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.