An Adaptive Real-Time Forecasting Framework for Cryogenic Fluid Management in Space Systems

TL;DR

This paper introduces ARCTIC, a lightweight, adaptive framework that enhances real-time cryogenic tank forecasts in space systems by integrating sensor data with precomputed models, improving accuracy and robustness.

Contribution

The study presents ARCTIC, a novel adaptive, data-driven correction method for real-time cryogenic fluid management, capable of continuous system state adaptation without altering underlying models.

Findings

ARCTIC improves forecast accuracy under model imperfections and noise.

The framework demonstrates robustness in synthetic and experimental scenarios.

ARCTIC is suitable for onboard autonomous space system applications.

Abstract

Accurate real-time forecasting of cryogenic tank behavior is essential for the safe and efficient operation of propulsion and storage systems in future deep-space missions. While cryogenic fluid management (CFM) systems increasingly require autonomous capabilities, conventional simulation methods remain hindered by high computational cost, model imperfections, and sensitivity to unanticipated boundary condition changes. To address these limitations, this study proposes an Adaptive Real-Time Forecasting Framework for Cryogenic Propellant Management in Space Systems, featuring a lightweight, non-intrusive method named ARCTIC (Adaptive Real-time Cryogenic Tank Inference and Correction). ARCTIC integrates real-time sensor data with precomputed nodal simulations through a data-driven correction layer that dynamically refines forecast accuracy without modifying the underlying model. Two updating mechanisms, auto-calibration and observation and correction, enable continuous adaptation to evolving system states and transient disturbances. The method is first assessed through synthetic scenarios representing self-pressurization, sloshing, and periodic operations, then validated using experimental data from NASA's Multipurpose Hydrogen Test Bed and K-Site facilities. Results demonstrate that ARCTIC significantly improves forecast accuracy under model imperfections, data noise, and boundary fluctuations, offering a robust real-time forecasting capability to support autonomous CFM operations. The framework's compatibility with existing simulation tools and its low computational overhead make it especially suited for onboard implementation in space systems requiring predictive autonomy.