CN106202731A - Bridge crane multi-flexibl e dynamics structural optimization method - Google Patents

Bridge crane multi-flexibl e dynamics structural optimization method Download PDF

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CN106202731A
CN106202731A CN201610550661.0A CN201610550661A CN106202731A CN 106202731 A CN106202731 A CN 106202731A CN 201610550661 A CN201610550661 A CN 201610550661A CN 106202731 A CN106202731 A CN 106202731A
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萧辉
杨国来
孙全兆
葛建立
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Nanjing University of Science and Technology
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Abstract

The invention discloses the multi-flexibl e dynamics structural optimization method of a kind of bridge crane, neural network model, NSGA II algorithm and the Maxi min criterion using multi-flexibl e dynamics technology, optimum Latin hypercube algorithm, particle cluster algorithm to optimize, solves the problem that when conventional multi-flexibl e dynamics optimizes, flexible body parts are difficult to optimize.Change modal neutral file information by amendment Parameters of Finite Element and change design variable value in kinetic model, introduce the BP neural network agent model that particle cluster algorithm optimizes, non-linear relation between optimization target values in each design variable of matching flexible body and multi-flexibl e dynamics model, use NSGA II genetic algorithm that this agent model carries out multiple-objection optimization and obtain Pareto disaggregation, use Max min criterion to find a feasible solution taking into account each optimization aim.

Description

Bridge crane multi-flexibl e dynamics structural optimization method
Technical field
The invention belongs to structural dynamics optimization research field, be specifically related to a kind of bridge crane multi-flexibl e dynamics knot Structure optimization method.
Background technology
Optimization of Mechanical Design be make a certain Machine Design regulation various design limit under the conditions of, decision design parameter, Make a certain or several design objectives acquisition optimal values." optimal value " or " optimum " in engineering design, refers to meeting multiple setting The most satisfactory and the optimum value obtained under the conditions of meter target and constraint.As material carrying machine is engaged in lifting, One of capital equipment of aerial carrying, bridge crane industry is also continuously improving the design mould of oneself along with the development in epoch Formula, traditional design pattern based on experience faded out the scope of design of hoisting machinery industry gradually, designers start to The direction effort of ND the Automation Design, and along with the advantage of development of computer, this Optimization Design the most slowly becomes Ripe.
Agent model is a kind of modeling method comprising the multinomial content such as EXPERIMENTAL DESIGN and approximation method in design optimization, it Overcome the problem that amount of calculation in engineering optimization is excessive, be widely used at many engineering fields.Based on Bayes just BP (Back-Propagation) neutral net then changing algorithm is made up of nonlinear transfer function neuron, uses error Back propagation is as the feedforward network of its learning algorithm.It has nonlinear ability and self-learning ability.Use this god Modeling link through network as the approximate model of agent model can the input and output of good matching agent model training sample database Relation.
Flexible multibody dynamics technology considers the plastic deformation of the components such as girder to structure dynamics compared with multi-rigid body theory The impact of response, is widely used in heavy-duty machine design and performance indications optimization thereof.But along with bridge crane performance requirement sum The raising that value simulation accuracy technology requires, existing multi-flexibl e dynamics technology is faced with higher technology on engineer applied and chooses War.But make a general survey of existing document, be related to the most little of flexible body component optimization design.Be primarily due to limited segmentation flexible body and The beformable body that software automatically generates can not meet computational accuracy requirement well, therefore soft in the dynamic system of engineer applied level Gonosome parts are typically generated by modal neutral file, describe the vibration deformation of component with hypothesis modal analysis method, therefore very Difficulty directly carries out parametric modeling as to rigid body, is difficult to directly carry out structure optimization.
Summary of the invention
It is an object of the invention to provide a kind of bridge crane multi-flexibl e dynamics structural optimization method, solve lifting The machine class engineering machinery problem that flexible member is difficult to be optimized when multi-flexibl e dynamics calculates with structural dynamics optimization.
The technical solution realizing the object of the invention is: a kind of bridge crane multi-flexibl e dynamics structure optimization side Method, method step is as follows:
Step S1, determines optimization aim according to optimization demand, and each structural parameters affecting optimization aim is carried out structure Sensitive analysis, thus choose 3-5 the design variable that impact is bigger, the span of each design variable is floated downward by its initial value Dynamic 5%～10%；According to rubber tyre gantry crane design method, bridge crane design constraint considers intensity, rigidity, technique and cross section chi Very little value retrains.
