A cooling fan performance optimization method based on approximate model method
An approximate model and cooling fan technology, applied in design optimization/simulation, instrumentation, calculation, etc., can solve problems such as high calculation costs
Pending Publication Date: 2019-03-12
SOUTH CHINA UNIV OF TECH
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Problems solved by technology
Traditional nonlinear programming methods such as generalized descent gradient method, sequential quadratic programming method, etc., are easy to fall into local optimum
Although the intelligent optimization method can jump out of the local optimal solution, it needs a lot of evaluation of the objective function.
The evaluation of fan performance requires the CFD calculation of the full three-dimensional flow field of the fan, which is expensive to calculate. Therefore, directly using the intelligent optimization method to optimize the performance of the fan is not suitable for actual engineering.
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[0049] The present invention will be described in further detail below by referring to specific examples in conjunction with the accompanying drawings, and the examples cannot be repeated here one by one, but the implementation of the present invention is not limited to the following examples.
[0050] like figure 1 As shown, a cooling fan performance optimization method based on the approximate model method, including steps:
[0051] (1) For example Figure 5 The geometry of the fan shown is parameterized and the corresponding variables are designed:
[0052] The 12 design variables are set as constraints as shown in Table 1.
[0053] Table 1 Fan geometric constraints
[0054]
[0055] (2) Take the most efficient flow point of the fan at 2.974m 3 / s is the target operating point, and the static pressure of the fan at this flow point is 820.36Pa as the lower limit as the performance constraint.
[0056](3) The optimal Latin hypercube test method is used to collect init...
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The invention discloses a cooling fan performance optimization method based on an approximate model method, which comprises the following steps: (1) parameterizing the geometry structure of the fan and designing corresponding variables; (2) choosing static pressure and static pressure efficiency of fan as performance optimization objectives; (3) The optimal Latin hypercube experimental design method is used to collect the initial samples from the design space; (4) The initial sample is simulated by CFD model, and the response of the initial sample is obtained, and the database between the initial sample and the response is formed; (5) Constructing approximate model with radial basis function neural network; (6) The prediction accuracy of the approximate model is checked by K-times cross-validation test; (7) Genetic algorithm is used to optimize the fan performance, and the optimal fan geometry parameters are obtained. The invention optimizes the fan structure by post-processing the simulation result and using the approximate model method to parameterize the fan so as to improve the fan performance.
Description
technical field [0001] The invention relates to the field of performance optimization design of automobile cooling fans, in particular to a cooling fan performance optimization method based on an approximate model method. Background technique [0002] The aerodynamic performance optimization design methods of cooling fans can be roughly divided into two categories: traditional design methods and modern design methods. The traditional design method is to select the efficiency value empirically based on the existing statistical data and correction coefficients. The design accuracy is usually not high, but the method is simple and easy to learn. The modern design method is based on the three-dimensional flow theory of gas, and the computer is used to calculate and solve through programming or general commercial software. In recent years, many scholars at home and abroad have done a lot of research on this method. It is characterized by high calculation accuracy, but the calc...
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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 袁宏伟上官文斌
Owner SOUTH CHINA UNIV OF TECH