CN105653768A - Particle swarm optimization algorithm based lightweight car body structure implementation method - Google Patents
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Abstract
Provided is a particle swarm optimization algorithm based lightweight car body structure implementation method. The method comprises: by carrying out high-precision finite element modeling and collision condition simulation analysis on a specified car body, carrying out sampling on a obtained 40% frontal offset collision simulation model in a optimization problem design domain, establishing a Kriging approximation model, and using a deterministic coefficient to carry out precision verification on the Kriging approximation model, so as to obtain a high-precision Kriging approximation model; and identifying a constraint domain with a lower violation degree by using a data mining technology, obtaining an optimization result by using a particle swarm optimization algorithm, rounding the optimization result to a project value, and verifying whether the project value meets a collision condition requirement through finite element simulation calculation, so as to obtain an optimized lightweight car body design structure of collision condition. The method disclosed by the present invention operates efficiently, and can provide a high-precision lightweight body design approximation model.
Description
Technical field
What the present invention relates to is the technology of a kind of coachbuilt body field of structural design, specifically the implementation method of a kind of coachbuilt body lightweight structure based on particle swarm optimization algorithm.
Background technology
Day by day serious along with global energy shortage and environmental pollution, safety, energy-saving and environmental protection become the theme of current automotive industry. According to statistics, automobile often subtracts heavy by 10%, and oil consumption can reduce by 6��8%, and quantity discharged reduces 5��6%. Body lightening design becomes the important channel of energy-saving and emission-reduction.
Further, vehicle safety is most important to reduction traffic accident mortality ratio, and crashworthiness is the important consideration factor of Modern Automobile Body design. Coachbuilt body structure light-weight design based on collision performance of operating condition index is the important directions that Hyundai Motor designs.
Considering collision operating mode simulation time cost height, in the vehicle body optimization design based on collision performance of operating condition index, approximate model is often used for substituting realistic model, and then performs computation optimization. But owing to car collision problem nonlinear degree is strong and problem dimension height, optimize the selection of algorithm and utilization reaches to optimize one of important factor in order of solving. Particle cluster algorithm is that R.Eberhart and J.Kennedy proposes in the meeting " TheSixthInternationalSymposiumonMicroMachineandHumanScie nce " of nineteen ninety-five, because of its relatively simple algorithm thought, algorithm convergence efficiency and relatively powerful computation optimization ability fast, particle swarm optimization algorithm is widely used in automotive industry.
Although particle swarm optimization algorithm has stronger optimizing ability, similar with evolution algorithm, its optimizing process is subject to the impact of Premature Convergence problem. Through the retrieval of prior art literature being found, between particle population individuality, the reduction of dispersion degree affects it to restrain one of too fast important factor. Along with the carrying out of optimizing process, the local best points that population is sought to the present age gradually is concentrated, and reduces particle cluster algorithm ability of searching optimum, and then causes the generation of Premature Convergence. Ratnaweera etc. use velocity jump pointer to increase the dispersion degree between a new generation population position " IEEETransactiononEvolutionaryComputation " 8 interim propositions in 2004;Sun etc. propose to use sudden change pointer to increase the diversity of particle population in optimizing process in meeting " InternationalConferenceonSimulatedEvolutionandLearning " for 2006, when dispersion degree between particle is reduced to certain threshold value, activated mutant pointer, increases the diversity between particle; Zhao etc. propose to use disturbance update strategy for Premature Convergence problem periodical " AppliedSoftComputing " 2010 10 is interim, change the position that the overall situation is separated, promote the diversity degree of population. Yildiz etc., in periodical " JournalofAutomobileEngineering " 226 interim exploitation Hybrid Particle Swarm in 2012, use receptor editing mechanism in immune algorithm to increase the diversity between population; Tang etc. proposed to use self-adaptation diversity to keep mechanism and elite's learning strategy to promote the diversity between population in periodical " EngineeringApplicationofArtificialIntelligence " 37 phases in 2015.
Diversity of particle swarm is one of important factor affecting algorithm optimization convergence process, how in searching process, rationally to ensure the dispersion degree between particle population, it it is the important channel alleviating particle swarm optimization algorithm Premature Convergence problem, for the body lightening design problem considering collision problem, improve while how keeping population diversity in searching process and optimize efficiency and precision, be the key that particle swarm optimization algorithm is applied in engineering reality.
