An Intelligence System Model to Study the Impact of Protruding length on Temperature Sensors for Cryogenic Propulsion

Abstract

Cryogenic Propulsion System of the Rocket Engine has in built control systems which control the critical propulsion parameters such as mixture ratio, thrust. Mixture Ratio Control System (MRC) ensure proper & steady mixture ratio during the engine operations. In general, fruitful sensor data improves the mission safety. LOX Main Pump Delivery Temperature (TOPD) is one of the critical feedback parameter for closed loop MRC. The sensor is mounted in the fluid line, with well-defined protruding length for the valid temperature. Otherwise, engine would work in undesired mixture ratio which may lead to a catastrophe failure or may require additional LOX loading which reduces the satellite pay load. Conventional MRC should take care of these kinds of scenarios, but sometime it becomes insufficient to capture the failure / degradation of the sensors because of the lack of information. Therefore, to provide reliable temperature data for the control system, an intelligence model has been developed using Neuro-Fuzzy Intelligence techniques with engine hot test data as inputs. This model validates the temperature data by taking propulsion parameters and corrects the data in case of data lapse due to the improper immersion length or otherwise. This paper presents the details of intelligence model, validation of the model, outcome and inferences.