CN114492195A - CAE model multi-parameter intelligent correction calculation method based on optimization algorithm - Google Patents
CAE model multi-parameter intelligent correction calculation method based on optimization algorithm Download PDFInfo
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- CN114492195A CN114492195A CN202210129575.8A CN202210129575A CN114492195A CN 114492195 A CN114492195 A CN 114492195A CN 202210129575 A CN202210129575 A CN 202210129575A CN 114492195 A CN114492195 A CN 114492195A
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Abstract
The invention belongs to the technical field of CAE model optimization, and particularly relates to a CAE model multi-parameter intelligent correction calculation method based on an optimization algorithm, which adopts the combination of an improved genetic algorithm and an annealing algorithm to enhance the global part optimization capability of a model; the method specifically comprises the following steps: inputting design parameters, obtaining physical property parameters and actual measurement parameters required by the CAE model through measurement and calculation, and inputting the physical property parameters and the actual measurement parameters into the CAE model; correcting and calculating model parameters, acquiring an optimal initial value by using a genetic algorithm, and finding a global optimal solution according to the initial value by using an annealing algorithm; and (5) correcting the model parameters, and sequentially replacing the parameters of the original model with the optimal solution of each parameter to form a high-precision CAE simulation model. The method overcomes the defects of the prior art, and can effectively find the corrected global optimal solution in a short time by adopting a genetic algorithm in the aspect of initialization of a correction algorithm on the basis of an annealing algorithm.
Description
Technical Field
The invention belongs to the technical field of CAE model optimization, and particularly relates to a CAE model multi-parameter intelligent correction calculation method based on an optimization algorithm.
Background
At present, industrial enterprises make great progress in measuring main parameters of the CAE model, but partial parameters still cannot be measured or cannot be accurately measured, and the accuracy and the applicability of the CAE model are obviously influenced. The parameters are basically strong nonlinear parameters, and the phenomenon that parameter values are discontinuous along with the change of conditions exists.
The traditional manual correction method has the advantages of long period, poor accuracy and very low universality; therefore, the CAE model multi-parameter intelligent correction calculation method can be developed to effectively solve the problems and greatly improve the correction efficiency and accuracy of CAE model parameters and coefficients. The algorithm also has excellent expansibility, and can be popularized to be a general calculation method for CAE model parameter correction through application in various fields.
CAE software developers all have parameter correction algorithms in European and American products including ANSYS and Comsol, and the algorithms are based on single-parameter correction methods and have the problem of low correction efficiency.
At present, no particularly effective algorithm exists in the field of high-efficiency multi-parameter intelligent correction algorithms. The CAE model multi-parameter intelligent correction calculation method based on the optimization algorithm breaks through the blank of the field.
Disclosure of Invention
The invention aims to provide a CAE model multi-parameter intelligent correction calculation method based on an optimization algorithm, which overcomes the defects of the prior art, is based on an annealing algorithm, adopts a genetic algorithm in the aspect of initialization of the correction algorithm, and can effectively find a corrected global optimal solution in a short time.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
a CAE model multi-parameter intelligent correction calculation method based on an optimization algorithm adopts the combination of an improved genetic algorithm and an annealing algorithm to enhance the global part optimization capability of the model; the method specifically comprises the following steps:
step one, inputting design parameters: obtaining physical property parameters and actual measurement parameters required by the CAE model through measurement and calculation, and inputting the physical property parameters and the actual measurement parameters into the CAE model;
step two, model parameter correction calculation: acquiring an optimal initial value by using a genetic algorithm, and finding a global optimal solution according to the initial value by using an annealing algorithm;
step three, correcting the model parameter result: and sequentially replacing the parameters of the original model with the optimal solution of each parameter to form a high-precision CAE simulation model.
Further, in the first step, the physical property parameters and actual measurement parameters required by the CAE model are obtained through measurement and calculation, and the specific method includes: extreme condition testing, model calculation, literature queries, and parameter combinations.
Further, the genetic algorithm in the second step comprises the following specific steps:
1) initialization: setting an evolution algebra counter T to be 0, setting a maximum evolution algebra T, and randomly generating M individuals as an initial population P (0);
2) calculating the fitness of individuals in the population P (t);
3) and (3) acting a selection operator on the population, evaluating the fitness of the individuals in the population, and simultaneously directly inheriting the optimized individuals to the next generation or generating new individuals through pairing crossing and then inheriting the new individuals to the next generation.
4) Applying a crossover operator to the population;
5) acting a mutation operator on the population, and obtaining a next generation population P (t +1) after selection, intersection and mutation operations of the population P (t);
6) and (4) judging termination conditions: and if T is equal to T, outputting the individual with the maximum fitness obtained in the evolution process as the optimal solution, and stopping the calculation.
