Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search

TL;DR

This paper introduces a real-time leakage estimation method for deep space habitats using an exponentially-weighted adaptively-refined search algorithm, validated through experiments on a pressure chamber, addressing safety concerns in extraterrestrial environments.

Contribution

The paper presents a novel real-time leakage estimation technique based on EWARS for deep space habitats, improving safety and reliability in extreme conditions.

Findings

The method accurately estimates constant leaks in real-time.

The approach effectively tracks variable leakage scenarios.

Experimental validation confirms the method's robustness and speed.

Abstract

The recent accelerated growth in space-related research and development activities makes the near-term need for long-term extraterrestrial habitats evident. Such habitats must operate under continuous disruptive conditions arising from extreme environments like meteoroid impacts, extreme temperature fluctuations, galactic cosmic rays, destructive dust, and seismic events. Loss of air or atmospheric leakage from a habitat poses safety challenges that demand proper attention. Such leakage may arise from micro-meteoroid impacts, crack growth, bolt/rivet loosening, and seal deterioration. In this paper, leakage estimation in deep space habitats is posed as an inverse problem. A forward pressure-based dynamical model is formulated for atmospheric leakage. Experiments are performed on a small-scaled pressure chamber where different leakage scenarios are emulated and corresponding pressure values are measured. An exponentially-weighted adaptively-refined search (EWARS) algorithm is developed and validated for the inverse problem of real-time leakage estimation. It is demonstrated that the proposed methodology can achieve real-time estimation and tracking of constant and variable leaks with accuracy.