Kriging model is a kind of unbiased esti-mator model, efficiency height, and response prediction speed is fast, repeatable high, is widely used in Engineering Structure Optimum designs.
Through the retrieval of prior art is found, Chinese patent literature CN103699938A, publication date 2014.4.2, disclose a kind of power system generation schedule formulating method containing pumped storage station, comprise the following steps: first formulate turnaround plans, obtain power system load curve; Then when balancing the load, unit output and climbing restriction, system rotate for subsequent use and pumped storage station capacity constrain, within the cycle of dispatching, the cost of electricity-generating of all fired power generating unit and switching cost sum are minimum as target Modling model, solve with the basic particle group algorithm of chaos controlling, export global extremum (scheduling scheme) and corresponding target function value. But this technology adopts the basic algorithm of population, export after only iteration number of times being judged in an iterative process, the impact on population overall situation optimal location under stagnant condition cannot be solved, there is certain deviation.
Summary of the invention
The present invention is directed to prior art above shortcomings, the implementation method of a kind of coachbuilt body lightweight structure based on particle swarm optimization algorithm is proposed, formula is reset in conjunction with the distribution of particle initial population and speed, optimize particle cluster algorithm, it is applied to coachbuilt body light-weight design, effectively optimizing solves, and overcomes collision operating mode strong nonlinearity and the high-dimensional optimization difficulty brought, it is to increase the optimization efficiency of body lightening design problem.
The present invention is achieved by the following technical solutions:
The present invention by carrying out high precision finite element modeling and collision operating mode simulation analysis to the coachbuilt body specified, the 40% Frontal offset impact realistic model obtained is sampled in optimization problem design domain, set up Kriging approximate model and adopt deterministic coefficient to carry out precision test, obtain high precision Kriging approximate model;The lower constraint territory of degree is violated by data mining technology identification, setting initial population also runs particle swarm optimization algorithm based on initial population, obtain optimum result, circle is whole to engineering value, verify whether meet collision working condition requirement by finite element stimulation, the collision operating mode vehicle body light-weight design structure after being optimized.
Described simulation analysis refers to: according to safety requirements, chooses performance index and the design variable of collision operating mode, and setting simulation time, obtains 40% Frontal offset impact realistic model.
Described performance index comprise: the maximum backward distortion amount of the left side maximum acceleration of B post, A post, the maximum intrusion volume of left side toe board and the maximum intrusion volume of right side toe board.
Described design variable be 21 thick with 21 plates of 40% Frontal offset impact maximally related vehicle body front portion.
Described sampling adopts optimum Latin hypercube experimental design method.
Described determinacy coefficients R2Calculation formula be:Wherein: yiFor the FEM (finite element) calculation value of sample point,For sample point Kriging approximate model predictor,For all sample points emulate the mean value of result, n is the sample points of inspection.
Described precision test refers to: be reconstructed by Kriging approximate model for improving deterministic coefficient.
Described reconstruct refers to: adopts and improves criterion (ExpectedImprovedCriterion based on expectation, EI) sequential sampling method, expectation corresponding to each performance index of computation optimization improves functional value, to expect that the maximum of points improving function is as newly-increased sample point, it is to construct high precision Kriging approximate model.
Described expectation improves the calculation formula of function EI (x):
Wherein: yminFor the minimum value of response in sample,It is respectively probability density function and the cumulative distribution function of standardized normal distribution with �� (x),The prediction average of Kriging approximate model at x point place and prediction square root of the variance it is respectively with �� (x).
Described particle swarm optimization algorithm comprises:
Step a) arranges algorithm parameter, produces the initial population of particle swarm optimization algorithm: part initial population uses optimum Latin Hypercube Sampling method to result from whole design domain; Another part results from the lower region of the constraint violation degree of data mining technology institute identification, and two portions quantity is identical.
Step b) calculate target function value corresponding to population, sort according to optimization aim, select overall situation optimal location and local optimal location; And speed more new formula and the location updating formula according to standard particle group's algorithm, all particles are carried out speed renewal and location updating.
Step c) if particle swarm optimization algorithm flow process is stagnated more than 5 generations at fitness value place, then the position of this place's fitness value is carried out speed and recalculate, thus change particle position.
Described speed recalculates and refers to: vmutation=�� rw vrand, wherein: xregbest=xgbest+vreset, vrandResult from the velocity range [-v set in advance at randommax,vmax], �� is the numerical value linearly reduced along with the increase of algebraically, itermaxFor the maximum algebraically set in advance, itercurrentFor current algebraically, rvFor the coefficient relevant to speed.
