Vertexformer: the interpretable predictive research on thermocapillary convection of large Prandtl number liquid bridges

This study investigates thermocapillary convection in large-Prandtl-number liquid bridges under microgravity. A novel, interpretable deep learning framework, Vortexformer-SHAP, is developed to accurately predict flow dynamics and uncover underlying physics from experimental data. The lightweight Vortexformer model, with 0.02 billion parameters, employs spatiotemporal attention mechanisms and achieves superior performance (R² > 0.99, RMSE = 0.2213) compared to baseline models. SHAP-based interpretability analysis rediscovers the Geometry Effect and reveals two key findings. First, the bifurcation effect of cold-end bridge temperature suggests the critical role of solid heat transfer relevant to the floating-zone liquid bridge. Second, an intrinsic oscillatory thermocapillary convection periodic law of 10–15 time steps (approximately 5–7 s) is identified within the liquid bridge geometric range of the experiment. This work provides a new paradigm integrating a customized deep learning model with physical interpretation, offering both a technical foundation and theoretical insights for real-time prediction and intelligent control of complex thermal-fluid systems in space.