Step S2, according to the span of each design variable, uses optimum Latin hypercube algorithm to carry out design variable EXPERIMENTAL DESIGN, obtains the EXPERIMENTAL DESIGN value of some groups of design variables, thus creates the sample space of design variable.
Step S3, creates the multi-flexibl e dynamics model of bridge crane, according to often organizing the EXPERIMENTAL DESIGN value of design variable, The modal neutral file that amendment flexible body is corresponding, generates new flexible body parts and covers original flexible body parts, then by newly Multi-flexibl e dynamics model carry out numerical simulation calculating, obtain correspondence optimization target values.
Step S4, according to the EXPERIMENTAL DESIGN value often organizing design variable in step S2, obtains the sample data of agent model The input in storehouse, according to the optimization target values that the EXPERIMENTAL DESIGN value often organizing design variable in step S3 is corresponding, obtains agent model The output of sample database.
Step S5, according to input and the output of above-mentioned sample database, sets up BP based on Regularization algorithms god Through its input of network matching and the nonlinear mapping relation of outlet chamber.
Step S6, uses particle swarm optimization algorithm to optimize initial weight and the threshold value of above-mentioned BP neutral net, and carries out net Network training.
Step S7, carries out precision test to the neutral net after training:
Select multiple correlation index R2With mean square deviation MSE, model being carried out test evaluation, if meeting required precision, proceeding to Step S8, otherwise returns step S6.
Step S8, preserves the above-mentioned BP neutral net meeting required precision and structural parameters thereof.
Step S9, in conjunction with above-mentioned BP neutral net, uses NSGA-II genetic algorithm that bridge crane structure is carried out many mesh Mark optimizes, the Pareto disaggregation after being optimized.
Step S10, the Pareto disaggregation corresponding data that S9 optimizes gained preserves, and uses Max-min criterion from preservation Pareto solves to concentrate and preferably goes out a feasible solution.
Compared with prior art, its remarkable advantage is the present invention: (1) overcomes conventional flexible body can not carry out structure optimization A difficult problem.
(2) the BP neutral net agent model of Regularization algorithms is used in multi-flexibl e dynamics structure optimization, Amount of calculation is less, it is possible to achieve rapid structural optimization.
(3) NSGA-II genetic algorithm is used to carry out bridge crane structure optimization, it is to avoid conventional multiple-objection optimization weight Coefficient need to manually be preset, and premature convergence problem easily occurs in genetic algorithm.
(4) use Max-min criterion, one can be quickly found out from Pareto solution concentration and take into account the feasible of each optimization aim Solve.
Accompanying drawing explanation
Fig. 1 is the flow chart of bridge crane multi-flexibl e dynamics structural optimization method in the present invention.
Fig. 2 is the flow chart of bridge crane multi-flexibl e dynamics structural optimization method step S3 in the present invention.
Fig. 3 is the embodiment of the present invention 1 middle girder schematic cross-section.
Fig. 4 is optimization gained Pareto disaggregation scattergram in the embodiment of the present invention 1.
Detailed description of the invention
Below in conjunction with the accompanying drawings the present invention is described in further detail.
The principle of the present invention is: the flexible body used in the large-scale multi-flexibl e dynamics model of engineer applied level makes more Carry out modal calculation gained modal neutral file with finite element grid division to generate, therefore be not easy to direct parameter and structure is excellent Change.The present invention comprehensively have employed flexible multibody dynamics technology, optimum Latin hypercube algorithm, the god of particle cluster algorithm optimization Through network model, NSGA-II genetic algorithm and Maxi-min preferred criteria technology, when solving the optimization of conventional multi-flexibl e dynamics Flexible body parts are difficult to the difficult problem optimized.Change modal neutral file information by amendment Parameters of Finite Element and change kinetic simulation Design variable value in type, utilizes BP neural network agent model based on Regularization algorithms, and calculates with population Method optimizes its initial weight and threshold value to obtain preferable fitting precision, each design variable of matching flexible body and multi-flexibl e dynamics Non-linear relation between optimization target values in model, then uses NSGA-II genetic algorithm that this agent model is carried out multiple target Optimize and obtain Pareto disaggregation, use Max-min criterion to find a feasible solution taking into account each optimization aim.