Steps d) according to the stopping criterion setting in advance, judge whether optimizing process reaches stopping requirement, if do not reached, then continue circulation and upgrade velocity of particle and position; Otherwise exit optimizing process and export optimum result.
Technique effect
Compared with prior art, the present invention: 1) by using data mining technology to carry out colliding the constraint territory identification of operating mode problem, more multiparticle is instructed to be distributed in the lower region of constraint violation degree, improve the ability that particle cluster algorithm solves constrained optimization problem, and then obtain the vehicle body structure light-weight design result of more excellent collision operating mode constraint;2) for the problem of particle cluster algorithm Premature Convergence, propose the adaptive speed based on stagnating judgment criterion and reset formula, in optimizing process, when particle cluster algorithm because population diversity reduces during local convergence, rate of activation resets formula, increase the diversity between particle, it is to increase the global optimization ability of particle cluster algorithm; 3) for collision operating mode nonlinear degree height, the feature that optimization design variable is many, approximate modeling technique is combined with improve PSO algorithm, optimizing process is reduced consuming time by approximate modeling technology, use the excavation of constraint territory and adaptive speed replacement pointer to promote particle cluster algorithm and optimize ability, body lightening for realizing considering under collision operating mode designs the optimization design flow process providing a set of high-accuracy high-efficiency, and applicability is strong.
Accompanying drawing explanation
Fig. 1 is schematic diagram of the present invention;
Fig. 2 is whole car finite element model schematic diagram;
Fig. 3 is 40% biased realistic model schematic diagram;
Fig. 4 is design variable schematic diagram;
Fig. 5 is constraint territory classification schematic diagram.
Embodiment
Doing embodiments of the invention to illustrate in detail below, the present embodiment is implemented under premised on technical solution of the present invention, gives detailed enforcement mode and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
Embodiment 1
As shown in Figure 1, the present embodiment comprises the following steps:
Step 1, as shown in Figure 2, Hypermesh and Primer is adopted to be pre-processing software platform, LS-DYNA is solver, and the coachbuilt body specified carries out high precision vehicle body finite element modeling, DOE test design and simulation analysis, obtains whole car finite element model and 40% offset collision realistic model.
Described whole car finite element model comprises 940224 unit, 892192 nodes, wherein: 940224 unit comprise 855608 face unit, 12793 hexahedron solid elements, 502 tetrahedral solid elements and 11850 beam beam elements, 855608 face unit have 12887 (1.5%) individual triangular elements, proves that whole car finite element model precision is reliable.
As shown in Figure 3; described simulation analysis refers to: according to the requirement of " passenger protection of passenger car Frontal offset impact " (GB T-20913-2003); in the offset collision of front 40%, the 40% of vehicle body width collides with the speed of 56km/h and deformable obstacle; choose performance index and the design variable of collision operating mode; setting simulation time, obtains 40% offset collision realistic model.
As shown in table 1, described performance index comprise: the maximum backward distortion amount of the left side maximum acceleration of B post, A post, the maximum intrusion volume of left side toe board and the maximum intrusion volume of right side toe board.
As shown in Figure 4, described design variable be 21 thick with 21 plates of 40% offset collision maximally related vehicle body front portion.
Table 1 collides performance index and the design variable of operating mode
Described simulation time is 120ms.
Step 2,40% offset collision realistic model previous step obtained are sampled in optimization problem design domain, and the sample points obtained by simulation calculation is according to setting up Kriging approximate model, and adopts deterministic coefficient R2Evaluate its prediction precision and obtain high precision Kriging approximate model to reconstruct.
Described sampling adopts optimum Latin hypercube experimental design method.
Described determinacy coefficients R2Calculation formula be:Wherein: yiFor the FEM (finite element) calculation value of sample point,For sample point Kriging approximate model predictor,For all sample points emulate the mean value of result, n is the sample points of inspection.
Described reconstruct refers to: adopts and improves criterion (ExpectedImprovedCriterion based on expectation, EI) sequential sampling method, expectation corresponding to each performance index of computation optimization improves functional value, to expect that the maximum of points improving function is as newly-increased sample point, it is to construct high precision Kriging approximate model.
