Speaker
Description
Abstract
Keywords: Space debris, Near Earth Orbit, Terminal rocket stage, Mitigation, Hazardous.
Terminal stage in Launch vehicle poses several challenges: space debris, residual propellant and high-pressure gases. Space debris is due to spent terminal rocket stage, explosion or fragmentation of damaged space objects. The increasing space debris leads the risk of collisions with operational satellites, space craft, and even crewed missions. This space debris can damage or destroy the services such as communication; navigation, weather monitoring on Near Earth Orbit (NEO). Also, the re-entry of space debris to earth’s atmosphere cause damage on the ground.
This paper explores the technical approach by using artificial intelligence and machine learning techniques for mitigating the spent terminal stage by controlled venting of hazardous propellants and gases with passivation and disposal systems, minimizing the risk of uncontrolled de-orbiting and controlled re-entry. Also spent terminal rocket stage can use as a orbital platform for short term scientific experiments using three axis control.
Passivation system can effectively mitigate the propellant vapor and high-pressure gases, explosion, fire hazards, contamination of the environment and damage to nearby spacecraft or space assets. Disposal system designed to safely dispose the residual propellant after passivation, preventing ignition or explosion. This is achieved through a cold gas-based control system utilizes the gas bottles isolated from the propellant tank circuit after the main mission. The leftover high pressure gas is utilized for the cold gas-based control system and the leftover propellant is dispensing through the engine. The efficient disposal of the residual propellant and gases from the terminal liquid stage is accomplished through sequential operation of engine valves, thrusters, pyro valves, and solenoid valves controlled by the onboard computer. Artificial Intelligence techniques are used to hazard mitigate the hazards in Near-Earth Orbit (NEO) operations, especially in managing the risks associated with hazardous propellants, gases, and other potential threats. AI can also optimize propellant usage to minimize waste and reduce the risk of residual propellant hazards at the end of the mission. For predictive maintenance, it is proposed to use Long Short -Term Memory (LSTM) neural network methodology. With the use of Isolation Forest Algorithm, any potential threats in real time can be detected. AI system enables continuous monitoring the health of propulsion systems and related components, providing real-time alerts, if abnormal behavior is detected.