CN106845006A - Rail vehicle weight center of gravity design optimization method and system based on multiple-objection optimization - Google Patents
Rail vehicle weight center of gravity design optimization method and system based on multiple-objection optimization Download PDFInfo
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Abstract
The present invention discloses a kind of rail vehicle weight center of gravity design optimization method based on multiple-objection optimization, including：Set up weight center of gravity parameter list and axle roller re-computation table, optimization task model is set up in optimization software with this, wherein, with car body weight and body gravity coordinate as optimized variable, weight and barycentric coodinates with building block weighs deviation as bound variable as design variable with axle again deviation and wheel；Determine the span of design variable and bound variable with preset algorithm, and optimized variable optimizing index；Multiple-objection optimization calculating is carried out to optimization task model by multi-objective optimization algorithm, the weight and barycentric coodinates of each building block after being optimized.The present invention can realize the weight and distribution optimization to each parts on rail vehicle, reach light-weight design purpose, while improving inside roominess utilization rate.Invention additionally discloses a kind of rail vehicle weight center of gravity design optimization system based on multiple-objection optimization, its advantage is as described above.
Description
Technical field
It is more particularly to a kind of to be based on multiple-objection optimization the present invention relates to rail vehicle and multi-objective optimization algorithm technical field
Rail vehicle weight center of gravity design optimization method.The invention further relates to a kind of rail vehicle weight weight based on multiple-objection optimization
Heart design optimization system.
Background technology
With the development of Chinese mechanical industry, rail vehicle manufactures and designs and reached its maturity already.
For rail vehicle, Vehicular system and component weight at different levels and gravity's center control, axle weight, wheel weight and unbalance factor
Calculating is the committed step in design link, and domestic and international vehicle R＆D firm is all extremely paid attention to.Assessment to result of calculation is international
Upper accepted standard is mainly IEC 61133 and DIN 50125.Wherein, the IEC 61133 specified in more detail error of locomotive weight
Scope, every axle weight and the difference range of average axle load and each wheel weight and wheel where this wheel to average value difference range；
And DIN 50125 specify only the weight of locomotive, axle weight, wheel weight, the error of locomotive weight, the every heavy difference with average axle load of axle
Value, each wheel weight and wheel where this wheel to the difference of average value should meet the requirement of contract or bidding documents, not to providing
Body numerical value.In Europe, especially in the locomotive design of Germany, still using the numerical value of the regulations of UIC 610 as DIN 50125
Supplement.
At present, existing vehicle weight calculates artificial control, during Car design, fails vehicle parts at different levels
Weight, center of gravity, wheel weight, axle press the allocative decision hardly possible of contract or relevant criterion examination, each component weight and distributing position again
To be optimal a little.In rail vehicle detail design or fabrication stage, part vehicle axle weight severe overweight is, it is necessary to Weight-optimised
System realizes vehicle loss of weight, while, it is necessary to Vehicular system and component weight at different levels when carrying out light-weight design to off-the-shelf item
Optimization distribution.The increase of vehicle weight causes energy consumption increase, wheel-rail wear increase and cost increase, and is difficult vehicle weight
Amount, axle weight and wheel are limited within prescribed limit again.
Therefore, the weight and distribution optimization to each parts on rail vehicle how are realized, light-weight design mesh is reached
, it is those skilled in the art's technical problem urgently to be resolved hurrily while improving inside roominess utilization rate.
The content of the invention
It is an object of the invention to provide a kind of rail vehicle weight center of gravity design optimization method based on multiple-objection optimization, energy
The enough weight and distribution optimization realized to each parts on rail vehicle, reaches light-weight design purpose, while improving car body
Interior space availability ratio.It is excellent it is a further object of the present invention to provide a kind of rail vehicle weight center of gravity design based on multiple-objection optimization
Change system.
