CN117150612A - Rail grinding reference profile design method based on multi-objective optimization - Google Patents
Rail grinding reference profile design method based on multi-objective optimization Download PDFInfo
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Abstract
The invention discloses a rail grinding reference profile design method based on multi-objective optimization, which relates to the technical field of rail profile design. The rail polishing reference profile design method provided by the invention is based on the multi-objective optimized reference profile design, can comprehensively consider factors such as vehicle body dynamics performance, vehicle body stability, wheel-rail contact force, rail abrasion and the like, ensures the safety and comfort of a line, simultaneously reduces the field polishing workload as much as possible, and prolongs the service life of the rail. The whole steel rail profile detection and design process realizes full automation without a large amount of field manual work.
Description
Technical Field
The invention relates to the technical field of steel rail profile design, in particular to a steel rail polishing reference profile design method based on multi-objective optimization.
Background
Abnormal abrasion of the wheel rail is one of important diseases of a railway curve rail, and causes the service life of the rail to be reduced, the maintenance cost of the rail and the wheel is increased, and even the safety and the comfort of the running of a vehicle are affected. The good profile matching of the wheel rail is obtained, so that the contact geometric relationship of the wheel rail can be effectively improved, the adverse abrasion between the wheel rails is reduced, and the contact stress of the wheel rail is reduced.
Many numerical studies on optimization of the contour of the vehicle are being carried out in China, and with the progress of time, students are continually striving to find reasonable wheel treads so as to design the treads to achieve the set targets. In 2000, the design method of the high-order curve abrasion tread is provided by the Liuzhujun on the basis of Chebyshev value approximation method. The new tread optimized by the method can improve the curve passing performance and ensure the stability in a safe range. 2002. Shen Gang and She Zhisen have proposed a wheel tread optimization method based on the reverse of the contact angle curve. And obtaining a desired contact angle curve through a minimum distance searching method, and expanding the partially optimized new profile according to the original profile, so as to obtain a new tread meeting the contact characteristic. After that, two persons develop the method to the optimal design of the independent wheel set of the low-floor vehicle, and the influence of different contact angle curves on the profile of the vehicle is analyzed. After years Shen Gang and Zhong Xiaobo have also proposed a method of optimizing the tread of a wheel that targets the wheel diameter difference and the contact angle difference as a function of the amount of wheel set traverse. The result shows that the algorithm based on the wheel diameter difference function is suitable for tread appearance design of the rigid wheel set, and the algorithm based on the contact angle difference is suitable for tread appearance design of the independent wheel set. 2008. In the years, zhang Jian and the like try to select a proper region of the profile of the steel rail to expand to optimize the tread of the wheel, and the tread of the wheel after optimization can effectively reduce the contact stress.
The optimization research of the vehicle profile should involve various factors such as vehicle dynamics, wheel track contact geometry, wheel track material characteristics and the like, so that partial scholars in the last ten years adopt a multi-target optimization design method. 2011. In the year, the secondary-secondary set proposes a multi-objective numerical solution method based on Gaussian radial basis function response surfaces by taking a plurality of circular arc center coordinates and radiuses as design variables, determining a plurality of optimized dynamic indexes as a plurality of optimized targets according to different vehicle types and lines, taking the aspects of wheel geometric shapes, vehicle running safety and stability, material strength requirements and the like into consideration as constraint conditions, optimizing the wheel treads of CRH2 high-speed motor train units and HXD2 electric locomotives, and obtaining expected target effects. In 2016, cui Meng established a tread optimization model using a quadratic rational Bezier curve description method. The nonlinear critical speed and the abrasion index are taken as optimization targets, derailment coefficients, lateral forces of wheel tracks and vertical forces of wheel tracks are taken as constraint conditions, the generated control point coordinates are taken as design variables, and the optimal wheel tread is obtained based on a NSGA-II genetic algorithm and an Isight Pareto multi-target optimization solving method.
