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Since completion of the National Ignition Facility (NIF) in 2010, more than 500 experiments were carried out to demonstrate ignition. After several unsuccessful experimental designs in the first 8 years, NIF has improved its experimental designs and achieved fusion yields of 55kJ, 170kJ and 1.35MJ respectively since 2019, approaching the ignition milestone. The improved designs are based on optimization process using experimental database, which has been widely used for optimization design, yield prediction, corrected simulation and other purposes. However, because of inaccessibility of the database, Chinese researchers can only understand its experimental process from very limited published data. Also, it is difficult to obtain a completion data matrix for analyzing and understanding the experimental designs of NIF experiments at each stage and to know how the NIF sets strategic priorities for each phase. We propose a method that combines the PMM algorithm with the trust region algorithm, to restore NIF data according to the empirical formula derived from regression of NIF experimental data and the parameter range of NIF experiments at each stage. We also propose a method to verify and analyze the completed data obtained. Using these data, this paper analyzes the different emphases and design ideas of NIF experiments at different stages in the past ten years, and predicts the hot spot pressure in the future experiments of NIF using different machine learning algorithms. These studies provide a feasible method for Chinese researchers to continuously follow up and understand the latest complete DATA of NIF deeply, and we hope these studies can provide reference for the design of laser fusion ignition experiments in China.

Analysis of design principles of the experiments on the National Ignition Facility since 2010