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A method of aerodynamic data fusion based on neural network

A fusion method, neural network technology, applied in instrumentation, design optimization/simulation, computing, etc.

Active Publication Date: 2020-12-08
ZHEJIANG UNIV
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Problems solved by technology

[0010] Currently, several methods of aerodynamic data fusion that are commonly used have some limitations when applied alone. The present invention aims at the respective advantages and disadvantages of the VCM method and the neural network method, and improves the VCM method by introducing influence weight parameters, and combines it with the neural network method Combined, a better aerodynamic data fusion method is proposed

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  • A method of aerodynamic data fusion based on neural network
  • A method of aerodynamic data fusion based on neural network
  • A method of aerodynamic data fusion based on neural network

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[0045] The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, and the purpose and effect of the present invention will become more obvious.

[0046] For data fusion needs, the present invention selects the double blunt cone model used by the researchers of the NASA Research Center, and provides a large amount of wind tunnel experiment data in the published papers. The size of the model is: the curvature radius of the head is 3.835mm, the front half cone angle is 12.84°, the rear half cone angle is 7°, the distance between the front half cone and the head is 69.55mm, and the distance between the back cone and the head is 122.24 mm. The dimensions of the model are shown in figure 1 shown.

[0047] The calculated free flow condition is Ma ∞ =9.86,T ∞ = 48.88K, p ∞ =59.92Pa, ρ ∞ =0.004271kg / m 3 . The outlet boundary condition is the centroid extrapolation boundary condition, the double blunt cone w...

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Abstract

The invention discloses a neural network-based pneumatic data fusion method. The method uses the VCM fusion method to correct the correlation of data volume during neural network fusion on the basis of traditional neural network fusion, and aims at the fusion of high-precision data. properties, the influence weight parameters are introduced to help the fusion, and finally the aerodynamic data fusion method combining the improved VCM method and the neural network method is obtained. The fusion method proposed by the present invention can effectively overcome the correlation between the traditional neural network fusion method and the amount of data, limit the fusion influence range of high-precision data, and obtain the overall fusion trend consistent with the data trend of large data volume, and the influence range of high-precision data is consistent with that of high-precision data. Fusion data consistent with precision data.

Description

technical field [0001] The present invention relates to the fusion of different aerodynamic data sources in aerodynamics, in particular to a neural network-based aerodynamic data fusion method, which can fuse aerodynamic data sources with complementary advantages and disadvantages, and can also be applied to sensors and other fields where multiple data sources exist. Background technique [0002] In aerodynamics, wind tunnel experiment, numerical calculation and flight test (model flight test) are often used to obtain aircraft aerodynamic data, but each method has its advantages and disadvantages. The data of wind tunnel experiments have high precision, but there are influences such as tunnel wall interference and bracket interference effects, which are very different from real flight conditions; numerical calculations are convenient and flexible, but they are highly dependent on grids and have certain influence on hardware and software. requirements, and the calculation ac...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G06K9/62
CPCG06F30/20G06F18/25
Inventor 吴昌聚曹世浩江中正吴宁
Owner ZHEJIANG UNIV
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