"The Fan Does Not Inhale: Core-Induced Inflow in the SIIEM Model"

https://www.youtube.com/watch?v=Izoh9gOB8Ek This dataset originates from long-term observational experiments using an axially aligned dual-fan setup. Despite repeatedly watching the experimental video over several years, the airflow phenomena captured could not be explained by existing principles of fluid mechanics, tornado models, or the second law of thermodynamics. Initially, the structure appeared to be a simple mechanical fan-driven flow. However, further analysis revealed that the fan was not the source of suction. Instead, a naturally formed rotational core was inducing the inflow. A crucial breakthrough occurred when the same experiment was observed from a side angle, where a short-tailed vortex and an inflow without any mechanical cause were clearly visible—contradicting conventional aerodynamic theories. Through high-level analytical collaboration with AI and continued hypothesis testing, these structures were formalized into a new model: the Suction-Induced Inflow Extension Model (SIIEM). This led to the derivation of 12 new physical equations, quantitatively describing the core formation, energy convergence, rotational inflow, and core collapse mechanisms—none of which can be explained by classical models. Importantly, this research identifies that the rotational flow model proposed by Dalal & Balachandar fundamentally conflicts with existing fluid dynamics, tornado theories, and thermodynamic laws. To resolve these contradictions, we propose 6 additional physical equations, offering a new, order-based framework for understanding natural flow structures beyond conventional mechanical assumptions. This dataset includes: Front and side view experimental videos AI-based structural flow analysis Annotated images of vortex formation and inflow behavior Documentation of the 12 derived physical equations This research proposes a paradigm shift from mechanism-driven science to a structure- and order-centered interpretation of nature. It represents one of the first cases where AI helped uncover hidden physical order not perceptible to human intuition, marking a foundational step toward a new frontier in physics and natural analysis.