In order to solve the above technical problems, the present invention provides a kind of rail vehicle weight center of gravity design based on multiple-objection optimization
Optimization method, including：
The current weight of each building block of measure track vehicle and current barycentric coodinates, rail vehicle is calculated with this
Car body weight FcbWith body gravity coordinate Xcb、Ycb、Zcb, and write data into weight center of gravity parameter list；
Parameter according to the weight center of gravity parameter list calculates the axle weight deviation and wheel weight deviation of each axle in bogie, and will
Data write axle roller re-computation table；
Data in the weight center of gravity parameter list and axle roller re-computation table are set up optimization in optimization software and are appointed
Business model, wherein, with car body weight FcbWith body gravity coordinate Xcb、Ycb、ZcbIt is optimized variable, with building block each described
Weight and barycentric coodinates be design variable, with bogie each axle axle weight deviation with wheel weight deviation as bound variable；
The value model of wherein described design variable and bound variable is determined with preset algorithm to the optimization task model
Enclose, and the optimized variable optimizing index；
Multiple-objection optimization calculating is carried out to the optimization task model by default multi-objective optimization algorithm, is optimized
The weight and barycentric coodinates of each building block afterwards.
Preferably, the car body weight and body gravity coordinate for calculating rail vehicle are specifically included：
Weight and barycentric coodinates according to each building block calculate its gravitational moment respectively；
According to the weight and gravitational moment of each building block, the gross weight and total force of read group total rail vehicle
Square, further according to bogie weight FbgCalculate car body weight FcbWith car body gravitational moment；
According to car body weight FbgBody gravity coordinate X is calculated with car body gravitational momentcb、Ycb、Zcb。
Preferably, weight center of gravity parameter list is write data into specifically include：
By the current weight of each building block and current barycentric coodinates write device weight table；
Measurement bogie pivot center a, wheelbase d, rolling circle are apart from d1, bogie center of gravity be supporting gravity apart from d with two2, turn
With two it is supporting gravity apart from d to frame center of gravity3, and write structure parameter list.
Preferably, wherein described design variable and bound variable are determined with preset algorithm to the optimization task model
Span, and the optimized variable optimizing index, specifically include：
Experimental design is carried out with optimal Latin hypercube algorithm to the optimization task model, each composition is determined
The weight value border of part, x coordinate value border, y-coordinate value border and z coordinate value border；
According to the design variable can use engineering centrifugal pump determine axle weight deviation and wheel weight deviation tolerance zone and
Car body weight FcbWith body gravity coordinate Xcb、Ycb、ZcbOptimizing index.
Preferably, after carrying out experimental design with optimal Latin hypercube algorithm to the optimization task model, also wrap
Include：
Result data to experimental design carries out basis of sensitivity analysis, obtains each described building block to car body weight FcbAnd car
Body barycentric coodinates Xcb、Ycb、ZcbContribution rate sequence；
Contribution rate sorting data is screened according to default engineering constraints, interception meets each of preset requirement
The building block is used as design variable.
Preferably, multiple-objection optimization calculating is carried out to the optimization task model by default multi-objective optimization algorithm,
Specifically include：
Approximate modeling is carried out to the result data of experimental design with response surface model or neural network model, is optimized
Car body weight F afterwardscbWith body gravity coordinate Xcb、Ycb、ZcbForecast model；
Global optimization is carried out with multi-objective Algorithm NSGA-II to the forecast model.
Preferably, multiple-objection optimization calculating is carried out to the optimization task model by default multi-objective optimization algorithm,
Specifically include：
Multiple-objection optimization calculating is carried out to the optimization task model with multi-objective Algorithm NSGA-II.
Preferably, multiple-objection optimization calculating is carried out to the optimization task model by default multi-objective optimization algorithm
When：
The design variable is adjusted step by step according to default step-length variable；
Wheel weight deviation in the bound variable is controlled below 3%, and axle weight deviation is controlled below 2%；
Body gravity coordinate X in the optimized variablecbOptimization range control in -50mm~50mm, body gravity is sat
Mark YcbOptimization range control in -3mm~3mm, body gravity coordinate ZcbOptimization range control in 0~1800mm.
