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Solid rocket engine storage life prediction method

A solid rocket, storage life technology, applied in the direction of instrumentation, design optimization/simulation, calculation, etc., to achieve the effect of solving the difficulty of uncertainty

Inactive Publication Date: 2018-05-11
CHONGQING UNIV
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  • Application Information

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Problems solved by technology

[0003] At present, deterministic methods are commonly used to study the structure of grains, but due to the influence of uncertain factors such as production technology, storage environment, and propellant physical properties, deterministic methods have certain limitations here.

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  • Solid rocket engine storage life prediction method

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Embodiment Construction

[0035] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0036] like figure 1 Shown, the present invention comprises the following steps: 1) based on deterministic finite element method, in conjunction with MonteCarlo sampling technology, form MonteCarlo stochastic finite element method; Carry out statistical analysis, and use the stress-strength interference model to calculate the structural reliability of the explosive column ignition moment in different storage periods; 3) The neural network can realize highly nonlinear mapping from input to output state space. Using this feature, based on machine learning The supervised learning artificial neural network algorithm predicts the structural reliability of the engine grain obtained by the stochastic finite element analysis, and predicts the storage life of the missile solid motor based on the change of the structural reliability with time.

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Abstract

The invention relates to a solid rocket engine storage life prediction method and belongs to the technical field of intelligent automation. The method comprises the steps that S1, a Monte Carlo randomfinite element method is formed on the basis of a certainty finite element method in combination with a Monte Carlo sampling technology; S2, statistical analysis is performed on strain in the ignition process of a solid rocket engine based on the Monte Carlo random finite element method, and a stress- strength interference model is utilized to calculate structural reliability of grain ignition moments in different storage periods; and S3, according to the characteristic that a neural network can realize highly non-linear mapping from an input state space to an output state space, the structural reliability, obtained through random finite element analysis, of engine grains is predicted based on a supervised learning artificial neural network algorithm in machine learning, and therefore thestorage life of a missile solid engine is predicted. Through the method, a data driving mode can be effectively adopted to solve a difficulty brought by uncertain factors in mechanism analysis, and the precision of solid rocket engine life prediction is improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent automation and relates to a method for predicting the storage life of a solid rocket motor. Background technique [0002] Solid rocket motor is an important component of missiles and space rockets, and its research work is closely related to space safety. When the solid rocket motor is working, the life of the grain is closely related to its structural reliability and performance reliability, and the structural reliability is the main factor. Therefore, the structural reliability of the grain determines the service life of the solid rocket motor, so the correct prediction of the service life of the solid rocket motor not only determines the safety of spaceflight but also maximizes the service life of the solid rocket motor, so as to reduce the unfavorable Necessary waste. [0003] At present, deterministic methods are commonly used to study the structure of grains, but due to the influence o...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/23
Inventor 赵玺王坤林景栋陈敏林正
Owner CHONGQING UNIV