Compressor blade robustness design method based on data driving

A technology of blade robustness and design method, applied in design optimization/simulation, special data processing application, CAD numerical modeling, etc., can solve the problem of training surrogate model with a large number of numerical simulation, and achieve the effect of avoiding fitting errors

Pending Publication Date: 2022-05-03
NORTHWESTERN POLYTECHNICAL UNIV
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However, training the proxy model itself also requires extensive numerical simulations

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  • Compressor blade robustness design method based on data driving
  • Compressor blade robustness design method based on data driving
  • Compressor blade robustness design method based on data driving

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[0027] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0028] A kind of data-driven compressor blade robust optimization design method proposed by the present invention, the steps are as follows:

[0029] Step 1: Use the NURBS curve to parameterize the center arc of the blade; the initial blade is constructed by superimposing the inscribed circle on the center arc, and the center arc of the blade is parameterized by the NURBS method to obtain the The control po...

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Abstract

The invention relates to a robustness design optimization method of a gas compressor blade based on data driving, which adopts a mean camber line superposition thickness distribution mode to construct the blade, and uses an NURBS curve to parameterize the mean camber line of the blade. A four-point three-order data-driven non-embedded polynomial chaos method is used for carrying out uncertainty quantization on sparse sampling data, and four blade configuration modes are obtained. Sampling a blade design space by using a Latin hypercube method, and training a Gaussian process regression model by using a sampling set in each blade configuration mode; and respectively obtaining a GPR agent model at each blade configuration mode. After training is finished, a multi-target optimization algorithm NSGA II is adopted, and optimization search is carried out with the statistical mean value and the standard deviation of the blade total pressure loss coefficient as targets; therefore, the robust compressor blade with better performance and greatly reduced sensitivity to input uncertainty is obtained.

Description

technical field [0001] The invention relates to a compressor blade optimization design method, in particular to a blade robustness optimization design method capable of quantifying the influence of sparse uncertainty input based on data-driven means. Background technique [0002] Advanced compressor blades require not only high performance, but also high reliability under the influence of uncertain factors. Uncertainty factors are unavoidable, which will cause the geometrical line or operating point of the blade to deviate from the initial design, and have a non-negligible impact on the aerodynamic performance. The robust optimization design of blades can eliminate the negative effects of uncertain factors and improve aerodynamic performance and reliability at the same time. [0003] The core of robust optimal design is uncertainty quantification technology. The reliability of uncertainty quantification depends on the distribution of model input parameters. In practical e...

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Application Information

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IPC IPC(8): G06F30/28G06F30/27G06F30/15G06F111/06G06F111/10
CPCG06F30/28G06F30/27G06F30/15G06F2111/10G06F2111/06
Inventor 高丽敏王浩浩杨光
Owner NORTHWESTERN POLYTECHNICAL UNIV
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