Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search. (February 2023)

• Record Type: Journal Article
• Authors: Rautela, Mahindra; Mirfarah, Motahareh; Silva, Christian E.; Dyke, Shirley; Maghareh, Amin; Gopalakrishnan, S.
• Is Part Of: Acta astronautica. Volume 203(2023)
• Page Start: 385
• Page End: 391
• Publication Date: 2023-02
• Subjects: Deep space habitats -- Leakage estimation -- Real-time inversion -- Exponential weighting -- Adaptively-refined search; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2022.12.003
• Languages: English
• ISSNs: 0094-5765

Abstract

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. Highlights: Leakage estimation in deep space habitats is proposed as an inverse problem. A novel exponential-weighted adaptively-refined search (EWARS) scheme is implemented. EWARS is a real-time model calibration framework. EWARS estimates are fast and less noisy as compared to full brute force search.