CN111753427A - Method for improving precision of electromechanical product simulation model based on evidence theory - Google Patents
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The invention discloses a method for improving the precision of a simulation model of an electromechanical product based on an evidence theory, which comprises the following steps of 1: carrying out a few test measurements on the electromechanical product, carrying out statistical analysis on the measured response data, and analyzing the measured response data by using an evidence theory; step 2: establishing an electromechanical product simulation model, acquiring a calculation response, and establishing a target function for identifying the electromechanical product modeling parameter interval range; and step 3: the identified modeling parameter interval is further divided into a plurality of subintervals, and a certain probability of modeling parameters is primarily distributed to each subinterval; and 4, step 4: and establishing a target function for identifying the probability value of the subinterval of the modeling parameter by using the simulation model of the electromechanical product and the probability of the distributed subinterval, and identifying accurate probability distribution on the subinterval of the modeling parameter by an optimization method. The method can accurately acquire the value range and the probability distribution of the modeling parameters under a small amount of measurement response, and effectively improve the precision level of the simulation model of the electromechanical product.
Description
Technical Field
The invention relates to a method for improving the precision of a simulation model of an electromechanical product, in particular to a method for improving the precision of a simulation model of an electromechanical product based on an evidence theory.
Background
Electromechanical products are complex systems commonly used in multiple fields such as machinery, hydraulic pressure, electronics and control. The simulation model has become a powerful analysis tool for a complex electromechanical product system due to the advantages of reusability of expression, predictability of structural performance, controllability of a product development process and the like. The accuracy of the simulation model of the electromechanical product depends on accurate acquisition of key modeling parameters such as force, electricity, heat, control and the like required by modeling.
However, due to factors such as complexity of multi-field coupling of the electromechanical product, insufficient cognitive level, experimental error and the like, the traditional test method is difficult to or even incapable of directly measuring the key parameters, so that uncertainty inevitably exists in simulation modeling, and the reliability of a simulation result of the electromechanical product is low. The evidence theory has a flexible uncertainty modeling framework, has little dependence on sample data, and can be used for uncertainty evaluation of complex electromechanical products with difficult experimental testing and high experimental cost.
Disclosure of Invention
The invention aims to identify the uncertainty of key unknown parameters in the modeling process through an evidence theory and an optimization method, thereby effectively reducing the uncertainty in the modeling process and improving the precision level of a simulation model of an electromechanical product. Therefore, the method and the device have important engineering significance for reducing the uncertainty in the modeling process and improving the precision level of the simulation model of the electromechanical product by identifying the uncertainty of the model parameters through the evidence theory and acquiring the probability distribution information on the interval range and the internal subinterval of the model parameters.
The technical scheme of the invention is to provide a method for improving the accuracy of a simulation model of an electromechanical product based on an evidence theory, which is characterized by comprising the following steps:
step 1: carrying out a small amount of test measurement on the electromechanical product, carrying out statistical analysis on the measured response data, analyzing the measured response data by using an evidence theory, and obtaining a value interval of the measured response, an upper boundary of the cumulative probability distribution and a lower boundary of the cumulative probability distribution;
step 2: establishing an electromechanical product simulation model, acquiring a calculation response, establishing a target function for identifying a modeling parameter interval range by comparing a value interval of the measurement response with a value interval of the calculation response, identifying the force, electricity and heat parameter interval required by electromechanical product simulation modeling by using an optimization method, determining the value range of a modeling parameter, and reducing uncertainty in the model;
and step 3: the identified modeling parameter interval is further divided into a plurality of subintervals, and a certain probability of modeling parameters is primarily distributed to each subinterval;
and 4, step 4: calculating and obtaining the upper boundary of the cumulative probability distribution of the model response and the lower boundary of the cumulative probability distribution by using the simulation model and the probability of the modeling parameters which are primarily distributed, comparing the upper boundary and the lower boundary of the cumulative probability distribution with the upper boundary and the lower boundary of the cumulative probability distribution of the measured response, thereby establishing an objective function, updating the probability distribution of the modeling parameters by using an optimization method, determining the accurate probability distribution on each subinterval of the modeling parameters, further enhancing the reliability of the simulation model of the electromechanical product, and improving the simulation precision.
Preferably, in the step 1, a plurality of nodes are selected in the measurement response value interval, and an upper boundary of the cumulative probability distribution and a lower boundary of the cumulative probability distribution at each value of the measurement response shown in fig. 1 are obtained by combining the test response information and expert experience.