Preferably, after the weight and barycentric coodinates of each building block after being optimized, also include：
Set to the weight and barycentric coodinates of building block each described is screened by engineering constraints, and is obtained
The optimization disaggregation that engineering is used must be adapted to.
The present invention also provides a kind of rail vehicle weight center of gravity design optimization system based on multiple-objection optimization, including：
First data acquisition module, current weight and current center of gravity for each building block of measure track vehicle are sat
Mark, the car body weight F of rail vehicle is calculated with thiscbWith body gravity coordinate Xcb、Ycb、Zcb, and write data into weight center of gravity ginseng
Number table；
Second data acquisition module, the axle for calculating each axle in bogie according to the parameter of the weight center of gravity parameter list
Weight deviation and wheel weight deviation, and write data into axle roller re-computation table；
Task model sets up module, exists for the data in the weight center of gravity parameter list and axle roller re-computation table
Optimization task model is set up in optimization software, wherein, with car body weight FcbWith body gravity coordinate Xcb、Ycb、ZcbFor optimization becomes
Amount, the weight and barycentric coodinates with building block each described weighs deviation with wheel as design variable with the axle of each axle in bogie
Weight deviation is bound variable；
Displacement variation control module, for the optimization task model with preset algorithm determine wherein described design variable and
The span of bound variable, and the optimized variable optimizing index；
Multiple-objection optimization computing module, for determining wherein described design with preset algorithm to the optimization task model
The span of variable and bound variable, and the optimized variable optimizing index.
Rail vehicle weight center of gravity design optimization method based on multiple-objection optimization provided by the present invention, measures rail first
The current weight of each building block and current barycentric coodinates on road vehicle, it is determined that the original state of initial each building block,
And the car body weight and body gravity coordinate of rail vehicle are calculated according to these parameters, then by the parameter read-in of each building block
In weight center of gravity parameter list；Secondly, the parameter according to each building block can calculate each axle in bogie axle weight deviation and
Wheel weight deviation, then deviation data and computing formula etc. are write into axle roller re-computation table；Then according to weight center of gravity parameter list and
Data in axle roller re-computation table set up optimization task model in optimization software, and the optimization task model is to become with design
Amount, bound variable, optimized variable and built-in algorithms etc., and the model with input quantity and output quantity, concretely 3D models etc..
Wherein, car body weight and body gravity coordinate are optimized variable (or optimization aim), and the weight and center of gravity of each building block are sat
Design variable is designated as, and the axle weight deviation of each axle and wheel weight deviation are bound variable in bogie.Next, optimization task model
After foundation, and before calculating is optimized, it is necessary first to the value of design variable and bound variable is determined with preset algorithm
Scope, and optimized variable optimizing index.Finally, you can optimization task model is entered by default multi-objective optimization algorithm
Row multiple-objection optimization calculate so that after being optimized, the weight and barycentric coodinates of satisfactory each building block.In this way,
The present invention for each building block of rail vehicle weight and barycentric coodinates, using car body weight and body gravity coordinate as
Optimization aim, then optimization task model is set up as constraints using the axle weight deviation and wheel weight deviation of each axle in bogie,
Calculating is optimized to optimization task model by default multi-objective optimization algorithm, each described group after being optimized with this
Into the weight and barycentric coodinates of part, the position adjustment of building block is carried out during rail vehicle actual assembled according still further to optimum results
With weight adjustment, the weight and distribution optimization to each parts on rail vehicle can be effectively realized, reach lightweight and set
Meter purpose, while improving inside roominess utilization rate.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this
Inventive embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
The accompanying drawing of offer obtains other accompanying drawings.
Fig. 1 is a kind of flow chart of specific embodiment provided by the present invention；
Fig. 2 is a kind of module map of specific embodiment provided by the present invention.
Wherein, in Fig. 2：
First data acquisition module -1, the second data acquisition module -2, task model sets up module -3, Variable Control
Module -4, multiple-objection optimization computing module -5.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Fig. 1 is refer to, Fig. 1 is a kind of flow chart of specific embodiment provided by the present invention.
