Simulation And Parameter Optimization Of Vacuum Casting Degassing Process For Composite Solid Propellant Slurry

The vacuum casting degassing process of the composite solid propellant slurry directly affects the quality of the grain forming,and the structural integrity of the grain determines the reliability of the solid engine operation to some extent.One of the purposes of vacuum casting is to remove the air doped in the propellant slurry.At present,in optimizing the vacuum casting process parameters of composite solid propellant slurry,the method of process experiment is mainly used,which has both safety hazards and a lot of manpower,material resources,financial resources and time.In addition,the experimental methods have great limitations,and lack of theoretical guidance,it is difficult to reveal the influence of vacuum casting process parameters on the degassing effect of the pulp.Using the combination of simulation technology and experimentation to find the best vacuum casting degassing process parameters will have a multiplier effect.The viscosity and density of multiple sets of typical AP/Al/HTPB propellant slurries were determined.A deep learning neural network with multiple layers and multiple neurons,automatic extraction features,simple algorithm,low time consumption and high efficiency is constructed.The input parameters of deep learning neural network are propellant formula,and the output value is propellant slurry viscosity and density.The experiment data was used as the training set to obtain the propellant slurry viscosity and density prediction model based on the propellant formula,which realized the viscosity and density prediction of the slurry based on the propellant formula.The solid-liquid-gas three-phase flow of the actual composite solid propellant slurry is equivalent to the liquid-gas two-phase flow fluid.Based on Solid Works software,a simplified single-hole vacuum casting system solid model is constructed;ANSYS finite element analysis software and the Fluent module is used,the simulation calculation of the vacuum casting degassing process of the propellant slurry was realized,and the morphology and outflow characteristics of the propellant slurry in the vicinity of the flower plate outlet were analyzed.The effect of the vacuum casting process parameters（inlet pressure,relative vacuum in the chamber and the diameter of the single hole of the flower plate）on the degassing effect were numerically studied..A deep learning neural network based on the simulation results of the vacuum casting degassing process was established.The vacuum casting degassing simulation results were used as the training set,and the bubble removal height prediction model based on the vacuum casting process parameters was obtained.Using the model as the fitness function,a genetic algorithm was used to establish an optimization method for the process parameters of vacuum casting degassing under the condition of minimum bubble removal height in the slurry.On this basis,aiming at minimizing the height of bubble removal in the slurry,the optimal process parameters of the vacuum casting degassing process were obtained and verified by experiments.The results show that:1)The deep learning neural network model for predicting the viscosity and density of the slurry has a relative error below 7%between the predicted value and the measured value,indicating that the method can be used to predict the density and viscosity of the slurry based on the propellant formulation.2)The optimized vacuum casting process parameters are inlet pressure 0.24×10⁵Pa,chamber relative vacuum of-0.93×10⁵Pa,and flower plate single hole diameter6.29×10^-3m.The casting process parameters were experimentally verified and the expected results were achieved.