A kind of bridge crane multi-flexibl e dynamics structural optimization method, method step is as follows:
Step S1, determines optimization aim according to optimization demand, and each structural parameters affecting optimization aim is carried out structure Sensitive analysis, thus choose 3-5 the design variable that impact is bigger, the span of each design variable is floated downward by its initial value Dynamic 5%～10%；According to rubber tyre gantry crane design method, bridge crane design constraint considers intensity, rigidity, technique and cross section chi Very little value retrains.
Step S2, according to the span of each design variable, uses optimum Latin hypercube algorithm to carry out design variable EXPERIMENTAL DESIGN, obtains the EXPERIMENTAL DESIGN value of some groups of (the most more have beneficially neural metwork training precision) design variables, thus Create the sample space of design variable.
Step S3, creates the multi-flexibl e dynamics model of bridge crane, according to often organizing the EXPERIMENTAL DESIGN value of design variable, The modal neutral file that amendment flexible body is corresponding, generates new flexible body parts and covers original flexible body parts, then by newly Multi-flexibl e dynamics model carry out numerical simulation calculating, obtain correspondence optimization target values:
Described step S3, specifically includes:
Step S31, uses Hyperworks software parts each to bridge crane respectively to carry out FEM meshing.
Step S32, sets up interface node in each parts and adjacent component junction and arranges boundary condition in it, checks Finite element grid quality, uses modal synthesis method that each parts carry out modal calculation respectively, obtains its modal neutral file:
In described step S32, at each interface node of each parts, boundary condition is connected with its actual in assembly Relation keeps consistent.
Step S33, imports many bodies software for calculation (such as ADAMS, RecurDyn after each modal neutral file optimization being processed Deng), establishment flexible body parts:
In described step S33, each modal neutral file is before importing multi-flexibl e dynamics model, all at Adams/Flex Carried out Advance data quality process in 2015, and obtain its flexible body quality, barycenter, rotary inertia, frequency, the vibration shape, to load Participation factors and stress-deformation characteristic information；Each modal neutral file is after importing multi-flexibl e dynamics model, according to mode tribute Offer factoring theorem, use modal parameter that former order mode state contribution factor is big to ignore remaining order mode state, create flexible body parts.
Step S34, arranges parts annexation, boundary condition according to practical situation, applies service load, thus obtains bridge The multi-flexibl e dynamics model of formula crane.
Step S35, according to often organizing the EXPERIMENTAL DESIGN value of design variable, the modal neutral file that amendment flexible body is corresponding, raw The flexible body parts of Cheng Xin cover original flexible body parts, then by each multi-flexibl e dynamics model is carried out numerical simulation meter Calculate, obtain the optimization target values of correspondence.
Step S4, according to the EXPERIMENTAL DESIGN value often organizing design variable in step S2, obtains the sample data of agent model The input in storehouse, according to the optimization target values that the EXPERIMENTAL DESIGN value often organizing design variable in step S3 is corresponding, obtains agent model The output of sample database；
Step S5, according to input and the output of above-mentioned sample database, sets up BP based on Regularization algorithms god Through its input of network matching and the nonlinear mapping relation of outlet chamber；
Step S6, uses particle swarm optimization algorithm to optimize initial weight and the threshold value of above-mentioned BP neutral net, and carries out net Network training；
Step S7, carries out precision test to the neutral net after training:
Select multiple correlation index R2With mean square deviation MSE, model being carried out test evaluation, if meeting required precision, proceeding to Step S8, otherwise returns step S6.
Step S8, preserves the above-mentioned BP neutral net meeting required precision and structural parameters thereof.
Step S9, in conjunction with above-mentioned BP neutral net, uses NSGA-II genetic algorithm that bridge crane structure is carried out many mesh Mark optimizes, the Pareto disaggregation after being optimized:
In described step S9, using NSGA-II genetic algorithm to carry out structure optimization during optimization, its Fitness analysis uses such as Fitness function shown in following formula
F i t ( f i ( x ) ) = C i , m a x - f ( x ) f i ( x ) ≤ C i , m a x 0 f i ( x ) > C i , m a x
In formula: Fit (x) represents fitness function, fiX () is each target function value, Ci,maxFor fiThe maximum estimated of (x) Value.
