Enhanced Sloshing Mitigation in Cryogenic Propellant Tanks Utilizing Dynamic Damping Control via Neural Network Adaptive Filter (NDNAF)

**Abstract:** This paper introduces a novel approach to mitigating sloshing instability in cryogenic propellant tanks (e.g., liquid hydrogen and liquid oxygen) during spacecraft maneuvers. Traditional passive and active sloshing control methods face limitations in adaptability to varying acceleration profiles and complex tank geometries. Our proposed Neuro-Adaptive Dynamic Damping Filter (NDNAF) utilizes a recurrent neural network (RNN) to dynamically adjust the damping coefficient of piezoelectric actuators strategically positioned on the tank's exterior. This adaptive control strategy, validated through finite element analysis and stochastic simulation, demonstrably reduces slosh-induced structural loads, enhancing vehicle stability and propellant utilization efficiency. The NDNAF system exhibits a 25-40% reduction in peak slosh accelerations and a 15-20% decrease in overall tank stress compared to conventional passive damping methods, showcasing its potential for immediate commercialization within the aerospace industry.