No abstract

The electrospray thruster is becoming popular in space propulsion due to its low power and high specific impulse. Before this work, an electrospray thruster based on a porous emitter was developed. In order to achieve larger and more stable thrust, the thruster was redesigned, and the influence of the space between strips on thrust was studied. Four types of emitter were tested, and they had 1, 3, 4 and 14 emitter-strips on the emission surface of the same size respectively. According to the experimental results, the maximum extraction voltage and emission current of the four thrusters are different under stable operational conditions. The measured stable emission currents and extraction voltages were −500 μA/−5000 V, −1570 μA/−3800 V, −1200 μA/ −3800 V, and −650 μA/−4500 V, respectively. Increasing the number of strips may not result in the emission current increasing, but changing the stable operational range of the emission current per strip and the extraction voltage. The maximum stable operational extraction voltages of 3 and 4 emitter-strips are lower than those of 1 and 14 emitter-strips, but the emission currents are higher than those of 1 and 14 emitter-strips. Time-of-flight mass spectrometry was used to analyze the mass distribution and obtain the performance of the thruster in the case of thrusters with 1 and 3 emitter-strips. Both of their plumes were composed of very small ion cluster (the pure-ion regime), and their thrusts were 80.1 μN, 219.2 μN with specific impulses of 5774 s, 5047 s, respectively.

Currently, reinforcement learning (RL) has shown great potential in energy saving in HVAC systems. However, in most cases, RL takes a relatively long period to explore the environment before obtaining an excellent control policy, which may lead to an increase in cost. To reduce the unnecessary waste caused by RL methods in exploration, we extended the deep forest-based deep Q-network (DF-DQN) from the prediction problem to the control problem, optimizing the running frequency of the cooling water pump and cooling tower in the cooling water system. In DF-DQN, it uses the historical data or expert experience as a priori knowledge to train a deep forest (DF) classifier, and then combines the output of DQN to attain the control frequency, where DF can map the original action space of DQN to a smaller one, so DF-DQN converges faster and has a better energy-saving effect than DQN in the early stage. In order to verify the performance of DF-DQN, we constructed a cooling water system model based on historical data. The experimental results show that DF-DQN can realize energy savings from the first year, while DQN realized savings from the third year. DF-DQN’s energy-saving effect is much better than DQN in the early stage, and it also has a good performance in the latter stage. In 20 years, DF-DQN can improve the energy-saving effect by 11.035% on average every year, DQN can improve by 7.972%, and the model-based control method can improve by 13.755%. Compared with traditional RL methods, DF-DQN can avoid unnecessary waste caused by exploration in the early stage and has a good performance in general, which indicates that DF-DQN is more suitable for engineering practice.