Establishing generalizable statistical prediction models and identifying primary controlling factors for nonlinear flow characteristic parameters in rough fractures

doi:10.7523/j.ucas.2026.035

Abstract

Abstract: Rock fractures are one of the main pathways for groundwater seepage and contaminant transport. Under the combined control of geometric characteristics such as aperture, roughness, and intersection configuration, as well as hydraulic gradient, the overall seepage in natural rough fractures may deviate from the traditional cubic law and exhibit nonlinear characteristics that can be described by the Forchheimer equation. For typical rough disc-shaped fractures, this study constructed a set of fracture geometric characteristics and hydrodynamic parameters affecting seepage, and determined representative parameter combinations through an orthogonal experimental design. Rough fracture surfaces were generated using a stochastic method controlled by power spectral density (PSD), and seepage-related parameters under 162 representative scenarios were obtained through numerical simulations. Based on the Forchheimer nonlinear seepage fitting equation, full-factor regression prediction models were established for nonlinear seepage characteristic parameters, including the linear coefficient A, nonlinear coefficient B, and nonlinear effect factor E. Furthermore, their primary controlling factors were identified through stepwise regression and analysis of variance (ANOVA). The results show that fracture size, outlet intersection-line length and mean aperture are key factors affecting nonlinear seepage behavior. The validation results based on numerical simulation data indicate that the statistical prediction models established in this study for nonlinear seepage characteristic parameters have good generalization capability, providing a reference for the rapid prediction and quantitative characterization of nonlinear seepage characteristic parameters in typical rough fractures.

Key words: rough disc-shaped fractures, fracture intersection configuration, nonlinear flow, statistical regression, generalizable prediction model

CLC Number:

X523

WANG Siyu, QU Cixiao, WANG Mingyu. Establishing generalizable statistical prediction models and identifying primary controlling factors for nonlinear flow characteristic parameters in rough fractures[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2026.035.

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