Described object function fiX the expression formula of () is
f i ( x ) = | F i F 0 , i |
In formula: FiFor the value of each optimization aim, F0,iInitial value for each optimization aim.
Step S10, the Pareto disaggregation corresponding data that S9 optimizes gained preserves, and uses Max-min criterion from preservation Pareto solves to concentrate and preferably goes out a feasible solution.
In described step S10, its expression formula of Max-min preferred criteria is as follows:
max [ min ( f i , max - f i , j f i , max - f i , min ) ] , i = 1 , 2 , ... , n ; j = 1 , 2 , ... , m
In formula, fi,jRepresenting that Pareto solves the value concentrating each feasible solution, its subscript i is the numbering of optimization aim, and n is for optimizing Total number of target, subscript j is that Pareto solves the numbering concentrating feasible solution, and m is the total number solved；fi,maxI-th is concentrated for solving The maximum of the feasible solution of optimization aim, fi,minFor solving the minima of the feasible solution concentrating i-th optimization aim.
Embodiment 1
In conjunction with Fig. 1 to Fig. 4, the embodiment of the present invention 1 provides a kind of bridge crane multi-flexibl e dynamics structure optimization Method, method step is as follows:
Step S1, determines optimization aim according to optimization demand, and what the design of crane was pursued is that quality is minimum, is reflected in meter Calculate on problem model is that volume is minimum, for safety when bridge machine works, it is to avoid resonance, it should make to affect bridge machine dynamic property The 5th maximum rank with there being frequency to try one's best maximum, therefore take the volume of bridge crane, the 5th rank natural frequency and maximum should Power is optimization object function.The each structural parameters affecting optimization aim are carried out STRUCTURAL SENSITIVITY ANALYSIS INDESIGN, thus chooses suitably Design variable.Grinding of the present embodiment makes internal disorder or usurp to liking bridge crane structural system, so major design variable is cutting of each girder Face size.According to STRUCTURAL SENSITIVITY ANALYSIS INDESIGN result, (wherein dimensional parameters X1 is flange plate width, X2 to select X1, X3 and X6 respectively With the thickness that X5 is respectively upper and lower cover plates, X4 and X6 is respectively web thickness and height, and X5 is two web spacing；).Respectively design change The span of amount fluctuates 5%～10% by its initial value, and its span is as follows:
X1∈[650,850]
X3∈[680,780]
X6∈[1900,2100]
The constraint of performance parameter: the present embodiment chooses the maximum of bridge crane operationally dolly span centre full load girder Hanging down the constraint as its performance parameter of displacement and maximum stress, it is the upper limit that maximum perpendicular displacement takes Allowable deflection 34.5mm, maximum Allowable stress takes 175MPa.
Step S2, in conjunction with the step S1 gained span of design variable, uses optimum Latin hypercube algorithm to design Variable carries out EXPERIMENTAL DESIGN, obtains 150 groups of design variable values, thus creates the sample space of design variable；
Step S3, creates the multi-flexibl e dynamics model of bridge crane, according to often organizing the EXPERIMENTAL DESIGN value of design variable, The modal neutral file that amendment flexible body is corresponding, the modal neutral file that amendment flexible body is corresponding generates new flexible body parts and covers Cover original flexible body parts.By new kinetic model is carried out numerical simulation calculating, obtain the optimization target values of correspondence.Should Concrete steps include:
Step S31, uses Hyperworks software parts each to bridge crane respectively to carry out FEM meshing. Especially, in order to be better described the stress distribution situation of each parts, each parts use finite element hexahedral element, and zero Each of part is all with at least 3 layers of grid.
Step S32, sets up interface node in each parts and adjacent component junction and arranges boundary condition in it, checks Finite element grid quality, uses modal synthesis method that each parts carry out modal calculation respectively, obtains its modal neutral file；
Step S33, has all carried out each modal neutral file Advance data quality process in Adams/Flex 2015, and has obtained Take its flexible body quality, barycenter, rotary inertia, frequency, the vibration shape, to the participation factors of load and stress-deformation characteristic information；Respectively Modal neutral file after importing the multi-flexibl e dynamics model set up of many bodies software for calculation ADAMS, according to modal contribution because of Son is theoretical, uses modal parameter that former order mode state contribution factor is big to ignore remaining order mode state, creates flexible body parts.
