Research On The Structure Of Jet Vortex Spinning Nozzle Based On The Coupling Of Airflow And Fiber

Jet vortex spinning is based on a special nozzle structure,through the rational use of gas flow to complete the yam twist new spinning technology.The nozzle is a key component of twisted yarn.The structure design and process parameters of the nozzle have an important influence on the spinning quality.At present,the research methods of domestic and foreign scholars are mainly spinning experiments and numerical simulation.In this paper,the numerical simulation method is used to study the yarn spinning process of jet vortex spinning.According to the quantitative numerical simulation results,the nozzle structure parameters and process parameters are optimized.In this paper,the jet vortex spinning nozzle structure and numerical simulation of two aspects,the domestic and foreign scholars on the status of a more comprehensive summary.At present,scholars generally consider the numerical simulation results considering the flow field separately to optimize several important nozzle structures(distance from the front roller holding point to the hollow spindle;pilot body structure;air injection hole structure;hollow spindle structure;vortex chamber structure Etc.),the optimization methods are mostly intuitive comparative analysis and calculation results.In view of the current research status,this paper proposed the establishment of numerical coupling method of three-dimensional airflow and fiber coupling,the use of neural networks and genetic algorithms to optimize the research objectives.In the early stage of the research,the simulation of the flow field was the basis of further complex analysis.First,the calculation and analysis of the flow field simulation were carried out.By examining the numerical calculation of the indicators and the analysis and calculation results,the rationality of the selected numerical model of flow field is verified to provide more accurate initial flow field data and calculation model for numerical simulation of airflow and fiber coupling in the next step.According to the yarn spinning mechanism of jet vortex spinning,some important nozzle structure parameters and the influence of process parameters on the yarn formation are analyzed by numerical results.In order to further realize the numerical simulation of airflow and fiber bi-directional coupling,a three-dimensional finite element model of fiber was established and the physical parameters such as density and elastic modulus of fiber were established.The dynamic model of the coupling between airflow and fiber was established.To the problem of solid-to-solid contact.By using arbitrary Lagrange Eulerian method,the coupled calculation of air flow and fiber is realized by moving mesh technology.The numerical simulation results of jet spinning vortex spinning mechanism are analyzed to determine the optimal variables and quantification After the optimization goal.Due to the complexity of the relationship between the optimization variables and the optimization objective,it is impossible to express their function relations with the deterministic equations.In this paper,BP neural network is used to build the model.Because there are few sample parameters for BP network training,BP neural network optimized based on genetic algorithm is adopted in this paper.By optimizing the initial full-time and threshold,the calculation results are deterministic and more accurate.After carrying out the single objective optimization of the trained BP network through genetic algorithm,we found that there exists a non-inferior solution relationship between the optimization variables,and then use the multi-objective optimization algorithm based on genetic algorithm to obtain the front-end solution space,and get a better optimization Variables,ie nozzle configuration and process parameters.Finally,this paper summarizes the results of the research and the results obtained,summarizes the deficiencies in the study and the areas to be improved and developed,and looks forward to the future research direction.