###### Abstract  
In order to improve the mixture ratio accuracy of the bipropellant thruster and prolong the service life of a satellite on orbit, a variety of different liquid-flow tests and the final firing test are critical in the production testing process. However, after comparison with a large number of tests data, it was found that the liquid-flow test results were far away from matching the firing-test data without clear regularity, resulting in a qualified rate of mixture ratio of less than 40%. This study developed a BP-RNN (back propagation--recurrent neural network) chain method based on machine learning, which uses multi-dimensional nonlinear parameters to construct the specific dataset after data enhancement. Then, the mapping characteristic of the neural network was used to fit the historical data for the weight analysis and mixture ratio prediction, and effectively improved the qualified rate of the mixture ratio. The back propagation neural network was used to learn the association rules of the 10-dimension characteristic data and the firing test results generated in the historical process of thruster production. Then, the features with high influencing weight were extracted and sorted, so the \"many-to-one\" mixture ratio prediction was conducted through the subsequent recurrent neural network. The accepted prediction accuracy could reach around 75% after the test data verification. By using this method, most of bipropellant thrusters could directly reach the qualified mixture ratio in the firing test after adjusting the throttle orifice size in the liquid-flow tests. This chain method first bridges the data between the liquid-flow test and the firing test. bipropellant thruster; mixture ratio; influencing weight; BP-RNN; the qualified rate +
Footnote †: journal: Applied Sciences  
relatively fuel-rich, which is conducive to organizing a cooling film with a safe working temperature. On the other hand, the NTO tank and MMH tank can achieve the equal-volume requirement for design, filling, and discharge. Once the thruster's mixture ratio deviates from the norm to a large extent, its thrust and working temperature sequentially move further, affecting the ignition safety in space with a great negative influence on its working life span [16; 17]. Only a few complex bipropellant propulsion systems have been designed with the precise mixing ratio control of the closed loop [18]; most are controlled in a conventional way to set a pair of orifices for throttling at the engine's upstream [19]. The flow resistance deviation is tested and then accordingly adjusted to meet the accurate requirements in engineering applications [20]. For attitude control thrusters on orbit, there are three main stages: orbital transfer attitude control, attitude control on orbit, and station keeping for spacecrafts. The pressure conditions at these three stages are different, and thus the range of the mixture ratios under the typical working conditions is shown in Table 1[21]. It shows that the mixture ratio accuracy requirement of the bipppellant thruster is quite high [22; 23]. Therefore, each thruster needs to go through liquid-flow tests many times and is then calibrated in the firing test at a rather high cost. The purpose is to provide a relatively accurate mixture ratio with the other performance parameters for space-mounted thrusters, experimentally match those thrusters with similar thrust, and effectively save on the precious propellant for satellites. However, after firing tests for hundreds of thrusters, it was found that there was a huge unexpected difference between the liquid-flow test results and the firing test data. For example, even when the flow rate results of the two thrusters after orifice adjustment were almost the same, their firing test data were still rather far from each other.