In summary, many studies on optimization of the profile of the wheel are focused in China, and methods proposed for different vehicle types and line conditions are different, wherein the methods comprise a reverse solution method based on contact characteristics of the wheel, such as a contact angle curve reverse-push method, a rolling circle radius difference reverse-push method and an equivalent taper curve reverse-push method. And a rail profile expansion method, a rail normal clearance inverse method and the like for achieving the desired rail contact conformal. In addition, in recent years, there are many wheel profile design methods using different dynamic indexes of a vehicle as a plurality of optimization targets, but the existing multi-target optimization lacks knowledge of mutual influence between sub-targets, so that it is difficult to ensure that the design result is globally optimal, and further intensive research is needed.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention discloses a rail grinding reference profile design method based on multi-objective optimization.
In order to achieve the above purpose, the present invention adopts the technical scheme that:
a rail grinding reference profile design method based on multi-objective optimization comprises the following steps:
s1, researching the basic information of a line and a vehicle, and establishing a vehicle dynamics model according to the research result;
preferably, in the step S1, the investigated line information includes a rail type, a curve radius, and a rail bottom slope breaking; the basic information of the investigated vehicles comprises the types of wheel treads, locomotive and carriage models, a vehicle body structure, a bogie structure and a shock absorption structure.
In the steps, the purpose of establishing the vehicle dynamics model is to simulate the running conditions of the vehicle under different profile conditions, so as to calculate the derailment coefficient, the comfort, the wheel track contact stress and the like, and the vehicle dynamics model is established by adopting SIMPACK multi-body dynamics simulation software.
S2, detecting the profile of the steel rail of the investigated line;
in the above steps, the steel rail profile detection means that the full section profile data of the steel rail is detected, the steel rail profile detection is carried out by adopting a minirof steel rail profile instrument, the purpose of carrying out steel rail profile detection on an investigated line is to obtain the current situation of the steel rail profile, and the abrasion condition of the steel rail profile is known so as to design the steel rail profile more in line with site conditions.
S3, screening and fusing the detected rail profile to obtain a representative profile of the section of line;
preferably, the step S3 includes the steps of:
s31, comparing the detected steel rail profile with a standard steel rail profile, and screening out a profile with profile points of 400-1000 and steel rail abrasion less than 2 mm;
s32, carrying out average fusion on all the screened profiles to obtain the representative profile of the section of line.
In the steps, firstly, the detected steel rail profile is compared with the standard steel rail profile, and only the profile with the number of profile points being less than 1000 and more than 400 and the steel rail abrasion being less than 2mm is reserved. And then carrying out average fusion on all the retained profiles, thereby obtaining the representative profile of the section.
For the representative profile of the steel rail, the existing mode at present collects one to two steel rail profiles to represent the profile of the whole line according to manual experience, and the mode has high randomness and cannot represent the whole line condition. Because of certain difference of the rail profiles of each line, in order to obtain a more representative rail profile, the invention needs to acquire the line profile in a high density, then screen the profile to acquire abnormal data, and fuse the screened and qualified profile, wherein the fused profile can represent most of the whole conditions.
S4, taking the representative profile as an initial reference profile, substituting the initial reference profile into a vehicle dynamics model to calculate vehicle dynamics performance, wheel rail contact performance and steel rail abrasion, and obtaining a plurality of parameters;
preferably, in the step S4, the vehicle dynamics calculation includes derailment coefficient calculation and vehicle running stability calculation, and the vehicle running stability calculation is:
wherein A is vehicle body vibration acceleration, F is vibration frequency, and F (F) is frequency correction coefficient.
In the present invention, the derailment coefficient is a ratio (Q/P) of a lateral force Q and a vertical force P acting on the wheel at a certain time, and is required to be 1 or less.
The vehicle running stability mainly evaluates that various vibrations generated during the running of the train affect the riding comfort of passengers and the integrity of goods. For passenger vehicles, the stability index is required to be less than 3.5, and for freight lines, the stability index is required to be less than 4.
Preferably, in the step S4, the wheel-rail contact performance calculation uses a hertz contact theory to calculate the wheel-rail contact stress.
Preferably, in the step S4, the rail abrasion calculation adopts an Archard abrasion calculation model, and the rail abrasion is:
wherein V is wear For the material wear volume, K, of the contact area of the wheel rail w Is a dimensionless wear coefficient, F n S is the relative sliding distance of the contact area of the wheel rail, and H is the hardness of the material.
