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Parametric Modal Analysis And Structural Optimization Of Railway Train Bogie

Posted on:2021-05-24Degree:MasterType:Thesis
Country:ChinaCandidate:M F KeFull Text:PDF
GTID:2392330611480511Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
The safe and stable operation of a rail train on the track is the first and top priority for train designers.However,due to the harsh operating environment and the complex structure of the locomotive itself,it will be subjected to loads in all directions during operation,which will cause the train to vibrate violently and cause great harm.The bogie of a train is a moving part of the vehicle,and its vibration characteristics directly affect the safety,stability and comfort of passengers.This paper takes a certain type of bogie frame as the research object and studies the dynamic characteristics of bogie frame based on ANSYS Parametric Design Language(APDL).The main research contents and results of this article are shown below:(1)Based on the ANSYS parametric design language(APDL),this paper selects the wall thickness of the transverse beam,the distance between the transverse beams,the diameter of the transverse beam,the thickness of the side beam cover,the thickness of the side beam,the distance between the longitudinal beams and the three-way stiffness of the primary spring system Parameters to establish a parametric model of the bogie frame.The free modal analysis and constrained modal analysis of the framework are performed to obtain the natural frequency and mode shape of the framework in two cases.Analyze the characteristics of the modal shapes of the bogie frame: the 7th to 10th-order modes of the obtained frame have a large deformation and the natural frequency is close to the power frequency or double frequency,which has a greater impact on the driving safety of the train and is optimized for the subsequent dynamic characteristics of the frame Provide theoretical basis.(2)The modal sensitivity analysis and orthogonal simulation test are used to analyze the factors affecting the dynamic characteristics of the framework.Using ANSYS Probability Design Module(PDS),using Monte Carlo probability design method and Latin hypercube sampling method,200 simulation cycles were performed to obtain more accurate results of the modal sensitivity analysis of the framework.For each of the 9 structural parameters of the framework,4 levels were selected and orthogonal simulation tests were performed.Through the analysis of variance and range of the test data,the importance order of the factors affecting the modal of the framework is obtained.Combined with the sensitivity analysis of the frame and the results of the orthogonal simulation test,the key structural parameters that have a greater impact on the 7th to 10 th mode of the frame are determined: the thickness of the transverse beam wall,the diameter of the transverse beam,the thickness of the side beam cover,and the thickness of the side beam riser Three-way stiffness with a series of spring systems.(3)The multi-objective optimization problem of the 7?10th-order modal frequency optimization of the framework is transformed into a single-objective optimization problem by using the linear weighting method,and an optimization objective function containing the 7-10th-order modal frequency of the framework is established,and the initial mass of the framework is used as a constraint Conditions,key structural parameters of the framework are design variables.Based on the ANSYS optimization module(OPT),a zero-order optimization algorithm is used,and the results are converged after 12 optimization iterations.With reference to the strength checking method provided by the International Railway Union UIC615-4,the static strength and fatigue strength checking of the optimized bogie frame is performed.After the optimization,the modal frequencies of the 7th to 10 th order frames are increased by an average of 17.4%,the mass of the frame is reduced by 5%.(4)Using the multi-disciplinary optimization software isight integrated finite element analysis software ANSYS,the second-generation non-dominated sorting multi-objective optimization genetic algorithm(NSGA-II)is adopted,with the framework's 7th to 10 th order modal frequencies being the largest and the framework quality being the smallest as the objective function.In the strength analysis,the equivalent stress of the frame under static load analysis is less than its allowable stress as a constraint condition.The key structural parameters of the 7th to 10 th mode of the frame are design variables.After 20 generations(ANSYS 240 batch operations),get the Pareto solution set of the 7th to 10 th order modal and the quality of the framework,select one of the non-dominated solutions,which corresponds to the optimization result.The quality of the framework is reduced by 3.6%,and the 7th to 10 th order modal frequency increases by an average of 17.2% and the result meets the strength requirements.The research results and methods provide references for the improvement of the dynamic characteristics of the framework.(5)Two optimization methods are used to optimize the dynamic characteristics of the framework.The linear weighting method converts the multi-objective optimization problem into a single-objective optimization problem.This method has high optimization efficiency and easy operation,but the choice of weight depends on the subjective judgment of the person,and the choice of weight is different,the optimization results are also different.The second-generation non-dominated sorting genetic algorithm(NSGA-?)has a wide range of applications for solving multi-objective optimization problems.The optimization results include optimal solutions under various weights,providing designers with more design choice space.The designer can choose the appropriate optimal solution according to the design requirements,and its optimization method is more scientific.
Keywords/Search Tags:Bogie frame, APDL, Modal analysis, Sensitivity analysis, Orthogonal simulation test, Linear weighting method, Isight, NSGA-? Algorithm, Multi-objective optimization
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