EXISTENCE AND SMOOTHNESS OF NAVIER STOKES
Description (Abstract) This work presents an accessible and innovative approach to the mathematical problem of the Existence and Smoothness of the three-dimensional incompressible Navier-Stokes equations. The proposed formulation introduces an explicit structural damping term (−λu) to the classical equation, establishing an additional dissipative mechanism that acts directly against the velocity field. The central focus of the analysis is the dynamics of vorticity (ω = ∇×u), proposing a control inequality that guarantees the exponential decay of the vorticity norm and prevents the unbounded concentration of gradients in finite time. Beyond traditional fluid dynamics modeling, the article integrates an "Autonomous Transduction Architecture" — structured in cycles of Latency, Structure, Generation, State, Refinement, Memory, Coverage, Branching, Recovery, and Audit. This architecture allows the transition from continuous dynamics to defined, computationally measurable, and auditable states, incorporating concepts of spiral geometry, chiral asymmetry, and relativistic limitations. Computational validation is performed using a three-dimensional Taylor-Green flow simulation. Under the tested conditions, the application of structural damping (λ = 1) reduced the kinetic energy to 1.62% of its initial value and decreased the vorticity norm by 86.86%. In contrast, the classical undamped regime (λ = 0) resulted in an approximate 10.92% increase in the vorticity norm. The document establishes a structured analytical and computational pathway for the control of nonlinear vortical dynamics. The work clearly distinguishes the numerical stabilization results of the modified formulation from a rigorous analytical proof for the classical equations, serving as a foundation for interdisciplinary research at the intersection of mathematics, computation, geometry, and the philosophy of science.
Authors
- Silva Vicente da Silva
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-11
- DOI
- https://doi.org/10.5281/zenodo.22707390
- Primary Topic
- Model Reduction and Neural Networks
- Type
- preprint