In the invention, in a wheel and rail abrasion calculation model, an Archard abrasion model is a sliding abrasion model for researching the abrasion mechanism of materials, the model considers that abrasion among materials only occurs in a contact spot sliding area, the abrasion of the materials is in direct proportion to the pressure and the sliding distance in the contact area, and an abrasion calculation formula is shown in the formula, wherein the hardness H of the materials is generally consistent with the hardness of a default wheel and a steel rail.
Preferably, in the step S4, the plurality of parameters include derailment coefficient, wheel load shedding rate, wheel set attack angle, vehicle body lateral stability index, vehicle body vertical stability index, wheel rail contact stress and rail total abrasion.
In the present invention, the derailment coefficient is a ratio (Q/P) of a lateral force Q and a vertical force P acting on a wheel at a certain moment, which is used to evaluate the running safety of a vehicle, and when the derailment coefficient is too large, the derailment phenomenon of the vehicle is liable to occur.
The wheel weight load shedding rate is delta P/P, wherein delta P is the wheel weight load shedding amount of the wheel at the load shedding side, P is the average static wheel weight of the wheel at the load shedding side and the load increasing side, and the wheel weight load shedding rate and the derailment coefficient are used for evaluating the running safety of the vehicle and preventing the derailment phenomenon of the vehicle.
The wheel set attack angle refers to an included angle formed between the tangential direction of a steel rail at the contact point of a wheel rail and the axis of the wheel shaft, and is used for reflecting the curve passing capability and difficulty of the vehicle.
The vehicle transverse stability index refers to the magnitude of acceleration in the vehicle transverse direction, which is used to judge the magnitude of transverse vibration during the running process of the vehicle.
The vehicle vertical stability index refers to the vertical acceleration of the vehicle and is used for judging the vertical vibration in the running process of the vehicle.
The wheel rail contact stress refers to a vertical force generated in a contact area between a rail and a wheel, and the wheel rail contact stress not only affects the running safety of a vehicle, but also can cause the abrasion of a steel rail to be increased.
The total abrasion of the steel rail refers to the phenomenon that the abrasion of the steel rail head is generated by the friction between the wheels and the steel rail, the total abrasion = vertical abrasion + side abrasion is 0.5, the vertical abrasion is the abrasion of the steel rail in the vertical direction, the side abrasion is the abrasion of the steel rail side surface caused by the interaction with the wheels, the profile is changed, and the side abrasion is used for reflecting the serious abrasion condition of the steel rail.
S5, carrying out normalization processing on a plurality of parameters, and substituting the parameters into an objective function according to weights of different parameters to obtain a target value;
preferably, in the step S5, the weights of the different parameters are obtained according to analysis and statistics of the historical detection data and the rail profile.
Preferably, in the step S5, the objective function is:
wherein,is a target value; />Is the weight coefficient of the parameter, wherein W 1 -W 7 The weight coefficient of the derailment coefficient, the weight coefficient of the wheel load shedding rate, the weight coefficient of the wheel set attack angle, the weight coefficient of the vehicle body transverse stability index, the weight coefficient of the vehicle body vertical stability index, the weight coefficient of the wheel track contact stress and the weight coefficient of the total abrasion of the steel rail are respectively; />Is a parameter, wherein a 1 -a 7 Respectively the derailment coefficient, the wheel load relief rate, the wheel set attack angle, the vehicle body transverse stability index, the vehicle body vertical stability index, the wheel rail contact stress and the total abrasion of the steel rail。
The function of the above objective function is: the influences of different profile designs on derailment coefficients, wheel load relief rates, wheel set attack angles, vehicle transverse stability indexes, vehicle vertical stability indexes, wheel rail contact stress and total abrasion of steel rails can be comprehensively considered.
S6, adopting a genetic algorithm to select, intersect and mutate the steel rail profile, returning to the step S4, and iteratively calculating an objective function until the objective function reaches a convergence standard to obtain the steel rail polishing reference profile.
