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Method and device for predicting Mach number of wind tunnel based on convolutional neural network

A technology of convolutional neural network and prediction method, applied in the field of non-transitory computer-readable storage medium, can solve the problems of slow training, huge parameters, and reducing the effect of fitting the real situation.

Active Publication Date: 2021-05-11
CHANGZHOU MICROINTELLIGENCE CO LTD
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Problems solved by technology

However, the parameters of this technical solution are huge, the training is slow, and the machine learning model needs to process the data, deleting single values ​​and outliers, which will reduce the fitting effect on the real situation

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  • Method and device for predicting Mach number of wind tunnel based on convolutional neural network
  • Method and device for predicting Mach number of wind tunnel based on convolutional neural network
  • Method and device for predicting Mach number of wind tunnel based on convolutional neural network

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Embodiment Construction

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0024] figure 1 It is a flowchart of a method for predicting the Mach number of a wind tunnel based on a convolutional neural network according to an embodiment of the present invention.

[0025] Such as figure 1 As shown, the method for predicting the Mach number of the wind tunnel based on the convolutional neural network in the embodiment of the present invention may include the following steps:

[0026] S1. Acquiring wind tunnel data within a first prese...

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Abstract

The present invention provides a method and device for predicting the Mach number of a wind tunnel based on a convolutional neural network. The method includes: acquiring wind tunnel data within a first preset time; preprocessing the wind tunnel data to convert the wind The tunnel data is converted into three-dimensional data; the convolutional neural network is used to train based on the processed wind tunnel data to obtain a prediction model; the wind tunnel data to be predicted is obtained, and the wind tunnel data to be predicted is input into the prediction model to obtain the wind tunnel data. The Mach number of the hole. The invention performs modeling according to the collected wind tunnel data, and can quickly and accurately predict the Mach number of the wind tunnel, thereby breaking away from the dependence on empirical formulas and improving the reliability and precision of Mach number measurement.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a method for predicting the Mach number of a wind tunnel based on a convolutional neural network, a device for predicting the Mach number of a wind tunnel based on a convolutional neural network, a computer device and a A non-transitory computer readable storage medium. Background technique [0002] The wind tunnel is an extremely important device for studying the aerodynamic characteristics of advanced aircraft. In wind tunnel tests, the stability and speed of the Mach number have a great influence on the quality of the wind tunnel. In order to accurately control the Mach number, we must have a fast and accurate prediction of the Mach number. In wind tunnel testing, there is often a large amount of data recorded from which we model and predict the Mach number. Mach number is an important parameter reflecting the performance index of wind tunnel flow field. Due ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06F30/28G01M9/06G06F113/08G06F119/14
CPCG01M9/06G06F30/27G06F30/28G06F2113/08G06F2119/14
Inventor 杭天欣马元巍陈红星王克贤潘正颐侯大为
Owner CHANGZHOU MICROINTELLIGENCE CO LTD
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