In a kind of specific embodiment provided by the present invention, the rail vehicle weight center of gravity based on multiple-objection optimization sets
Meter optimization method mainly includes five steps, respectively：The current weight of each building block of measure track vehicle and current
Barycentric coodinates, the car body weight Fcb that rail vehicle is calculated with this and body gravity coordinate Xcb, Ycb, Zcb, and write data into
Weight center of gravity parameter list；The axle weight deviation that parameter according to the weight center of gravity parameter list calculates each axle in bogie is inclined with wheel weight
Difference, and write data into axle roller re-computation table；According to the data in the weight center of gravity parameter list and axle roller re-computation table
Optimization task model is set up in optimization software, wherein, with car body weight Fcb and body gravity coordinate Xcb, Ycb, Zcb as excellent
Change variable, the weight and barycentric coodinates with building block each described weigh deviation as design variable with the axle of each axle in bogie
It is bound variable with wheel weight deviation；Wherein described design variable and constraint are determined with preset algorithm to the optimization task model
The span of variable, and the optimized variable optimizing index；By default multi-objective optimization algorithm to the optimization
Task model carries out multiple-objection optimization calculating, the weight and barycentric coodinates of each building block after being optimized.
Wherein, in the first step, when the current weight and current barycentric coodinates of each building block of measure track vehicle,
Can be using the car body bottom surface of rail vehicle as x-y plane, using car body one end side as x-z-plane, with short transverse as z-axis.
After the measurement has been completed, you can determine the original state of each building block on rail vehicle, to determine to be built in subsequent step
Original state during mould.And calculate the car body of rail vehicle in the current weight according to each building block and current barycentric coodinates
Weight FcbWith body gravity coordinate Xcb、Ycb、ZcbWhen, specifically, can first according to the weight and barycentric coodinates of each building block
Its gravitational moment is calculated respectively, such as the weight of certain part is G1, and its barycentric coodinates is x, y, z, then its gravitational moment is respectively Mx
=xG1, My=yG1, Mz=zG1.Simultaneously as the parts on rail vehicle are numerous, and according to its functional structure, can
Think to be divided into three parts, i.e. I grade part (such as certain system etc.), II grades of part (such as certain mechanism etc.) and III level part (ratio
Such as certain part).Obviously, the weight and barycentric coodinates of III level part be can be measured directly, and according to above-mentioned computational methods meter
After calculating the weight and gravitational moment of all III level parts, you can it is sued for peace respectively, so as to calculate all II grades of parts
Weight and gravitational moment, then II grades of part is sued for peace respectively, you can the weight and gravitational moment of all I grades of parts are calculated, finally to I
Level part is sued for peace respectively, can calculate the gross weight and gross weight torque of rail vehicle.Wherein, bogie can integrally as railcar
A building block, its weight FbgIt has been measured (or during for preset value, without measurement) with barycentric coodinates, therefore,
Car body weight F can be calculated come the gross weight of that and gross weight torque, and the weight and gravitational moment of bogie according to railcarcb
With car body gravitational moment (rail vehicle is made up of car body and bogie), further according to car body gravitational moment and car body weight FcbPhase
Division result, can calculate body gravity coordinate Xcb、Ycb、Zcb。
, it is necessary to write data into weight center of gravity ginseng after the car body weight of rail vehicle and body gravity coordinate is calculated
Number table.Specifically, can then survey first by the current weight of each building block and current barycentric coodinates write device weight table
Amount bogie pivot center a, wheelbase d, rolling circle are apart from d1, bogie center of gravity be supporting gravity apart from d with two2, bogie center of gravity with
Two is supporting gravity apart from d3Deng (such as also including primary spring transverse direction span, secondary spring transverse direction span, Vehicle length, car
Width and height of car etc.) it is related to axle weight, the data of wheel weight of bogie, and by these parameter read-in structural parameters tables.
That is, the data of weight of equipment table and structural parameters table together constitute weight center of gravity parameter list.