Further, the annealing algorithm in the second step specifically comprises the following steps:
1) obtaining an optimal initial solution obtained by a genetic algorithm;
2) randomly generating a new solution T on the basis of the initial solution;
3) constructing a fitness evaluation function, evaluating a new solution T by using the evaluation function, and if the new solution is better evaluated, accepting the new solution;
4) if the new solution evaluation is not good enough, accepting the new solution with a certain probability p;
5) and repeating the steps 2-4 until the obtained solution error is minimum.
Further, constructing a fitness evaluation function in the step 3), which is expressed as:
wherein, Target is the Target error,
SST (residual sum of squares), i.e. the sum of squares of the differences between the original data and the mean, expressed as
Wherein wiIs a weight, p _ caliTo calculate the value, p _ testiIs an actual measurement value.
Further, the method also comprises the verification of the high-precision CAE simulation model, and the specific method comprises the following steps: and comparing the calculation result of the corrected CAE simulation model with the result obtained by the experiment to determine the error of the CAE simulation model.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention is based on the annealing algorithm, and adopts the genetic algorithm in the aspect of the initialization of the correction algorithm, so that the corrected global optimal solution can be effectively found in a short time.
2. The invention classifies two types of parameters by analyzing the parameters to be corrected of the CAE model, considers the order of parameter correction in the correction algorithm, greatly improves the multi-parameter correction capability, and can correct more than ten parameters at one time.
Drawings
FIG. 1 is a flow chart of a CAE model multi-parameter intelligent correction calculation method.
Fig. 2 is a schematic flow chart of an annealing algorithm.
FIG. 3 is a schematic flow chart of a genetic algorithm.
FIG. 4 is a graph comparing experimental voltage and CAE model simulation voltage.
FIG. 5 is a graph comparing experimental current with CAE model simulation current.
FIG. 6 shows the result of parameters corrected by the CAE simulation model.
FIG. 7 is a comparison graph of CAE simulation model result verification.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIGS. 1-3, the CAE model multi-parameter intelligent correction calculation method based on the optimization algorithm of the invention adopts the combination of the improved genetic algorithm and the annealing algorithm to enhance the global part optimization capability of the model; the method specifically comprises the following steps:
step one, inputting design parameters: obtaining physical property parameters and actual measurement parameters required by the CAE model through measurement and calculation, and inputting the physical property parameters and the actual measurement parameters into the CAE model;
step two, model parameter correction calculation: acquiring an optimal initial value by using a genetic algorithm, and finding a global optimal solution according to the initial value by using an annealing algorithm;
step three, correcting the model parameter result: and sequentially replacing the parameters of the original model with the optimal solution of each parameter to form a high-precision CAE simulation model.
Further, in the first step, the physical property parameters and actual measurement parameters required by the CAE model are obtained through measurement and calculation, and the specific mode comprises the following steps: extreme condition testing, model calculation, literature query, and parameter combination.
Further, the genetic algorithm in the second step comprises the following specific steps:
1) initialization: setting an evolution algebra counter T to be 0, setting a maximum evolution algebra T, and randomly generating M individuals as an initial population P (0);
2) calculating the fitness of individuals in the population P (t);
3) and (3) acting a selection operator on the population, evaluating the fitness of the individuals in the population, and simultaneously directly inheriting the optimized individuals to the next generation or generating new individuals through pairing and crossing and then inheriting the new individuals to the next generation.
4) Applying a crossover operator to the population;
5) acting a mutation operator on the population, and obtaining a next generation population P (t +1) after selection, intersection and mutation operations of the population P (t);
6) and (4) judging termination conditions: and if T is equal to T, outputting the individual with the maximum fitness obtained in the evolution process as the optimal solution, and stopping the calculation.
Further, the annealing algorithm in the second step specifically comprises the following steps:
1) obtaining an optimal initial solution obtained by a genetic algorithm;
2) randomly generating a new solution T on the basis of the initial solution;
3) constructing a fitness evaluation function, evaluating a new solution T by using the evaluation function, and if the new solution is better evaluated, accepting the new solution;
4) if the new solution evaluation is not good enough, accepting the new solution with a certain probability p;
5) and repeating the steps 2-4 until the obtained solution error is minimum.
Further, constructing a fitness evaluation function in the step 3), wherein the fitness evaluation function is represented as:
wherein, Target is the Target error,
SST (residual sum of squares), i.e. the sum of squares of the differences between the original data and the mean, expressed as
Wherein wiAs a weight, p _ caliTo calculate the value, p _ testiIs an actual measurement value.
Further, the method also comprises the verification of the high-precision CAE simulation model, and the specific method comprises the following steps: and comparing the calculation result of the corrected CAE simulation model with the result obtained by the experiment to determine the error of the CAE simulation model.
Demonstration example:
taking a lithium battery as an example, the main implementation mode comprises three parts of macroscopic actual measurement data input, CAE model parameter correction, CAE model verification and the like, and the three parts are explained in detail below.
