Geometric Vorticity Control ver.3 with Supplementary Material ver.2

This paper presents a constructive geometric framework for generating classical solutions to the three-dimensional incompressible Navier–Stokes equations. The proposed method extends the Coherence Tube Density (CTD) framework to a spherical formulation by introducing a three-dimensional radial coherence structure. Representative vectors are extracted from concentric spherical layers, from which representative particle trajectories are constructed. The collection of these trajectories is then synthesized into a particle ensemble that reconstructs the velocity, pressure, and vorticity fields. The framework combines spherical coherence analysis, layered (Baum-Kuchen) decomposition, trajectory synthesis, and kernel-based field reconstruction into a unified geometric approach. It provides a constructive interpretation of fluid motion and investigates the resulting regularity through coherence-based spacing constraints. This repository contains the manuscript describing the proposed framework.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-14
DOI
https://doi.org/10.5281/zenodo.22750717
Primary Topic
Model Reduction and Neural Networks
Type
preprint
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preprint

Geometric Vorticity Control ver.3 with Supplementary Material ver.2

Ren Matsuoka
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

Geometric Vorticity Control ver.3 with Supplementary Material ver.2

Ren Matsuoka
preprint en

Abstract

This paper presents a constructive geometric framework for generating classical solutions to the three-dimensional incompressible Navier–Stokes equations. The proposed method extends the Coherence Tube Density (CTD) framework to a spherical formulation by introducing a three-dimensional radial coherence structure. Representative vectors are extracted from concentric spherical layers, from which representative particle trajectories are constructed. The collection of these trajectories is then synthesized into a particle ensemble that reconstructs the velocity, pressure, and vorticity fields. The framework combines spherical coherence analysis, layered (Baum-Kuchen) decomposition, trajectory synthesis, and kernel-based field reconstruction into a unified geometric approach. It provides a constructive interpretation of fluid motion and investigates the resulting regularity through coherence-based spacing constraints. This repository contains the manuscript describing the proposed framework.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Model Reduction and Neural Networks
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