In the invention, each steel rail profile is subjected to vehicle dynamics performance calculation, wheel-rail contact performance calculation and steel rail abrasion calculation to obtain 7 parameter values, namely a1-a7, and the 7 parameters are substituted into an objective function to obtain an A value. However, the A value is not necessarily the optimal solution, so that a large number of steel rail profiles are generated through a genetic algorithm, and a large number of A values can be obtained through calculation of vehicle dynamics performance, wheel-rail contact performance and steel rail abrasion calculation, and the optimal solution of the objective function, namely the most suitable steel rail profile, namely the steel rail grinding reference profile, is obtained through comparison of the A values.
The convergence of genetic algorithms generally refers to the fact that a new population generated by genetic iteration reaches a certain steady state, and the evolution process is regarded as a random process and examined.
Preferably, in the step S6, the global convergence of the genetic algorithm is:
wherein S is the individual space, S N Is population space, P is S N Probability distribution on X t ={X 1 (t),X 2 (t),...,X M (t) } is a population of the t generation, X i (t) is the population of individuals of the t generation, i=1, 2,..m, M is the population size, Q is set t =max{f(X i (t) |i=1, 2,..m) } is the maximum value of the fitness function value of the individuals in the population, and y=max { f (X) |x e S } is the global optimal solution.
The invention has the beneficial effects that:
according to the rail grinding reference profile design method provided by the invention, before rail grinding, automatic statistical analysis can be performed on the rail profile of the detection line, and the most typical representative profile is calculated, so that the reference profile can be designed according to the latest line profile, automatic collection and calculation can be realized in the whole process, and the workload of manual collection and calculation is reduced.
The rail polishing reference profile design method provided by the invention is based on the multi-objective optimized reference profile design, can comprehensively consider factors such as vehicle body dynamics performance, vehicle body stability, wheel-rail contact force, rail abrasion and the like, ensures the safety and comfort of a line, simultaneously reduces the field polishing workload as much as possible, and prolongs the service life of the rail. The whole steel rail profile detection and design process realizes full automation without a large amount of field manual work.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, features, and effects of the present invention.
Example 1
A rail grinding reference profile design method based on multi-objective optimization, as shown in fig. 1, comprises the following steps:
s1, researching the basic information of a line and a vehicle, and establishing a vehicle dynamics model according to the research result;
s2, detecting the profile of the steel rail of the investigated line;
s3, screening and fusing the detected rail profile to obtain a representative profile of the section of line;
s4, taking the representative profile as an initial reference profile, substituting the initial reference profile into a vehicle dynamics model to calculate vehicle dynamics performance, wheel rail contact performance and steel rail abrasion, and obtaining a plurality of parameters;
s5, carrying out normalization processing on a plurality of parameters, and substituting the parameters into an objective function according to weights of different parameters to obtain a target value;
s6, adopting a genetic algorithm to select, intersect and mutate the steel rail profile, returning to the step S4, and iteratively calculating an objective function until the objective function reaches a convergence standard to obtain the steel rail polishing reference profile.
In this embodiment, before each rail polishing, automatic statistical analysis can be performed on the rail profile of the detection line, and the most typical representative profile is calculated, so that the design of the reference profile according to the latest line profile is ensured, the automatic collection and calculation can be realized in the whole process, and the workload of manual collection and calculation is reduced.
Example 2
The embodiment further describes step S1 on the basis of embodiment 1, wherein in step S1, the investigated line information includes rail type, curve radius, rail bottom slope breaking; the basic information of the investigated vehicles comprises the types of wheel treads, locomotive and carriage models, a vehicle body structure, a bogie structure and a shock absorption structure.
In the S1 step, the purpose of establishing a vehicle dynamics model is to be used for simulating the running conditions of a vehicle under different profile conditions, so that derailment coefficients, comfortableness, wheel-rail contact stress and the like are calculated, and the vehicle dynamics model is established by adopting SIMPACK multi-body dynamics simulation software.
Example 3
In this embodiment, the step S2 is further described on the basis of embodiment 2, in the step S2, the rail profile detection means that the rail full section profile data is detected, the rail profile detection is performed by using a minitrof rail profile meter, and the purpose of performing rail profile detection on the investigated line is to obtain the current situation of the rail profile, and understand the rail profile abrasion condition so as to design the rail profile more conforming to the site conditions.
