• An active image- and model-based framework for LRCs airtightness is built. • A fractal-based damage variable is defined to quantify sealing layer deterioration. • Derive a closed-form damage–permeability mapping with improved physical interpretability. • The framework enables cavern-scale leakage prediction via THM coupled simulations. • Dynamic early warning is achieved by converting image data into leakage metrics. Lined rock caverns (LRCs) offer a flexible solution for large-scale hydrogen storage, but sealing layer damage and subsequent leakage pose a critical safety risk. Existing airtightness evaluation methods predominantly rely on passive detection after leakage occurs, limiting early warning capability. To enable proactive risk management, this study proposes an integrated image- and model-based framework for active airtightness evaluation and early warning of LRCs. The framework processes images of sealing-layer cracks via adaptive filtering to extract clear crack patterns, from which a damage variable is quantified using fractal theory. A closed-form damage-permeability mapping is then derived via a hybrid series–parallel model of the crack-matrix system. This mapping is embedded into a thermo-hydro-mechanical (THM) coupled model to dynamically compute key airtightness indicators, including leakage rate and leakage mass percentage (LMP). Validation using tensile test images and LRCs thermodynamic response data reported in the literature confirms that the proposed image processing method effectively extracts and quantifies crack damage features, and the constructed THM model accurately simulates pressure and temperature variations within the LRCs, facilitating the calculation of airtightness indicators. This framework offers a mechanism-driven and engineering-oriented technical pathway from post-leakage detection toward proactive airtightness evaluation and early warning for LRCs hydrogen storage.
Zhang et al. (Wed,) studied this question.