Preferably, the objective function for identifying the range of the modeling parameter interval in step 2 is:
wherein g (X) represents the simulation model of the electromechanical product, X represents the vector formed by uncertain modeling parameters, and XjFor the jth uncertain model-making parameter,
and
representing a parameter XjUpper and lower boundaries of (a). Y is a calculated response vector, Yi LAnd Yi RUpper and lower boundaries, Y, representing the ith calculation response intervali *LAnd Yi *RAnd n and m respectively represent the number of uncertainty modeling parameters and measurement responses, and m is more than or equal to n. And continuously updating the interval of the modeling parameters by an optimization method until the interval corresponding to the modeling parameters is the identified interval when the formula (1) is taken as the minimum value.
Preferably, the objective function for accurately quantifying uncertainty of the modeling parameters in the step 4 is as follows:
in the formula, YikAnd
the kth value, Q, representing the ith calculated response and the measured responseiAnd the ith response is represented by Q values in total. Bel and Pl are the lower bounds of the cumulative probability distribution at the response values, respectivelyAnd an upper boundary of the cumulative probability distribution. And continuously matching probability distribution on each subinterval of the modeling parameters by an optimization method until the probability of each subinterval of the corresponding parameters is the identified optimal probability distribution when the formula (2) is taken as the minimum value.
According to the technical scheme, the beneficial effects of the invention comprise that:
(1) the invention can accurately identify the uncertainty of key parameters in the simulation modeling process based on the evidence theory, and can effectively reduce the physical test times and test cost of the electromechanical product required by determining model parameters.
(2) The method combines an evidence theory and an optimization method to identify the intervals of force, electricity and heat parameters required by simulation modeling, and can effectively reduce the uncertainty of model parameters of the electromechanical product;
(3) the method combines the upper boundary of the cumulative probability distribution and the lower boundary of the cumulative probability distribution of the measurement response statistical data to perform refinement, probability distribution and updating on the model parameter subinterval, thereby realizing accurate evaluation of the modeling parameters and improving the precision level of the simulation model of the electromechanical product.
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The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 measures the upper and lower bounds of the cumulative probability distribution of the response;
FIG. 2 is a vehicle occupant restraint system simulation model;
FIG. 3 illustrates ranges and sub-interval probability distributions for the constraint system modeling parameters identified.
Detailed Description
The following describes a preferred embodiment of the present invention, with reference to the accompanying drawings, which form a part of this application and together with the embodiments of the invention serve to explain the principles of the invention, by way of example the identification of probability distributions within key modeled parameter intervals of a vehicle occupant restraint system and the accuracy improvement of simulation models thereof as shown in fig. 2.
Due to the complexity of the vehicle occupant restraint system, particularly due to the limitations of high instantaneity, uncertainty and crash test cost of a vehicle crash accident, some key parameters of the restraint system in the modeling process, including a seat belt stiffness scaling factor and an air bag flow rate scaling factor, are difficult to be determined directly through a test method. However, the human body injury response is relatively easy to measure in the collision process, and how to utilize limited measurement response data to determine key modeling parameters and uncertainty of the key modeling parameters has important significance for improving the precision level and usability of a simulation model of the constraint system.
According to a specific embodiment of the invention, the invention discloses a simulation model precision improving method based on an evidence theory, which specifically comprises the following steps:
step 1: carrying out a small amount of test measurement on the electromechanical product, carrying out statistical analysis on the measured response data, analyzing the measured response data by using an evidence theory, and obtaining a value interval of the measured response, an upper boundary of the cumulative probability distribution and a lower boundary of the cumulative probability distribution;
in the present embodiment, taking the vehicle occupant restraint system as an example, a male or female dummy is attached to the occupant restraint system, and a few crash tests are performed on the vehicle. Measuring the acceleration response of the head of a male or female passenger and converting the acceleration response into a male head injury value Y1 *And female head injury value
As shown in fig. 1, the data of the responses measured by these several tests are counted and analyzed by the evidence theory, specifically:
(1) statistics of Y1 *And
to obtain a measured response Y1 *And
the value intervals of (1) are respectively [0.51 and 0.8 ]]And [0.45,0.83 ]](ii) a Wherein: y is1 *Is the value of the head injury of the male,
is the value of female head injury.
(2) At Y1 *And
selecting 50 nodes in the interval, and combining test response information and expert experience to obtain Y1 *And
a lower bound and an upper bound of the cumulative probability distribution at each node.
Step 2: establishing an electromechanical product simulation model, acquiring a calculation response, establishing a target function for identifying a modeling parameter interval range by comparing a value interval of the measurement response with a value interval of the calculation response, identifying the force, electricity and heat parameter interval required by electromechanical product simulation modeling by using an optimization method, determining the value range of a modeling parameter, and reducing uncertainty in the model;
in this embodiment, a vehicle occupant restraint system simulation model is established consistent with the test conditions, as shown in FIG. 2. Due to the limitations of instantaneity, uncertainty, high collision test cost and the like of vehicle collision accidents, some key parameters of the restraint system in the modeling process comprise a safety belt rigidity scaling coefficient X1And an airbag flow rate scaling factor X2And the like are difficult to determine directly by means of experimental tests.