Example 4
This embodiment further describes step S3 on the basis of embodiment 3, where step S3 includes the following steps:
s31, comparing the detected steel rail profile with a standard steel rail profile, and screening out a profile with profile points of 400-1000 and steel rail abrasion less than 2 mm;
s32, carrying out average fusion on all the screened profiles to obtain the representative profile of the section of line.
In the steps, firstly, the detected steel rail profile is compared with the standard steel rail profile, and only the profile with the number of profile points being less than 1000 and more than 400 and the steel rail abrasion being less than 2mm is reserved. And then carrying out average fusion on all the retained profiles, thereby obtaining the representative profile of the section.
For the representative profile of the steel rail, the existing mode at present collects one to two steel rail profiles to represent the profile of the whole line according to manual experience, and the mode has high randomness and cannot represent the whole line condition. Because of certain difference of the rail profiles of each line, in order to obtain a more representative rail profile, the invention needs to acquire the line profile in a high density, then screen the profile to acquire abnormal data, and fuse the screened and qualified profile, wherein the fused profile can represent most of the whole conditions.
Example 5
The present embodiment further describes step S4 on the basis of embodiment 4, wherein in step S4, the vehicle dynamics performance calculation, the wheel-rail contact performance calculation, the rail abrasion calculation, and several parameters are specifically as follows:
1. vehicle dynamics calculation
In the step S4, the vehicle dynamics performance calculation includes derailment coefficient calculation and vehicle running stability calculation, where the vehicle running stability calculation is:
wherein A is vehicle body vibration acceleration, F is vibration frequency, and F (F) is frequency correction coefficient.
In the present invention, the derailment coefficient is a ratio (Q/P) of a lateral force Q and a vertical force P acting on the wheel at a certain time, and is required to be 1 or less.
The vehicle running stability mainly evaluates that various vibrations generated during the running of the train affect the riding comfort of passengers and the integrity of goods. For passenger vehicles, the stability index is required to be less than 3.5, and for freight lines, the stability index is required to be less than 4.
The frequency correction coefficient F (F) is specifically as follows:
TABLE 1 frequency correction coefficient
2. Wheel-rail contact performance calculation
In the step S4, the wheel-rail contact performance calculation adopts the hertz contact theory to calculate the wheel-rail contact stress, and specifically includes the following steps:
table 2 Hertz formula
3. Rail wear calculation
In the step S4, the steel rail abrasion calculation adopts an Archard abrasion calculation model, and the steel rail abrasion is as follows:
wherein V is wear For the material wear volume, K, of the contact area of the wheel rail w Is a dimensionless wear coefficient, F n S is the relative sliding distance of the contact area of the wheel rail, and H is the hardness of the material.
In the invention, in a wheel and rail abrasion calculation model, an Archard abrasion model is a sliding abrasion model for researching the abrasion mechanism of materials, the model considers that abrasion among materials only occurs in a contact spot sliding area, the abrasion of the materials is in direct proportion to the pressure and the sliding distance in the contact area, and an abrasion calculation formula is shown in the formula, wherein the hardness H of the materials is generally consistent with the hardness of a default wheel and a steel rail.
4. Several parameters
In the step S4, the parameters comprise derailment coefficient, wheel load shedding rate, wheel set attack angle, vehicle body transverse stability index, vehicle body vertical stability index, wheel rail contact stress and steel rail total abrasion.
In the present invention, the derailment coefficient is a ratio (Q/P) of a lateral force Q and a vertical force P acting on a wheel at a certain moment, which is used to evaluate the running safety of a vehicle, and when the derailment coefficient is too large, the derailment phenomenon of the vehicle is liable to occur.
The wheel weight load shedding rate is delta P/P, wherein delta P is the wheel weight load shedding amount of the wheel at the load shedding side, P is the average static wheel weight of the wheel at the load shedding side and the load increasing side, and the wheel weight load shedding rate and the derailment coefficient are used for evaluating the running safety of the vehicle and preventing the derailment phenomenon of the vehicle.