Described expectation improves the calculation formula of function EI (x):
Wherein: yminFor the minimum value of response in sample,It is respectively probability density function and the cumulative distribution function of standardized normal distribution with �� (x),The prediction average of Kriging approximate model at x point place and prediction square root of the variance it is respectively with �� (x).
Described reconstruct can improve the prediction precision of Kriging approximate model, affects the result of optimization design.
Described deterministic coefficient is more big, and the precision of Kriging approximate model is more high, as deterministic coefficient R2Reach more than 0.85, it will be recognized that the precision of approximate model meets the requirement optimized further.
The sample number that the approximate model of the present embodiment is corresponding and precision index are as shown in table 2:
Table 2 approximate model precision
Step 3, by the lower region of classification and regression tree (CART) data mining technology identification constraint violation degree, it is optimized variable by particle cluster algorithm, specifically comprises:
Step 3.1) the high precision Kriging approximate model that obtains based on previous step, formation problem constraint territory judges function, use optimum Latin hypercube experimental design method to sample in optimization problem design domain, calculate constraint territory and judge that function exports.
Described constraint territory judges function YjudgeCalculation formula be:ifCi��0,fi=0elsefi=Ci, wherein: CiFor constraint function, nc is constraint function number.
The present embodiment is 40 for the sampling point number of constraint territory decision process.
Step 3.2) by classification and regression tree (CART), the sample point (particle) for constraint territory decision process is judged and classifies, result is as shown in Figure 5.
Described judgement and classification refer to: according to judgement function, the region that constraint violation is little, it judges that functional value is less, the mean value of functional value is then judged according to the constraint territory corresponding to 40 sampling points, each dimension degree of each sample point is classified based on judge criterion, constraint territory corresponding to 40 sampling points is judged, and particle class declaration that functional value is greater than mean value is as 1, is judged to " vacation " a class, the design domain residing for it is the region that constraint violation degree is bigger; The particle class declaration being less than mean value is 0, is judged to " very " a class, the design domain residing for it is the region that constraint violation degree is less.
Described judge criterion is:Wherein: xcjFor design variable,For classification value selected in design domain.
The described index judged is as Gini impurity level index.
Step 3.3) particle cluster algorithm of sample points certificate is optimized, be optimized result, specifically comprises:
Step 3.3.1) as shown in table 3, algorithm parameter is set, produces the particle swarm optimization algorithm initial population for quality minimum optimization problem: part initial population uses optimum Latin Hypercube Sampling method to result from whole design domain, and quantity is 15; Another part results from the lower region of the constraint violation degree of data mining technology institute identification, and quantity is 15.
Table 3 algorithm parameter
Step 3.3.2) calculate target function value corresponding to population, sort according to optimization aim, select overall situation optimal location and local optimal location;And speed more new formula and the location updating formula according to standard particle group's algorithm, all particles are carried out speed renewal and location updating.
Step 3.3.3) if particle swarm optimization algorithm flow process is stagnated more than 5 generations at fitness value place, then activate adaptive speed and reset formula, the position of this place's fitness value is carried out speed and recalculates, thus change particle position.
Described adaptive speed resets formula:
vmutation=�� rw vrand
, xregbest=xgbest+vreset, wherein: vrandRandom product
It is born in the velocity range [-v set in advancemax,vmax], �� is the numerical value linearly reduced along with the increase of algebraically, itermaxFor the maximum algebraically set in advance, itercurrentFor current algebraically, rvFor the coefficient relevant to speed.
R in the present embodimentvmaxIt is 0.9, rvminIt is 0.1.
Step 3.3.4) according to the stopping criterion setting in advance, judge whether optimizing process reaches stopping requirement, if do not reached, then continue circulation and upgrade velocity of particle and position; Otherwise exit optimizing process and export optimum result.
By repeatedly repeating, the minimum optimizing process of quality objectives reduces calculating randomness to the present embodiment, obtaining the vehicle body front structure quality after carrying out particle swarm optimization algorithm is 46.4kg, and the quality of ordinary particle group's algorithm is 48.6kg, it is seen that particle swarm optimization algorithm consider collision index body lightening solve in advantageously.
Step 4, the optimum result obtained according to step 3, by whole for optimization variable circle to engineering value, verify whether meet collision working condition requirement by finite element stimulation, and the collision operating mode vehicle body light-weight design structure after being optimized, as shown in table 4 and table 5.
