are the following aspects:(1)Study on the calculation method of fission products release from fuel pellet to coolant.According to the fission products release models from pellet to gap and from gap to coolant,the calculation method of coolant activity contributed by the fission products is established.(2)Numerical simulation study on the fission gas release.Fission gas release from the gap to the coolant after the cladding rupture is studied by the CFD method.The fission gas release process is obtained,including the jet release during the burst release stage and the entrained release by the vortex during pressure equilibrium stage.It is found that the vapor generation rate in the gap is the most important factor that affecting the fission gas release rate through the sensitivity analysis of the fission gas release process.(3)Experimental study on the fission gas release.The effects of defect size,loop temperature,loop pressure on gas release are studied by the scaled experiment and the visualization experiment.The scaled experiment finds that two-phase critical flow may occur during the gap pressure increase process,during which the loop pressure has no effect on the gap pressure increase.The gas release conforms to the first-order kinetic process,and the defect size significantly affect the escape rate coefficient.The visualization experiment finds that gas release is a periodical process.Comparing the experimental results with the numerical simulation,they agree well with each other.(4)Develop a fission gas escape rate coefficient model.Based on the simulation and experimental results,the fission gas release process is a first-order process according to the model.A mechanistic model is proposed to calculate the escape rate coefficient of fission gas.(5)Inverse method for fuel failure status detection.A fuel failure detection method is developed based on neural network.Comparing with the traditional isotopic ratio method,the neural network method has higher accuracy and faster response,also can resist the interference of input errors.In summary,this paper studies the physical process of fission gas release after fuel cladding is defective and the inverse method for fuel failure status detection.Through the numerical simulation and experimental investigation of fission gas release process under cladding defect condition are performed,which reveals the fission gas release mechanisms and acquire the critical parameters that affecting the fission gas release rate.Then based on fission gas release mechanism,escape rate coefficient model is established.Finally,a fuel failure status detection method is established based on neural network.The results of this paper can support for online monitoring of fuel rupture.