Evaluation method of carbon-fiber-reinforced polymer material anti-radiation performance based on synergistic effect model

Abstract

Aiming at the high computational costs of the current multi-source X-ray radiation numerical simulations, a multi-source X-ray evaluation method based on a synergistic effect model is studied. First, based on the advantage of the low computational cost of single-source X-ray radiation numerical simulations, a hierarchical Gaussian process model is used to construct a superimposed single-source numerical simulation data fusion model. Second, by constructing composite correlation functions, the data fusion ability of the Gaussian process model is improved. The unknown parameters in the data fusion evaluation model are solved with the help of the Markov chain Monte Carlo method and a nonlinear optimization algorithm. Based on the obtained sampling values of the unknown parameters, an approximate solution method for the estimated value of the superimposed single-source X-ray radiation numerical simulations with unknown input conditions is given. Then a synergistic effect model is constructed based on the established linear regression surrogate model. The synergistic evaluation of multi-source X-ray radiation tests based on single-source X-ray radiation numerical simulation superimposed test data is studied. Finally, carbon-fiber-reinforced polymer experiments show the proposed method in better than existing Kriging methods, for prediction of multi-source X-ray radiation data.