Corrosion fatigue life prediction method based on BP neural network and application

Abstract

The invention relates to a corrosion fatigue life prediction method based on a BP neural network and application. The prediction method comprises the following steps: selecting maximum stress, stress ratio, loading frequency and pH value of a solution as main factors influencing corrosion fatigue life; designing and processing a corrosion solution circulating device matched with a corrosion fatigue test, and carrying out a corrosion fatigue circulation failure series experiments on a high-strength sucker rod sample in a specific production environment, collecting and neatening experiment data and dividing the experiment data into training samples and prediction samples; setting artificial neuron network parameters, and establishing nonlinear mapping between the influencing factors and the corrosion fatigue life; training and testing a nervous system; and predicting the corrosion fatigue life of a new sample. The corrosion fatigue life prediction method based on the BP neural network has the beneficial effects that the corrosion fatigue life of a high-strength sucker rod is predicted by high non-linear approximation capability of the BP neural network model, and operation is simple; and the prediction method is high in generalization performance, and engineering application is facilitated.

Description

technical field [0001] The invention relates to the field of engineering mechanics, in particular to a BP neural network-based corrosion fatigue life prediction method and its application, which is used for the prediction of corrosion fatigue life of high-strength sucker rod materials, and provides a basis for the optimization of rod production schemes. Background technique [0002] With the increase in the development of deep wells, ultra-deep wells and heavy oil wells, the depth of the sucker rod pump and the load of the rod string increase, the conventional E and D grade sucker rods can no longer meet the production needs of unconventional oil wells, and major oil fields are gradually developing and adopting Super high strength sucker rods to replace conventional sucker rods. At the same time, due to the development of tertiary oil recovery, especially the application of sewage reinjection technology, the working environment of sucker rods has become increasingly harsh, t...

AI Technical Summary

Problems solved by technology

In this case, the service life of high-strength sucker rods is much lower than the design life, which greatly limits the efficiency of oilfield development

[0003] Due to the strong material-environment dependence of corrosion fatigue, material type, load factors (stress amplitude, stress ratio, load frequency, waveform, etc.) and environmental factors (solution composition, concentration, and pH value) can greatly affect The corrosion fatigue life of materials, due to many influencing factors, it is difficult to express the corrosion fatigue life of materials explicitly. At the same time, these influencing factors are random, and if the load and environmental parameters change slightly, it is difficult to predict the corresponding corrosion fatigue life according to the experimental data. Limiting the promotion of corrosion fatigue test results, in order to meet the service life prediction of high-strength sucker rods in corrosive environments, optimize the production scheme with rods in unconventional oilfields

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