Radiation temperature inversion method based on artificial neural network algorithm

Abstract

The invention discloses a radiation temperature inversion method based on an artificial neural network algorithm. The method specifically comprises the following steps: constructing an artificial neural network model; simulating and generating a sample data set by utilizing a one-dimensional laser target coupled fluid program; preprocessing the sample data set; training an artificial neural network model by using the preprocessed sample data to obtain an optimal artificial neural network model; and carrying out a shock wave experiment on a sample material to obtain a target back light-emittingimage of the sample material having been impacted, and acquiring radiation unloading temperature evolution of the sample material by utilizing the optimal artificial neural network model according tothe target back light-emitting image. According to the method, inversion of the radiation temperature under metal impact loading is realized by utilizing the artificial neural network, and the methodis a novel method for calculating the metal impact radiation temperature under impact loading and has important significance for theoretical modeling of a high-pressure state equation.

Description

technical field [0001] The invention relates to the technical field of shock radiation temperature research, in particular to a radiation temperature inversion method based on an artificial neural network algorithm. Background technique [0002] The equation of state (EOS) of a material defines the functional relationship between state quantities in an equilibrium system, usually linking thermodynamic variables such as temperature with mechanical variables such as density, pressure and internal energy. The equation of state of materials under extreme conditions is very important to research fields such as astrophysics, inertial confinement fusion, high energy density physics and basic materials. Shock waves are usually used to study the equation of state of materials under high pressure. Among all the physical quantities in the equation of state, temperature is one of the most important physical quantities to characterize the thermodynamic state of matter, and the theoretic...

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Problems solved by technology

[0005] Based on the strong generalization ability and learning ability of artificial neural network, it is necessary to find a radiation temperature inversion method based on artificial neural network algorithm. The problems of various influencing factors, complex mechanisms, and difficulty in direct and accurate measurement; and the rise of various optimization algorithms, such as genetic algorithm, particle swarm algorithm, etc., have also overcome the shortcomings of artificial neural network calculations such as slow convergence speed and easy to fall into local minimum.

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