Composite material propeller layering angle optimization method based on neural network

A composite material and neural network technology, applied to the optimization of the ply angle for maximizing the propulsion efficiency of the ship's composite propeller, and in the field of composite propeller ply angle optimization, it can solve the problem of large amount of calculation and no careful consideration of the blade rear skew , pitch and pitch changes, lack of versatility, etc.

Active Publication Date: 2020-07-24
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF5 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing ply angle optimization method is too computationally intensive, relies too much on finite element software and is not universal, and does not carefully consider the changes in side tilt, pitch and pitch of the blade after fluid-solid interaction

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Composite material propeller layering angle optimization method based on neural network
  • Composite material propeller layering angle optimization method based on neural network
  • Composite material propeller layering angle optimization method based on neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] The present embodiment discloses in conjunction with accompanying drawing, take SEIUN-MARU large skew ship propeller (HSP) as embodiment, the specific embodiment of the present invention is as follows Figure 1-3 shown. The fiber composite material marine propeller ply angle optimization method disclosed in this implementation is realized by the following steps:

[0045] Step 1: Use the ACP module in the WorkBench platform to define the angles of each layer of the composite laminate with the middle surface of the metal propeller blade as the symmetry center to complete the fiber composite layer (unidirectional or braided layer), and import the metal The pressure surface and suction surface models of the propeller blades are used to constrain the shape of the composite material layup, and finally the establishment of the finite element model of the composite material propeller is realized.

[0046] Step 2: Import the finite element model of the composite propeller estab...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a composite material propeller layering angle optimization method based on a neural network, and belongs to the technical field of turbomachinery simulation. The method comprises the following steps: establishing a composite propeller finite element model, and performing bidirectional fluid-solid coupling calculation on the composite propeller finite element model and a composite propeller computational fluid mechanics model to obtain a corresponding open-water characteristic curve; adopting an orthogonal test design method to preprocess the layering angle combinationand the corresponding hydrodynamic performance data; constructing a BP neural network with high nonlinear fitting based on numerical calculation software, training the constructed neural network by adopting a gradient descent algorithm, and finally obtaining the BP neural network with a mapping rule between the composite material propeller layering angle and the propulsion efficiency; and according to the trained BP neural network, carrying out optimization prediction on the combination situation of all the layering angles, and analyzing the obtained efficiency data to obtain the maximum network prediction value and the corresponding layering angle combination, that is, optimization of the lay-up angles of the composite propeller is realized based on the neural network.

Description

technical field [0001] The invention relates to a method for optimizing the ply angle of a composite material propeller based on a neural network, in particular to a method for optimizing a ply angle that can maximize the propulsion efficiency of a composite material propeller of a ship, and belongs to the technical field of impeller machinery simulation. Background technique [0002] Traditional metal propellers have high manufacturing and processing costs, poor damping performance, are prone to noise, and the blades are prone to corrosion, which directly affects the service life of the propeller and the survival and attack capabilities of military ships. Composite materials have high specific strength, high specific modulus, corrosion resistance, fatigue resistance, good damping and vibration reduction, good damage safety and designable performance, etc., and have been widely used in the fields of aerospace and civil engineering. With the development of my country's naval ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/23G06F30/17G06N3/12G06F119/14G06F111/10G06F113/26
CPCG06N3/08G06N3/044G06N3/045
Inventor 吴钦曲毅田茂宇张晶王国玉
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products