Step S34, according to parts annexation, the boundary condition of practical situation setting, applies service load, thus obtains The multi-flexibl e dynamics model of bridge crane；
Step S35, according to often organizing the EXPERIMENTAL DESIGN value of design variable, the modal neutral file that amendment flexible body is corresponding, raw The flexible body parts of Cheng Xin cover original flexible body parts, then by each multi-flexibl e dynamics model is carried out numerical simulation meter Calculate, obtain the optimization target values of correspondence.
Step S4, according to the EXPERIMENTAL DESIGN value often organizing design variable in step S2, obtains the sample data of agent model The input in storehouse, according to the optimization target values that the EXPERIMENTAL DESIGN value often organizing design variable in step S3 is corresponding, obtains agent model The output of sample database；
Step S5, according to input and the output of above-mentioned sample database, sets up BP based on Regularization algorithms god Through its input of network matching and the nonlinear mapping relation of outlet chamber；
Step S6, uses particle swarm optimization algorithm to optimize initial weight and the threshold value of above-mentioned BP neutral net, and carries out net Network training；
Step S7, carries out precision test to the neutral net after training:
Select multiple correlation index R2With mean square deviation MSE, model being carried out test evaluation, if meeting required precision, proceeding to Step S8, otherwise returns step S6；The multiple correlation index that the present embodiment obtains is respectively 0.9987,0.9876 and 0.9863, Being all higher than 0.95, mean square deviation MSE is 1.12e-5,1.16e-5 and 1.27e-5.The neural network model that this explanation is set up has good Good generalization ability and higher precision of prediction.
Step S8, preserves the above-mentioned BP neutral net meeting required precision and structural parameters thereof；
Step S9, in conjunction with above-mentioned neutral net agent model, uses NSGA-II genetic algorithm to enter bridge crane structure Row multiple-objection optimization, the Pareto disaggregation after being optimized, its distribution is as shown in Figure 4；
Step S10, the Pareto disaggregation corresponding data that S9 optimizes gained preserves.Use Max-min criterion from preservation Pareto solves to concentrate and preferably goes out a feasible solution.It is S point in Fig. 4.
Instant invention overcomes conventional flexible body and can not carry out a difficult problem for structure optimization, by the BP god of Regularization algorithms Through network agent model in multi-flexibl e dynamics structure optimization, amount of calculation is less, it is possible to achieve rapid structural optimization.

Claims (7)

1. a bridge crane multi-flexibl e dynamics structural optimization method, it is characterised in that: method step is as follows:
Step S1, determines optimization aim according to optimization demand, and each structural parameters affecting optimization aim is carried out structural sensitive Degree is analyzed, thus chooses 3-5 the design variable that impact is bigger, and the span of each design variable fluctuates by its initial value 5%～10%；According to rubber tyre gantry crane design method, bridge crane design constraint considers intensity, rigidity, technique and sectional dimension Value retrains；
Step S2, according to the span of each design variable, uses optimum Latin hypercube algorithm to test design variable Design, obtains the EXPERIMENTAL DESIGN value of some groups of design variables, thus creates the sample space of design variable；
Step S3, creates the multi-flexibl e dynamics model of bridge crane, according to often organizing the EXPERIMENTAL DESIGN value of design variable, and amendment The modal neutral file that flexible body is corresponding, generates new flexible body parts and covers original flexible body parts, then by new many Kinetics of deformable bodies model carries out numerical simulation calculating, obtains the optimization target values of correspondence；
Step S4, according to the EXPERIMENTAL DESIGN value often organizing design variable in step S2, obtains the sample database of agent model Input, according to the optimization target values that the EXPERIMENTAL DESIGN value often organizing design variable in step S3 is corresponding, obtains the sample of agent model The output of database；
Step S5, according to input and the output of above-mentioned sample database, sets up BP nerve net based on Regularization algorithms Its input of network matching and the nonlinear mapping relation of outlet chamber；
Step S6, uses particle swarm optimization algorithm to optimize initial weight and the threshold value of above-mentioned BP neutral net, and carries out network instruction Practice；
Step S7, carries out precision test to the neutral net after training:
Select multiple correlation index R2With mean square deviation MSE, model being carried out test evaluation, if meeting required precision, proceeding to step S8, otherwise returns step S6；
Step S8, preserves the above-mentioned BP neutral net meeting required precision and structural parameters thereof；
Step S9, in conjunction with above-mentioned BP neutral net, uses NSGA-II genetic algorithm that bridge crane structure is carried out multiple target excellent Change, the Pareto disaggregation after being optimized；
Step S10, the Pareto disaggregation corresponding data that S9 optimizes gained preserves, and uses Max-min criterion from preservation Pareto solves to concentrate and preferably goes out a feasible solution.
