Method for predicting outlet flow non-uniformity coefficient of turbine gas collection cavity

Abstract

The invention discloses a method for predicting an outlet flow non-uniformity coefficient of a turbine gas collection cavity. The method comprises the following steps: selecting an input parameter ofa prediction model, and determining a change interval of the parameter; performing a variable working condition numerical experiment to prepare a training sample and a test sample of the prediction model; establishing a radial basis function neural network prediction model based on the training sample, and determining a neural network empirical coefficient by using a trial-and-error method; and testing the radial basis function neural network prediction model by using the test sample, and verifying the generalization ability of the prediction model. The defect that a large number of samples are needed in a traditional experience correlation method is overcome, and the turbine gas collection cavity flow non-uniformity coefficient prediction method is high in prediction precision, high in generalization capacity and high in robustness.

Description

technical field [0001] The invention belongs to the field of aero-engine air systems, and in particular relates to a method for predicting the non-uniformity coefficient of outlet flow of a turbine air collection cavity. Background technique [0002] Increasing the maximum temperature of the thermodynamic cycle is one of the basic technical approaches to improve the performance of aero turbine engines. At present, the gas temperature at the turbine inlet of an aero-engine with a thrust-to-weight ratio of 10 has reached 1900K; according to the U.S. high-performance turbine engine technology comprehensive plan and the European advanced military engine technology plan, the thrust-to-weight ratio of the next-generation aviation gas turbine engine will reach 15-20, and the turbine inlet gas The temperature will also be as high as 2200K ~ 2300K. The development of high-performance aero turbine engines puts forward more and more stringent requirements for the technical indicators ...

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Problems solved by technology

[0004] Purpose of the invention: In order to solve the problem in the background technology of realizing the accurate prediction of the uneven coefficient of the outlet flow of the turbine collector chamber, the present invention proposes a prediction method based on radial basis function neural network, overcomes the shortcomings of the traditional empirical correlation method, and provides a A flow non-uniform coefficient prediction method with high prediction accuracy, strong generalization ability and high robustness

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