Ground experimental investigations into an ejected spray cooling system for space closed-loop application

Spray cooling has proved its superior heat transfer performance in removing high heat flux for ground applications. However, the dissipation of vapor liquid mixture from the heat sur- face and the closed-loop circulation of the coolant are two challenges in reduced or zero gravity space enviromnents. In this paper, an ejected spray cooling system for space closed-loop application was proposed and the negative pressure in the ejected condenser chamber was applied to sucking the two-phase mixture from the spray chamber. Its ground experimental setup was built and exper- imental investigations on the smooth circle heat surface with a diameter of 5 mm were conducted with distilled water as the coolant spraying from a nozzle of 0.51 mm orifice diameter at the inlet temperatures of 69.2 ℃ and 78.2 ℃ under the conditions of heat flux ranging from 69.76 W/cm2 to 311.45 W/cm2, volume flow through the spray nozzle varying from 11,22 L：h to 15.76 L·h. Work performance of the spray nozzle and heat transfer performance of the spray cooling system were analyzed; results show that this ejected spray cooling system has a good heat transfer performance and provides valid foundation for space closed-loop application in the near future.

Neural adaptive control for a ground experiment of the space proximity operation in a six-degree-of freedom micro-gravity simulation system

In this study,a neural adaptive controller is developed for a ground experiment with a spacecraft proximity operation.As the water resistance in the experiment is highly nonlinear and can significantly affect the fidelity of the ground experiment,the water resistance must be estimated accurately and compensated using an active force online.For this problem,a novel control algorithm combined with Chebyshev Neural Networks(CNN)and an Active Disturbance Rejection Control(ADRC)is proposed.Specifically,the CNN algorithm is used to estimate the water resistance.The advantage of the CNN estimation is that the coefficients of the approximation can be adaptively changed to minimize the estimation error.Combined with the ADRC algorithm,the total disturbance is compensated in the experiment to improve the fidelity.The dynamic model of the spacecraft proximity maneuver in the experiment is established.The ground experiment of the proximity maneuver that considers an obstacle is provided to verify the efficiency of the proposed controller.The results demonstrate that the proposed method outperforms the pure ADRC method and can achieve close-to-real-time performance for the spacecraft proximity maneuver.