The wheel set attack angle refers to an included angle formed between the tangential direction of a steel rail at the contact point of a wheel rail and the axis of the wheel shaft, and is used for reflecting the curve passing capability and difficulty of the vehicle.
The vehicle transverse stability index refers to the magnitude of acceleration in the vehicle transverse direction, which is used to judge the magnitude of transverse vibration during the running process of the vehicle.
The vehicle vertical stability index refers to the vertical acceleration of the vehicle and is used for judging the vertical vibration in the running process of the vehicle.
The wheel rail contact stress refers to a vertical force generated in a contact area between a rail and a wheel, and the wheel rail contact stress not only affects the running safety of a vehicle, but also can cause the abrasion of a steel rail to be increased.
The total abrasion of the steel rail refers to the phenomenon that the abrasion of the steel rail head is generated by the friction between the wheels and the steel rail, the total abrasion = vertical abrasion + side abrasion is 0.5, the vertical abrasion is the abrasion of the steel rail in the vertical direction, the side abrasion is the abrasion of the steel rail side surface caused by the interaction with the wheels, the profile is changed, and the side abrasion is used for reflecting the serious abrasion condition of the steel rail.
Example 6
The embodiment further describes step S5 on the basis of embodiment 5, wherein in step S5, weights of different parameters are obtained according to analysis and statistics on historical detection data and rail profile. In this embodiment, the weights of the 7 parameters are as follows:
TABLE 3 parameter weights
In the step S5, the objective function is:
wherein,is a target value; />Is the weight coefficient of the parameter, wherein W 1 -W 7 The weight coefficient of the derailment coefficient, the weight coefficient of the wheel load shedding rate, the weight coefficient of the wheel set attack angle, the weight coefficient of the vehicle body transverse stability index, the weight coefficient of the vehicle body vertical stability index, the weight coefficient of the wheel track contact stress and the weight coefficient of the total abrasion of the steel rail are respectively; />Is a parameter, wherein a 1 -a 7 The derailment coefficient, the wheel load relief rate, the wheel set attack angle, the vehicle body transverse stability index, the vehicle body vertical stability index, the wheel rail contact stress and the total abrasion of the steel rail are respectively.
The function of the above objective function is: the influences of different profile designs on derailment coefficients, wheel load relief rates, wheel set attack angles, vehicle transverse stability indexes, vehicle vertical stability indexes, wheel rail contact stress and total abrasion of steel rails can be comprehensively considered.
Example 7
In this embodiment, the step S6 is further described on the basis of embodiment 6, in this embodiment, each rail profile performs calculation of vehicle dynamics performance, wheel-rail contact performance and rail wear calculation, so as to obtain 7 parameter values, namely a1-a7, and the 7 parameters are substituted into the objective function to obtain an a value. However, the A value is not necessarily the optimal solution, so that a large number of steel rail profiles are generated through a genetic algorithm, and a large number of A values can be obtained through calculation of vehicle dynamics performance, wheel-rail contact performance and steel rail abrasion calculation, and the optimal solution of the objective function, namely the most suitable steel rail profile, namely the steel rail grinding reference profile, is obtained through comparison of the A values.
The convergence of genetic algorithms generally refers to the fact that a new population generated by genetic iteration reaches a certain steady state, and the evolution process is regarded as a random process and examined.
In the step S6, the global convergence of the genetic algorithm is:
wherein S is the individual space, S N Is population space, P is S N Probability distribution on X t ={X 1 (t),X 2 (t),...,X M (t) } is a population of the t generation, X i (t) is the population of individuals of the t generation, i=1, 2,..m, M is the population size, Q is set t =max{f(X i (t) |i=1, 2,..m) } is the maximum value of the fitness function value of the individuals in the population, and y=max { f (X) |x e S } is the global optimal solution.