(1) Design parameter input
For a general CAE model, the design parameter data is directly obtained and comprises macroscopic data such as pressure, temperature, flow, voltage, resistance and the like. These data are satisfactory whether measured by laboratory or by real-time acquisition of current measurement techniques. The design parameters entered are shown in the following table:
the design parameters are data bases of CAE modeling, physical parameters of the CAE model are provided, and data are provided for model parameter correction calculation of the next step by combining a small amount of measured data.
(2) Model parameter correction calculation
The purpose of the step is to establish a lithium battery simulation model, correct battery model parameters by a small amount of experimental data, establish a reliable simulation model, and simultaneously compare performance performances of different batteries through simulation research.
Fig. 4 and 5 are graphs illustrating correction of the CAE model through input experimental voltage and current data, and it can be seen from the graphs that the accuracy of the correction algorithm of the present application is very high, and the error can be controlled within 1%.
And the model parameter correction obtains the macroscopic parameters of the targets, and provides precision guarantee for the next model parameter correction result.
(3) Correction results of model parameters
FIG. 6 shows the model's corrected optimization interval and the corrected results.
The corrected model parameters replace the parameters of the original model to form a high-precision CAE simulation model for the next CAE model application and verification.
(4) Post-correction CAE model application validation
The corrected CAE model calculation and experimental result calibration is shown in FIG. 7, the real-time voltage data prediction is compared, and it can be seen from FIG. 7 that the corrected CAE model and the measured data are basically overlapped, and the error is controlled to be below 1%.
The final model application and the calibration of the actual measurement result prove the precision of the method.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A CAE model multi-parameter intelligent correction calculation method based on an optimization algorithm is characterized by comprising the following steps: the global optimization capability of the model is enhanced by combining an improved genetic algorithm and an annealing algorithm; the method specifically comprises the following steps:
step one, inputting design parameters: obtaining physical property parameters and actual measurement parameters required by the CAE model through measurement and calculation, and inputting the physical property parameters and the actual measurement parameters into the CAE model;
step two, model parameter correction calculation: acquiring an optimal initial value by using a genetic algorithm, and finding a global optimal solution according to the initial value by using an annealing algorithm;
step three, correcting the model parameter result: and sequentially replacing the parameters of the original model with the optimal solution of each parameter to form a high-precision CAE simulation model.
2. The CAE model multi-parameter intelligent correction calculation method based on the optimization algorithm according to claim 1, characterized in that: in the first step, the physical property parameters and actual measurement parameters required by the CAE model are obtained through measurement and calculation, and the specific mode comprises the following steps: extreme condition testing, model calculation, literature query, and parameter combination.
3. The CAE model multi-parameter intelligent correction calculation method based on the optimization algorithm according to claim 1, characterized in that: the genetic algorithm in the second step comprises the following specific steps:
1) initialization: setting an evolution algebra counter T to be 0, setting a maximum evolution algebra T, and randomly generating M individuals as an initial population P (0);
2) calculating the fitness of individuals in the population P (t);
3) and (3) acting a selection operator on the population, evaluating the fitness of the individuals in the population, and simultaneously directly inheriting the optimized individuals to the next generation or generating new individuals through pairing and crossing and then inheriting the new individuals to the next generation.
4) Applying a crossover operator to the population;
5) acting a mutation operator on the population, and obtaining a next generation population P (t +1) after selection, intersection and mutation operations of the population P (t);
6) and (4) judging termination conditions: and if T is equal to T, outputting the individual with the maximum fitness obtained in the evolution process as the optimal solution, and stopping the calculation.
4. The CAE model multi-parameter intelligent correction calculation method based on the optimization algorithm according to claim 3, characterized in that: the annealing algorithm in the second step comprises the following specific steps:
1) obtaining an optimal initial solution obtained by a genetic algorithm;
2) randomly generating a new solution T on the basis of the initial solution;
3) constructing a fitness evaluation function, evaluating a new solution T by using the evaluation function, and if the new solution is better evaluated, accepting the new solution;
4) if the new solution evaluation is not good enough, accepting the new solution with a certain probability p;
5) and repeating the steps 2-4 until the obtained solution error is minimum.
5. The CAE model multi-parameter intelligent correction calculation method based on the optimization algorithm according to claim 4, characterized in that: constructing a fitness evaluation function in the step 3), wherein the fitness evaluation function is represented as:
wherein, Target is the Target error,
SST is the sum of the squares of the differences between the raw data and the mean, expressed as
Where wi is the weight, p _ caliTo calculate the value, p _ testiIs an actual measurement value.
6. The CAE model multi-parameter intelligent correction calculation method based on the optimization algorithm as claimed in claim 1, characterized in that: the method also comprises the verification of the high-precision CAE simulation model, and the specific method comprises the following steps: and comparing the calculation result of the corrected CAE simulation model with the result obtained by the experiment to determine the error of the CAE simulation model.
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| CN116562470B (en) * | 2023-07-10 | 2023-09-08 | 苏州毕恩思实验器材有限公司 | Parameter configuration management method and system for purification type fume hood |
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