In second step, the axle weight of each axle is inclined in the parameter calculating bogie of the weight center of gravity parameter list according to foregoing foundation
Difference and wheel weight deviation, and calculation result data is write into axle roller re-computation table.Specifically, by taking two-axis bogie as an example, it is first
First can be according to formula：Fhs=Fcb×(a/2±Xcb)/a calculates bogie sprung mass Fhs, then further according to formula：Fx1=(Fhs
×(d/2-d3)+Fbg×(d/2-d3+d2))/d calculating first axle weights, according to formula：Fx2=Fhs+Fbg-Fx1The second axle weight is calculated,
Further according to formula：Fw=Fx1×(d1/2±Ycb)/d1Calculate wheel weight.And after the completion of each axle axle weight and each wheel wheel weight are calculated,
Shaft weight deviation and wheel weight deviation can respectively be calculated according to the default average axle load peace deferent of bogie again.Finally, then will
Correlation computations formula, relevant parameter and the axle weight deviation for calculating and wheel weight deviation are written in axle roller re-computation table.
Preferably, the said equipment weight table, structural parameters table and axle roller re-computation table can be excel tables.And if adjusting
Each excel tables of data is tried, the parameter in adjusting device weight table and structural parameters table can obtain difference in axle roller re-computation table
Axle weight deviation and wheel weight deviation.
In the third step, weight center of gravity parameter list and axle roller re-computation table set up completion after, you can according to this two
Data in table set up optimization task model in optimization software.Specifically, can be with the data of two tables in multidisciplinary optimization software
Middle to set up optimization task model, the optimization task model is the Application Software Program of built-in algorithms, certainly can be while output display
It is 3D modeling, can imports and include weight of equipment table, structural parameters table, the excel data of axle roller re-computation table, is defined
Input/output variable, increase and deletion variable, modification variable etc., debug flow so that supplemental characteristic is transmitted.Wherein, with car body
Weight FcbWith body gravity coordinate Xcb、Ycb、ZcbIt is optimized variable, with each building block (predominantly each composition portion of car body
Part) weight and barycentric coodinates be design variable, with bogie each axle axle weight deviation with wheel weight deviation as bound variable.Such as
This, equivalent in task model is optimized, the weight and barycentric coodinates using each building block as input, and with car body weight
With body gravity coordinate as output, while using the axle weight deviation of each axle in bogie and taking turns weight deviation as restrictive condition.
In the 4th step, the optimization task model to above-mentioned foundation determines wherein design variable and constraint with preset algorithm
The span of variable, and optimized variable optimizing index.The weight and center of gravity of design variable, i.e. each building block are sat
Mark, it is necessarily adjustable in the range of certain, but its adjustable range needs considered critical.Equally, bound variable, i.e. axle weight are inclined
Difference and wheel weight deviation itself also have certain span, and optimized variable, i.e. car body weight and body gravity coordinate also have
A range of applicable space, i.e. its optimizing index not unique value, but an interval.Specifically, can be to optimization task
The optimal Latin hypercube algorithm of model use carries out experimental design, and then determines the span of design variable.I.e. each is constituted
The weight value border of part, x coordinate value border, y-coordinate value border and z coordinate value border.Then, it is determined that each
After the interval interval with barycentric coodinates of the weight of building block, each building block can be determined according to default engineering applicable elements
Weight and barycentric coodinates engineering centrifugal pump, i.e. several applicable values.Further according to several values of design variable, can
To further determine that the tolerance zone and car body weight F of axle weight deviation and wheel weight deviationcbWith body gravity coordinate Xcb、Ycb、Zcb
Optimizing index.