Bridge crane multi-flexibl e dynamics structural optimization method the most according to claim 1, it is characterised in that described step Rapid S3, specifically includes:
Step S31, uses Hyperworks software parts each to bridge crane respectively to carry out FEM meshing；
Step S32, sets up interface node in each parts and adjacent component junction and arranges boundary condition in it, checking limited Unit's mesh quality, uses modal synthesis method that each parts carry out modal calculation respectively, obtains its modal neutral file；
Step S33, imports many bodies software for calculation after each modal neutral file optimization being processed, create flexible body parts；
Step S34, arranges parts annexation, boundary condition according to practical situation, applies service load, thus obtains bridge-type and rise The multi-flexibl e dynamics model of heavy-duty machine；
Step S35, according to often organizing the EXPERIMENTAL DESIGN value of design variable, the modal neutral file that amendment flexible body is corresponding, generate new Flexible body parts cover original flexible body parts, then by each multi-flexibl e dynamics model is carried out numerical simulation calculating, To corresponding optimization target values.
Bridge crane multi-flexibl e dynamics structural optimization method the most according to claim 2, it is characterised in that: described step In rapid S32, at each interface node of each parts, boundary condition keeps consistent with its actual annexation in assembly.
Bridge crane multi-flexibl e dynamics structural optimization method the most according to claim 2, it is characterised in that: described step In rapid S33, each modal neutral file, before importing multi-flexibl e dynamics model, is all believed in Adams/Flex 2015 Breath optimization processes, and obtain its flexible body quality, barycenter, rotary inertia, frequency, the vibration shape, to the participation factors of load and stress Deformation characteristic information；Each modal neutral file is after importing multi-flexibl e dynamics model, according to modal contribution factoring theorem, uses Modal parameter that former order mode state contribution factors are big and ignore remaining order mode state, create flexible body parts.
Bridge crane multi-flexibl e dynamics structural optimization method the most according to claim 1, it is characterised in that: described step In rapid S9, using NSGA-II genetic algorithm to carry out structure optimization during optimization, its Fitness analysis uses the adaptation being shown below Degree function
F i t ( f i ( x ) ) = C i , m a x - f i ( x ) f i ( x ) ≤ C i , m a x 0 f i ( x ) > C i , m a x
In formula: Fit (x) represents fitness function, fiX () is each target function value, Ci,maxFor fiThe maximum estimated value of (x).
Bridge crane multi-flexibl e dynamics structural optimization method the most according to claim 5, it is characterised in that: described mesh Scalar functions fiX the expression formula of () is
f i ( x ) = | F i F 0 , i |
In formula: FiFor the value of each optimization aim, F0,iInitial value for each optimization aim.
Bridge crane multi-flexibl e dynamics structural optimization method the most according to claim 1, it is characterised in that: described step In rapid S10, its expression formula of Max-min preferred criteria is as follows:
max [ min ( f i , max - f i , j f i , max - f i , min ) ] , i = 1 , 2 , ... , n ; j = 1 , 2 , ... , m
In formula, fi,jRepresenting that Pareto solves the value concentrating each feasible solution, its subscript i is the numbering of optimization aim, and n is optimization aim Total number, subscript j be Pareto solve concentrate feasible solution numbering, m be solve total number；fi,maxI-th optimization is concentrated for solving The maximum of the feasible solution of target, fi,minFor solving the minima of the feasible solution concentrating i-th optimization aim.