Due to the scaling coefficient X of the rigidity of the safety belt1And an airbag flow rate scaling factor X2And uncertainty exists, so that the reliability of the currently established passenger restraint system simulation model is not high, and the parameter value interval of the simulation model is identified and the uncertainty is reduced by combining the test and evidence theory.
Given arbitrary model parameters X1And X2Obtaining a calculated response Y from the constraint system simulation model1And Y2。
Likewise, an arbitrary modeling parameter interval is given
And
the value range [ Y ] of the calculation response can be obtained through model simulation1 L，Y1 R]And
calculating the response Y by comparison1And Y2And the value range of (1) and the measurement response Y obtained in the step (1)1 *And
value range of [0.51,0.8 ]]And [0.45,0.83 ]]Establishing the recognition X as shown in formula (1)1And X2Objective function of real interval range:
wherein g (X) represents a simulation model of the vehicle occupant restraint system established, X represents a vector formed by uncertain modeling parameters, and XjFor the jth uncertain model-making parameter,
and
representing a parameter XjUpper and lower boundaries of (a). Y is a calculated response vector, Yi LAnd Yi RUpper and lower boundaries, Y, representing the ith calculation response intervali *LAnd Yi *RThe upper boundary and the lower boundary of the ith measurement response interval are represented, n and m respectively represent the number of the uncertainty modeling parameter and the measurement response, m is larger than or equal to n, and m is larger than or equal to n and is larger than or equal to 2 in the embodiment. General best of allOptimizing the target function formula (1) by the algorithm, continuously iteratively updating the interval of the model parameters, and identifying and acquiring the scaling coefficient X of the stiffness of the safety belt when the target function formula (1) takes the minimum value1And an airbag flow rate scaling factor X2Intervals of [0.2,0.8 ], respectively]And [0.1,0.7]The identification results reduce the uncertainty of the modeling parameters.
And step 3: the identified modeling parameter interval is further divided into a plurality of subintervals, and a certain probability of modeling parameters is primarily distributed to each subinterval;
in the present embodiment, the identified seatbelt stiffness scaling factor X is1Interval [0.2,0.8 ]]Further divided into 4 sub-intervals, i.e. [0.2,0.35 ]]、[0.35,0.5]、[0.5,0.65]And [0.65,0.8 ]](ii) a Scaling the identified airbag flow rate by a factor X2Interval [0.1,0.7 ]]Further divided into 5 sub-intervals, i.e. [0.1,0.22 ]]、[0.22,0.34]、[0.34,0.46]、[0.46,0.58]And [0.58,0.7 ]]. For parameter X1And X2Each subinterval of (2) is assigned a certain initial probability, the probability on each subinterval is greater than 0, and the sum of the probabilities of all subintervals of each parameter is equal to 1.
And 4, step 4: calculating and obtaining the upper boundary of the cumulative probability distribution of the model response and the lower boundary of the cumulative probability distribution by using the simulation model and the probability of the modeling parameters which are primarily distributed, comparing the upper boundary and the lower boundary of the cumulative probability distribution with the upper boundary and the lower boundary of the cumulative probability distribution of the measured response, thereby establishing an objective function, updating the probability distribution of the modeling parameters by using an optimization method, determining the accurate probability distribution on each subinterval of the modeling parameters, further enhancing the reliability of the simulation model of the electromechanical product, and improving the simulation precision.
In this embodiment, the parameter X in step 3 is set1And X2The sub-regions are combined pairwise, so that the whole parameter space can be divided into 20 two-dimensional rectangular sub-regions, and the probability of each sub-region is corresponding X1And X2Product of probabilities of the subintervals. The upper and lower boundaries of the calculation response corresponding to all the sub-regions can be obtained through simulation model calculation. Respectively comparing with the response values of 50 nodes in the step 1, and if the lower bound of the calculated response in the sub-area is larger than the response of the nodeIf so, calculating the probability distribution of the sub-region into the upper boundary and the lower boundary of the calculated cumulative probability distribution of the node response; if the node response value is between the upper boundary and the lower boundary of the calculated response, only the probability distribution of the sub-region is counted into the upper boundary of the calculated cumulative probability distribution of the node response; if the upper bound of the calculated response is less than the value of the node response, the probability distribution of the sub-region does not contribute to the calculated cumulative probability distribution of the node response. Thereby, an upper and a lower boundary of the calculated cumulative probability distribution over the entire response interval may be obtained.