Further, it is contemplated that the quantity of design variable is larger, if all adjusting, optimize the speed for calculating it is relatively slow and
Amount of calculation is larger, therefore, the present embodiment is screened to design variable.Specifically, using optimal drawing to optimization task model
Fourth hypercube multiprocessors are carried out after experimental design, can carry out basis of sensitivity analysis to the result data of experimental design, can obtain normalizing
Each building block is to car body weight F after changecbWith body gravity coordinate Xcb、Ycb、ZcbContribution rate sequence.Contribution rate herein
Refer to the adjustment amount in proportion of each building block to car body weight and the influence degree of body gravity coordinate, contribution rate is bigger, shadow
The degree of sound is bigger.After obtaining the contribution rate sequence of each building block, you can according to default engineering constraints to contribution rate
Sorting data is screened, so as to intercept each building block part for meeting preset requirement as design variable, discharge is not inconsistent
Close preset requirement, contribution rate is relatively low, influence less building block to car body weight and body gravity coordinate.In this way, logical
The contribution rate sequence to design variable is crossed, the quantity of design variable has been simplified, optimization efficiency is improve.
In the 5th step, multiple-objection optimization calculating is carried out to optimization task model by default multi-objective optimization algorithm.
Specifically, in one embodiment, multi-objective Algorithm NSGA-II (Non-Dominated Sorting Genetic can be used
Algorithm II, the genetic algorithm of the non-dominated ranking with elitism strategy) directly to optimization task model, to carry out multiple target excellent
Change and calculate, so as to obtain the optimum results (car body weight and body gravity coordinate) of optimized variable and the optimal solution set of design variable
(weight and barycentric coodinates of each building block).Meanwhile, in another embodiment, can first use response surface model or god
Approximate modeling is carried out to the result data of experimental design through network model, so that car body weight and body gravity after being optimized
The forecast model of coordinate, in the process, the error general control of forecast model is below 5%.The forecast model can directly exist
Output display pseudo-entity in three-dimensional software, can carry out the overall situation to the forecast model with multi-objective Algorithm NSGA-II again afterwards
Optimization, its optimization calculates directly identical to optimization task model with foregoing, and here is omitted.By the global excellent of forecast model
Change, compared to directly multiple-objection optimization calculating is carried out to optimization task model, its optimization speed is faster.
In addition, when calculating is optimized, design variable can enter in span allowable according to default step-length variable
Row is adjusted step by step.Such as, the weight of each building block can be set by experimental design boundary discrete method, be 5kg according to step-length variable
Adjusted step by step, the x coordinate of each building block is set by experimental design boundary discrete method, according to step-length variable for 10mm is carried out
Adjust step by step, the y and z coordinate of each building block are set by experimental design boundary discrete method, according to step-length variable for 5mm is carried out
Adjust step by step.Meanwhile, calculating formula is being optimized, the wheel weight deviation in bound variable specifically can be controlled in less than 3%, and axle weight is inclined
Difference specifically can be controlled in less than 2%.And the body gravity coordinate X in optimized variablecbSpecific optimization range can for -50mm~
Between 50mm, body gravity coordinate YcbOptimization range can be controlled in -3mm~3mm, body gravity coordinate ZcbOptimization range
Control is in 0~1800mm.
In this way, weight and barycentric coodinates of the present embodiment for each building block of rail vehicle, with car body weight and
Body gravity coordinate is built as optimization aim, then using the axle weight deviation of each axle in bogie and wheel weight deviation as constraints
Vertical optimization task model, calculating is optimized by default multi-objective optimization algorithm to optimization task model, obtains excellent with this
The weight and barycentric coodinates of each building block after change, group is carried out during rail vehicle actual assembled according still further to optimum results
Adjusted into the position adjustment of part and weight, can effectively realized excellent to the weight of each parts on rail vehicle and distribution
Change, reach light-weight design purpose, while improving inside roominess utilization rate.
Additionally, after calculating is optimized with multi-objective optimization algorithm to optimization task model or forecast model, can obtain
Obtain the optimal solution set of several weight and barycentric coodinates on each building block.For further raising reliability and accurately
Property, set that can be again to the weight and barycentric coodinates of each building block is screened by engineering constraints, such as each
After intensity, tolerance of building block etc., so screening, the optimization disaggregation that suitable engineering is used can be obtained.
As shown in Fig. 2 Fig. 2 is a kind of module map of specific embodiment provided by the present invention.