Table 4 design variable optimum result
| X1 | X2 | X3 | X4 | X5 | X6 | X7 |
| 1.30 | 1.37 | 0.60 | 0.73 | 0.70 | 0.78 | 0.75 |
| X8 | X9 | X10 | X11 | X12 | X13 | X14 |
| 0.60 | 1.12 | 1.74 | 0.60 | 1.20 | 0.60 | 0.99 |
| X15 | X16 | X17 | X18 | X19 | X20 | X21 |
| 0.60 | 0.81 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 |
Table 5 design variable optimization is compared
Described optimum Latin hypercube experimental design method by people such as JinR. in " JournalofStatisticalPlanningandInference " of 134 phases in 2005 " Anefficientalgorithmforconstructingoptimaldesignofcomput erexperiments " (page268-287) a literary composition proposes, compared with traditional Latin Hypercube Sampling method, the sampling point distributions obtained has more regularity, can evenly be covered with whole design domain.
Claims (7)
1. the implementation method based on the coachbuilt body lightweight structure of particle swarm optimization algorithm, it is characterized in that, by the coachbuilt body specified being carried out high precision finite element modeling and collision operating mode simulation analysis, the 40% Frontal offset impact realistic model obtained is sampled in optimization problem design domain, set up Kriging approximate model and adopt deterministic coefficient to carry out precision test, obtain high precision Kriging approximate model; The lower constraint territory of degree is violated by data mining technology identification, setting initial population also runs particle swarm optimization algorithm based on initial population, obtain optimum result, circle is whole to engineering value, by verifying whether meet collision working condition requirement by finite element stimulation, the collision operating mode vehicle body light-weight design structure after being optimized.
2. implementation method according to claim 1, is characterized in that, described simulation analysis refers to: according to safety requirements, chooses performance index and the design variable of collision operating mode, and setting simulation time, obtains 40% Frontal offset impact realistic model;
Described performance index comprise: the maximum backward distortion amount of the left side maximum acceleration of B post, A post, the maximum intrusion volume of left side toe board and the maximum intrusion volume of right side toe board;
Described design variable be 21 thick with 21 plates of 40% Frontal offset impact maximally related vehicle body front portion.
3. implementation method according to claim 1, is characterized in that, described determinacy coefficients R2Calculation formula be:Wherein: yiFor the FEM (finite element) calculation value of sample point,For sample point Kriging approximate model predictor,For all sample points emulate the mean value of result, n is the sample points of inspection.
4. implementation method according to claim 1, is characterized in that, described precision test refers to: be reconstructed by Kriging approximate model for improving deterministic coefficient;
Described reconstruct refers to: adopt based on the sequential sampling method expecting to improve criterion, and the expectation calculating each performance index improves function, to expect that the maximum of points improving function is as newly-increased sample point, it is to construct high precision Kriging approximate model.
5. implementation method according to claim 4, is characterized in that, described expectation improves function and is:
Wherein: yminFor the minimum value of response in sample,It is respectively probability density function and the cumulative distribution function of standardized normal distribution with �� (x),The prediction average of Kriging approximate model at x point place and prediction square root of the variance it is respectively with �� (x).
6. implementation method according to claim 1, is characterized in that, described particle swarm optimization algorithm comprises:
Step a) arranges algorithm parameter, produces the initial population of particle swarm optimization algorithm: part initial population uses optimum Latin Hypercube Sampling method to result from whole design domain; Another part results from the lower region of the constraint violation degree of data mining technology institute identification, and two portions quantity is identical;
Step b) calculate target function value corresponding to population, sort according to optimization aim, select overall situation optimal location and local optimal location; And speed more new formula and the location updating formula according to standard particle group's algorithm, all particles are carried out speed renewal and location updating;
Step c) if particle swarm optimization algorithm flow process is stagnated more than 5 generations at fitness value place, then the position of this place's fitness value is carried out speed and recalculate, thus change particle position;
Steps d) according to the stopping criterion setting in advance, judge whether optimizing process reaches stopping requirement, if do not reached, then continue circulation and upgrade velocity of particle and position; Otherwise exit optimizing process and export optimum result.
7. implementation method according to claim 6, is characterized in that, described speed recalculates and refers to: vmutation=�� rw vrand, wherein: xregbest=xgbest+vreset, vrandResult from the velocity range [-v set in advance at randommax,vmax], �� is the numerical value linearly reduced along with the increase of algebraically, itermaxFor the maximum algebraically set in advance, itercurrentFor current algebraically, rvFor the coefficient relevant to speed.
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