fluentFluid

The rapid diffusion and steep concentration gradients of ship exhaust plumes create substantial challenges in velocity field estimation and quantification. To address this limitation, this study develops FluentFluid, the first large-scale optical flow dataset specifically designed for plume motion analysis, as a standardized training benchmark. A novel deep optical flow network architecture guided by grayscale attention mechanisms is proposed. The architecture adopts a dual enhancement strategy through Effective Grayscale Pixels (EGPs) and Grayscale Attention Weights. This design enables targeted focus on regions with strong gas concentration gradients. Building upon the EGP concept, we introduce the Grayscale Endpoint Error metric and establish a unified evaluation framework for ship exhaust plume monitoring. Experimental results show that the grayscale attention-enhanced RAFT_GA model achieves 13.6% higher optical flow prediction accuracy in EGP regions compared with baseline models, with 74.14% improvement over conventional approaches. Field validation demonstrates 87.45% phase accuracy in real-world scenarios, confirming the attention mechanism’s effectiveness in gas motion field prediction. The proposed framework provides critical insights for motion analysis in ultraviolet/infrared spectral domains, particularly under nighttime or high-luminance conditions.