In a kind of specific embodiment provided by the present invention, the rail vehicle weight center of gravity based on multiple-objection optimization sets
Meter optimization system mainly includes that the first data acquisition module 1, the second data acquisition module 2, task model set up module 3, variable
Control module 4 and multiple-objection optimization computing module 5.Wherein, the first data acquisition module 1 is mainly used in each of measure track vehicle
The current weight of individual building block and current barycentric coodinates, the car body weight F of rail vehicle is calculated with thiscbWith body gravity coordinate
Xcb、Ycb、Zcb, and write data into weight center of gravity parameter list；Second data acquisition module 2 is mainly used according to the weight weight
The parameter of heart parameter list calculates the axle weight deviation and wheel weight deviation of each axle in bogie, and writes data into axle roller re-computation
Table；Task model is set up the data that module 3 is mainly used in the weight center of gravity parameter list and axle roller re-computation table and is existed
Optimization task model is set up in optimization software, wherein, with car body weight FcbWith body gravity coordinate Xcb、Ycb、ZcbFor optimization becomes
Amount, the weight and barycentric coodinates with building block each described weighs deviation with wheel as design variable with the axle of each axle in bogie
Weight deviation is bound variable；Displacement variation control module 4 is mainly used in determining wherein the optimization task model with preset algorithm
The span of the design variable and bound variable, and the optimized variable optimizing index；Multiple-objection optimization calculates mould
Block 5 is mainly used in determining the optimization task model with preset algorithm the value of wherein described design variable and bound variable
Scope, and the optimized variable optimizing index.
The optimized calculation method of the rail vehicle weight center of gravity design optimization system based on multiple-objection optimization is based on foregoing
The rail vehicle weight center of gravity design optimization method of multiple-objection optimization is identical, and here is omitted.
The foregoing description of the disclosed embodiments, enables professional and technical personnel in the field to realize or uses the present invention.
Various modifications to these embodiments will be apparent for those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, the present invention
The embodiments shown herein is not intended to be limited to, and is to fit to and principles disclosed herein and features of novelty phase one
The scope most wide for causing.
Claims (10)
1. a kind of rail vehicle weight center of gravity design optimization method based on multiple-objection optimization, it is characterised in that including：
The current weight of each building block of measure track vehicle and current barycentric coodinates, the car body of rail vehicle is calculated with this
Weight FcbWith body gravity coordinate Xcb、Ycb、Zcb, and write data into weight center of gravity parameter list；
Parameter according to the weight center of gravity parameter list calculates the axle weight deviation and wheel weight deviation of each axle in bogie, and by data
Write-in axle roller re-computation table；
Data in the weight center of gravity parameter list and axle roller re-computation table set up optimization task mould in optimization software
Type, wherein, with car body weight FcbWith body gravity coordinate Xcb、Ycb、ZcbIt is optimized variable, with the weight of building block each described
Amount and barycentric coodinates are design variable, and the axle weight deviation of each axle and wheel weight deviation are as bound variable with bogie；
The span of wherein described design variable and bound variable is determined with preset algorithm to the optimization task model, with
And the optimizing index of the optimized variable；
Multiple-objection optimization calculating is carried out to the optimization task model by default multi-objective optimization algorithm, after being optimized
The weight and barycentric coodinates of each building block.
2. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 1, its feature
It is that the car body weight and body gravity coordinate for calculating rail vehicle are specifically included：
Weight and barycentric coodinates according to each building block calculate its gravitational moment respectively；
According to the weight and gravitational moment of each building block, the gross weight and gross weight torque of read group total rail vehicle, then
According to bogie weight FbgCalculate car body weight FcbWith car body gravitational moment；
According to car body weight FbgBody gravity coordinate X is calculated with car body gravitational momentcb、Ycb、Zcb。
3. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 2, its feature
It is to write data into weight center of gravity parameter list to specifically include：
By the current weight of each building block and current barycentric coodinates write device weight table；
Measurement bogie pivot center a, wheelbase d, rolling circle are apart from d1, bogie center of gravity be supporting gravity apart from d with two2, bogie
Center of gravity is supporting gravity apart from d with two3, and write structure parameter list.
4. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 3, its feature
It is to determine the span of wherein described design variable and bound variable with preset algorithm to the optimization task model,
And the optimizing index of the optimized variable, specifically include：
Experimental design is carried out with optimal Latin hypercube algorithm to the optimization task model, each building block is determined
Weight value border, x coordinate value border, y-coordinate value border and z coordinate value border；
The engineering centrifugal pump that be can use according to the design variable determines the tolerance zone and car body of axle weight deviation and wheel weight deviation
Weight FcbWith body gravity coordinate Xcb、Ycb、ZcbOptimizing index.
5. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 4, its feature
It is after carrying out experimental design with optimal Latin hypercube algorithm to the optimization task model, also to include：
Result data to experimental design carries out basis of sensitivity analysis, obtains each described building block to car body weight FcbWith car body weight
Heart coordinate Xcb、Ycb、ZcbContribution rate sequence；
Contribution rate sorting data is screened according to default engineering constraints, interception meet preset requirement each described in
Building block is used as design variable.
6. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 5, its feature
It is that multiple-objection optimization calculating is carried out to the optimization task model by default multi-objective optimization algorithm, specifically includes：
Approximate modeling is carried out to the result data of experimental design with response surface model or neural network model, after being optimized
Car body weight FcbWith body gravity coordinate Xcb、Ycb、ZcbForecast model；
Global optimization is carried out with multi-objective Algorithm NSGA-II to the forecast model.
7. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 1, its feature
It is that multiple-objection optimization calculating is carried out to the optimization task model by default multi-objective optimization algorithm, specifically includes：
Multiple-objection optimization calculating is carried out to the optimization task model with multi-objective Algorithm NSGA-II.
8. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 6 or 7, it is special
Levy and be, when carrying out multiple-objection optimization calculating to the optimization task model by default multi-objective optimization algorithm：
The design variable is adjusted step by step according to default step-length variable；
Wheel weight deviation in the bound variable is controlled below 3%, and axle weight deviation is controlled below 2%；
Body gravity coordinate X in the optimized variablecbOptimization range control in -50mm~50mm, body gravity coordinate Ycb
Optimization range control in -3mm~3mm, body gravity coordinate ZcbOptimization range control in 0~1800mm.
9. the rail vehicle weight center of gravity design optimization method based on multiple-objection optimization according to claim 8, its feature
It is after the weight and barycentric coodinates of each building block after being optimized, also to include：
Set to the weight and barycentric coodinates of building block each described is screened by engineering constraints, and is fitted
Close the optimization disaggregation that engineering is used.
10. a kind of rail vehicle weight center of gravity design optimization system based on multiple-objection optimization, it is characterised in that including：
First data acquisition module, current weight and current barycentric coodinates for each building block of measure track vehicle,
The car body weight F of rail vehicle is calculated with thiscbWith body gravity coordinate Xcb、Ycb、Zcb, and write data into weight center of gravity parameter
Table；
Second data acquisition module, the axle weight for calculating each axle in bogie according to the parameter of the weight center of gravity parameter list is inclined
Difference and wheel weight deviation, and write data into axle roller re-computation table；
Task model sets up module, for the data in the weight center of gravity parameter list and axle roller re-computation table in optimization
Optimization task model is set up in software, wherein, with car body weight FcbWith body gravity coordinate Xcb、Ycb、ZcbIt is optimized variable, with
The weight and barycentric coodinates of each building block are design variable, with the axle weight deviation of each axle in bogie and wheel weight deviation
It is bound variable；
Displacement variation control module, for determining wherein described design variable and constraint with preset algorithm to the optimization task model
The span of variable, and the optimized variable optimizing index；
Multiple-objection optimization computing module, for determining wherein described design variable with preset algorithm to the optimization task model
With the span of bound variable, and the optimized variable optimizing index.
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