A neural network based approach for thrust prediction in cold gas propulsion systems

In this paper, we present a machine learning method to accurately predict thrust in a cold gas thruster using a feedforward neural network (FFNN). The model leverages critical operational parameters, such as storage pressure, mass flow rate, nozzle length, exit pressure, and propellant mass density, to achieve high precision in thrust predictions. To make this technology accessible and practical, we introduce an intuitive graphical user interface (GUI) that allows users to estimate thrust in real-time systems. This tool simplifies design and analysis processes, offering engineers a powerful resource for optimizing the performance of the cold gas thrusters. Based on the simulation results, our proposed method achieves an accuracy of 0.98 and an F1 score of 0.981, showcasing its robustness and generalizability across various test cases. Our work highlights how machine learning methods can be effectively integrated into propulsion system development, paving the way for more innovative, more efficient designs.