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CN112307657A (en) * 2020-09-22 2021-02-02 山东大学 Superconducting magnetic resonance magnet for neonatal imaging and design method
CN112507570A (en) * 2020-12-29 2021-03-16 江西五十铃汽车有限公司 Novel BP neural network calculation method and flexibility factor optimization design method thereof
CN112580163A (en) * 2020-12-14 2021-03-30 上海交通大学 Rapid dynamics simulation implementation method for hull hoisting planning and operation training
CN113591230A (en) * 2021-09-13 2021-11-02 桂林电子科技大学 Multi-objective optimization method for commercial vehicle cab based on beam section
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102194025A (en) * 2010-03-08 2011-09-21 利弗莫尔软件技术公司 Improved method and system in engineering design optimization based on multi-objective evolutionary algorithm
CN102622459A (en) * 2011-07-15 2012-08-01 苏州纽威机床设计研究院有限公司 Flexible body dynamics analysis method of large-scale hard rail machining centre
CN102708406A (en) * 2012-05-10 2012-10-03 湖北省电力公司 Scheduling graph optimizing method based on multi-target genetic algorithm
CN104765916A (en) * 2015-03-31 2015-07-08 西南交通大学 Dynamics performance parameter optimizing method of high-speed train

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102194025A (en) * 2010-03-08 2011-09-21 利弗莫尔软件技术公司 Improved method and system in engineering design optimization based on multi-objective evolutionary algorithm
CN102622459A (en) * 2011-07-15 2012-08-01 苏州纽威机床设计研究院有限公司 Flexible body dynamics analysis method of large-scale hard rail machining centre
CN102708406A (en) * 2012-05-10 2012-10-03 湖北省电力公司 Scheduling graph optimizing method based on multi-target genetic algorithm
CN104765916A (en) * 2015-03-31 2015-07-08 西南交通大学 Dynamics performance parameter optimizing method of high-speed train

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
蔡文勇: "大口径车载火炮多柔体动力学与总体优化研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 *

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CN106681146A (en) * 2016-12-31 2017-05-17 浙江大学 Blast furnace multi-target optimization control algorithm based on BP neural network and genetic algorithm
CN108563831A (en) * 2018-03-16 2018-09-21 浙江工业大学 Optimization method for transmission precision of RV reducer
CN108563831B (en) * 2018-03-16 2019-12-24 浙江工业大学 Optimization method for transmission precision of RV reducer
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CN110866333A (en) * 2019-10-31 2020-03-06 广东志高暖通设备股份有限公司 Optimal design method for grid structure arranged in rectangular pipeline
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CN112507570A (en) * 2020-12-29 2021-03-16 江西五十铃汽车有限公司 Novel BP neural network calculation method and flexibility factor optimization design method thereof
CN113591230A (en) * 2021-09-13 2021-11-02 桂林电子科技大学 Multi-objective optimization method for commercial vehicle cab based on beam section
CN113591230B (en) * 2021-09-13 2023-05-23 桂林电子科技大学 Multi-objective optimization method for commercial vehicle cab based on beam section
CN114048632A (en) * 2021-12-15 2022-02-15 上海市特种设备监督检验技术研究院 Dynamic characteristic analysis method of bridge crane hoisting mechanism based on ADAMS
CN114117871A (en) * 2021-12-24 2022-03-01 昆明理工大学 Beam unit structure optimization method based on PSO-BP neural network
CN116541975A (en) * 2023-04-11 2023-08-04 哈尔滨工业大学 Dynamic optimization design method for nuclear power tower crane structural system based on proxy model
CN116541975B (en) * 2023-04-11 2024-01-30 哈尔滨工业大学 Dynamic optimization design method for nuclear power tower crane structural system based on proxy model
CN116384257A (en) * 2023-05-29 2023-07-04 浙江大学 Method for predicting assembly errors and optimizing tolerance of air separation integral cold box
CN116384257B (en) * 2023-05-29 2023-09-29 浙江大学 Method for predicting assembly errors and optimizing tolerance of air separation integral cold box
CN116976014A (en) * 2023-06-13 2023-10-31 江苏省特种设备安全监督检验研究院 Crane design optimization method and system based on performance check
CN116976014B (en) * 2023-06-13 2024-03-22 江苏省特种设备安全监督检验研究院 Crane design optimization method and system based on performance check
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