Spatial Dimension, Internal Phase, and Kinematics of Helical Line Families
This preprint develops differential-geometric tools for helical coordinates, spatial dimension, internal phase, and kinematics. It studies how families of one-dimensional helical curves can represent three-dimensional effective positions while retaining an independent phase degree of freedom. On a selected regular local position chart, the construction forms a circle bundle over a three-dimensional effective position space. The complete position–phase state space is four-dimensional; the effective spatial dimension follows from the chosen position projection. Connections and moving frames provide covariant phase comparison, loop compatibility conditions, and explicit velocity and acceleration formulas for arbitrary position–phase paths. The map from complete states to ambient positions has rank three and a degenerate Euclidean pullback, so ambient position alone does not determine the full state. Selecting a common initial phase yields an explicit helical coordinate system on a proven injective domain, with a closed-form inverse, Jacobian, second derivatives, and a flat Euclidean pullback metric. These formulas map the same kinematic trajectory between Cartesian and helical coordinates. Finite-radius and finite-resolution analysis shows why small position offsets need not imply small tangent corrections or equivalent acceleration averages. The paper situates these tools within the mathematical history of intrinsic geometry, moving frames, and fiber bundles, and relates them to published The Emergent Frame (TEF) rollout constructions. It distinguishes local spatial dimension from large-scale volume growth and states the assumptions needed for subsequent physical applications.
Authors
- Xiaodan Wu (ORCID: https://orcid.org/0009-0005-5892-0293)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-25
- DOI
- https://doi.org/10.5281/zenodo.22945611
- Primary Topic
- Structural Analysis and Optimization
- Type
- preprint