While the embodiments of the present invention have been described in detail, the present invention is not limited to the embodiments described above, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (10)
1. A rail grinding reference profile design method based on multi-objective optimization is characterized by comprising the following steps:
s1, researching the basic information of a line and a vehicle, and establishing a vehicle dynamics model according to the research result;
s2, detecting the profile of the steel rail of the investigated line;
s3, screening and fusing the detected rail profile to obtain a representative profile of the section of line;
s4, taking the representative profile as an initial reference profile, substituting the initial reference profile into a vehicle dynamics model to calculate vehicle dynamics performance, wheel rail contact performance and steel rail abrasion, and obtaining a plurality of parameters;
s5, carrying out normalization processing on a plurality of parameters, and substituting the parameters into an objective function according to weights of different parameters to obtain a target value;
s6, adopting a genetic algorithm to select, intersect and mutate the steel rail profile, returning to the step S4, and iteratively calculating an objective function until the objective function reaches a convergence standard to obtain the steel rail polishing reference profile.
2. The method for designing a rail grinding reference profile according to claim 1, wherein in the step S1, the investigated line information includes rail type, curve radius, rail bottom slope breaking; the basic information of the investigated vehicles comprises the types of wheel treads, locomotive and carriage models, a vehicle body structure, a bogie structure and a shock absorption structure.
3. A rail grinding reference profile design method as set forth in claim 1, wherein said step S3 includes the steps of:
s31, comparing the detected steel rail profile with a standard steel rail profile, and screening out a profile with profile points of 400-1000 and steel rail abrasion less than 2 mm;
s32, carrying out average fusion on all the screened profiles to obtain the representative profile of the section of line.
4. The rail grinding reference profile design method as set forth in claim 1, wherein in the step S4, the vehicle dynamics calculation includes a derailment coefficient calculation and a vehicle running smoothness calculation, the vehicle running smoothness calculation being:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein A is vehicle body vibration acceleration, f is vibration frequencyThe rate, F (F), is the frequency correction coefficient.
5. A rail grinding reference profile design method as set forth in claim 1, wherein in said step S4, said wheel-rail contact performance calculation uses a hertz contact theory to calculate wheel-rail contact stress.
6. The method for designing a rail grinding reference profile as claimed in claim 1, wherein in the step S4, the rail abrasion calculation is implemented by using an Archard abrasion calculation model, and the rail abrasion is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein V is wear For the material wear volume, K, of the contact area of the wheel rail w Is a dimensionless wear coefficient, F n S is the relative sliding distance of the contact area of the wheel rail, and H is the hardness of the material.
7. The method of claim 1, wherein in step S4, the plurality of parameters includes derailment coefficient, wheel load reduction rate, wheel set angle of attack, vehicle body lateral stability index, vehicle body vertical stability index, wheel rail contact stress, and rail total wear.
8. A rail grinding reference profile design method as set forth in claim 1, wherein in step S5, weights of the different parameters are obtained according to analysis statistics of historical detection data and rail profiles.
9. A rail grinding reference profile design method as set forth in claim 1, wherein in said step S5, said objective function is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is a target value; />Is the weight coefficient of the parameter, wherein W 1 -W 7 The weight coefficient of the derailment coefficient, the weight coefficient of the wheel load shedding rate, the weight coefficient of the wheel set attack angle, the weight coefficient of the vehicle body transverse stability index, the weight coefficient of the vehicle body vertical stability index, the weight coefficient of the wheel track contact stress and the weight coefficient of the total abrasion of the steel rail are respectively; />Is a parameter, wherein a 1 -a 7 The derailment coefficient, the wheel load relief rate, the wheel set attack angle, the vehicle body transverse stability index, the vehicle body vertical stability index, the wheel rail contact stress and the total abrasion of the steel rail are respectively.
10. A rail grinding reference profile design method as claimed in claim 1, wherein in step S6, the global convergence of the genetic algorithm is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein S is the individual space, S N Is population space, P is S N Probability distribution on X t ={X 1 (t),X 2 (t),...,X M (t) } is a population of the t generation, X i (t) is the population of individuals of the t generation, i=1, 2,..m, M is the population size, Q is set t =max{f(X i (t) |i=1, 2,..m) } is the maximum value of the fitness function value of the individuals in the population, and y=max { f (X) |x e S } is the global optimal solution.
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| CN117973086B (en) * | 2024-03-28 | 2024-06-11 | 中铁第四勘察设计院集团有限公司 | Multi-objective optimization-based whole-path steel rail polishing planning method and system |
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