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Oil pump performance curve estimation method based on ADMGA (Adaptive Dissortative Mating Genetic Algorithm)

A technology of self-adaptive parameters and genetic algorithm, which is applied in the field of establishing the surface model of the working characteristics of the oil pump, can solve the problem of small modeling workload

Active Publication Date: 2012-06-27
盐城市大丰区生产力促进中心
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Problems solved by technology

[0007] The purpose of the present invention is to overcome the defects of existing oil pump working characteristic modeling methods, and provide a kind of estimation of oil pump performance curve based on adaptive parameter field genetic algorithm with unified expression, less modeling workload and simple operation method

Method used

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  • Oil pump performance curve estimation method based on ADMGA (Adaptive Dissortative Mating Genetic Algorithm)
  • Oil pump performance curve estimation method based on ADMGA (Adaptive Dissortative Mating Genetic Algorithm)
  • Oil pump performance curve estimation method based on ADMGA (Adaptive Dissortative Mating Genetic Algorithm)

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Experimental program
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Effect test

Embodiment Construction

[0063] Table 1 shows the test data of a S195 type oil pump on the oil pump performance test bench established according to the test system schematic diagram provided by JB / T8886-1999.

[0064] Table 1 S195 working characteristic test data sheet

[0065]

[0066]

[0067] Select the initial population N=4 and the initial population N=9 of the test points respectively to establish the objective function, carry out 300 genetic iterations under the state of single population and multi-population, obtain the parameters a, b, c, d and substitute them into formula (1) to obtain The amount of oil supplied and the error with the real value are shown in Table 2.

[0068] Table 2 Prediction and error of genetic algorithm for random test points

[0069]

[0070] It can be obtained from the analysis of Table 2:

[0071] In the state of a single population, the difference in the value of N leads to a great difference in error. When N=4, due to the relatively small number of point...

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Abstract

The invention provides an oil pump performance curve estimation method based on an ADMGA (Adaptive Dissortative Mating Genetic Algorithm) and in particular relates to a method for building an oil pump working characteristic curved surface model. The method comprises the steps: 1) measuring the test data of an oil pump on an oil pump performance test bench; 2) according to the experimental data, estimating the value range of parameters a, b, c and d in the formula (1); 3) updating the parameter domain of genetic iterations; and 4) estimating the oil pump performance curve by adopting the ADMGA. The method used for building the oil pump working characteristic curved surface model has the advantages of unitary expression, less modeling work load and simplicity in operation. According to the invention, not only can the global search capability improved, but also the search time is shortened, and the search performance of the genetic algorithm is improved.

Description

technical field [0001] The invention relates to a method for establishing a curved surface model of the working characteristics of an oil pump. Background technique [0002] The oil pump is an important part of the engine. Its function is to provide lubricating oil to the friction surface to reduce the wear of the parts. The general characteristics of the oil pump can be expressed as: [0003] Q=a+b·n+c·p s +d T (1) [0004] In formula (1), a is the constant coefficient of the working characteristic surface, b, c, d are the coefficients of the test motor speed n, pump pressure ps, and oil temperature T. [0005] According to the general characteristic test results, the mathematical model of the oil pump working characteristics can be estimated, and the general characteristic curve can be drawn. [0006] At present, the modeling methods of oil pump working characteristics mainly include test data fitting method, neural network modeling analysis method and computational fl...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06N3/12
Inventor 甘屹兰连旺陈杰余陈锐志
Owner 盐城市大丰区生产力促进中心
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