On the basis of the above, response Y is compared1And Y2Establishing an identification modeling parameter X shown in formula (2) by upper and lower boundaries of the calculated cumulative probability distribution at 50 nodes and upper and lower boundaries of the measured cumulative probability distribution obtained in step 1 and shown in FIG. 11And X2Objective function of probability assignment:
in the formula, YikAnd
the kth node value, Q, representing the ith computational response and the measurement responseiThe ith response is represented by Q values, and in the embodiment, Q is 50. Optimizing the target function formula (2) through an optimization algorithm, iteratively updating the probability of the distribution of the model parameters on the subintervals, and identifying and obtaining the scaling coefficient X of the rigidity of the safety belt1And an airbag flow rate scaling factor X2The result of accurate sub-interval probability distribution is shown in fig. 3. The identified interval and the probability distribution result can realize the evaluation of the precision level of the modeling parameter, and the identification result is applied to the occupant restraint system simulation model shown in FIG. 2, so that the precision level of the model simulation can be improved.
The embodiment verifies the correctness and the effectiveness of the method, and although only a calculation example of a vehicle passenger restraint system is given, the skilled person in the art can understand that the method is generally applicable to other electromechanical products, so that the method effectively reduces the uncertainty of the modeling parameters and improves the precision level of the simulation model by identifying the interval and the probability distribution of the key modeling parameters of the simulation model.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (4)
1. A method for improving the accuracy of a simulation model of an electromechanical product based on an evidence theory is characterized by comprising the following steps:
step 1: carrying out a small amount of test measurement on the electromechanical product, carrying out statistical analysis on the measured response data, analyzing the measured response data by using an evidence theory, and obtaining a value interval of the measured response, an upper boundary of the cumulative probability distribution and a lower boundary of the cumulative probability distribution;
step 2: establishing an electromechanical product simulation model, acquiring a calculation response, establishing a target function for identifying a modeling parameter interval range by comparing a value interval of the measurement response with a value interval of the calculation response, identifying the force, electricity and heat parameter interval required by electromechanical product simulation modeling by using an optimization method, determining the value range of a modeling parameter, and reducing uncertainty in the model;
and step 3: the identified modeling parameter interval is further divided into a plurality of subintervals, and a certain probability of modeling parameters is primarily distributed to each subinterval;
and 4, step 4: calculating and obtaining the upper boundary of the cumulative probability distribution of the model response and the lower boundary of the cumulative probability distribution by using the simulation model and the probability of the modeling parameters which are primarily distributed, comparing the upper boundary and the lower boundary of the cumulative probability distribution with the upper boundary and the lower boundary of the cumulative probability distribution of the measured response, thereby establishing an objective function, updating the probability distribution of the modeling parameters by using an optimization method, determining the accurate probability distribution on each subinterval of the modeling parameters, further enhancing the reliability of the simulation model of the electromechanical product, and improving the simulation precision.
2. The method for improving the accuracy of the simulation model of the electromechanical product according to claim 1, wherein a plurality of nodes are selected from a measurement response value range in the step 1, and an upper boundary of the cumulative probability distribution and a lower boundary of the cumulative probability distribution at each value of the measurement response are obtained by combining test response information and expert experience.
3. The method for improving the accuracy of the simulation model of the electromechanical product according to claim 1, wherein the objective function for identifying the range of the modeling parameter interval in the step 2 is as follows:
wherein g (X) represents the simulation model of the electromechanical product, X represents the vector formed by uncertain modeling parameters, and XjFor the jth uncertain model-making parameter,
and
representing a parameter XjUpper and lower boundaries of (a); y is a calculated response vector, Yi LAnd Yi RUpper and lower boundaries, Y, representing the ith calculation response intervali *LAnd Yi *RRepresenting the upper boundary and the lower boundary of the ith measurement response interval, wherein n and m respectively represent the uncertainty modeling parameter and the number of measurement responses, and m is more than or equal to n; and continuously updating the interval of the modeling parameters by an optimization method until the interval corresponding to the modeling parameters is the identified interval when the formula (1) is taken as the minimum value.
4. The method for improving the accuracy of the simulation model of the electromechanical product according to claim 1, wherein the objective function for determining the probability distribution of the modeling parameter subintervals in the step 4 is as follows:
in the formula, YikAnd
the kth value, Q, representing the ith calculated response and the measured responseiAnd the ith response is represented by Q values in total. Bel and Pl are the lower boundary of the cumulative probability distribution and the upper boundary of the cumulative probability distribution at the response value, respectively. And continuously matching probability distribution on each subinterval of the modeling parameters by an optimization method until the probability of each subinterval of the corresponding parameters is the identified optimal probability distribution when the formula (2) is taken as the minimum value.
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