Emergence Inevitability and Algebraic Computational Methods of Turbulence Based on Discrete Microscopic Causal Statistics and Multidimensional Manifold Reconstruction

The classical Navier-Stokes equations formulate fluid dynamics within a continuous continuum medium, which inherently encounters mathematical singularities (blow-up) and divergence difficulties under extreme turbulent conditions. To bypass these continuous barriers, this paper establishes an entirely new discrete dynamical paradigm derived from Status-Relational Entropy (SRE) dynamics and multidimensional metric scaling. We reconstruct continuous fluid media as a statistical information network governed purely by local topological invariants and causal correlations among massive discrete microscopic states. The framework is driven by a composite functorial chain of three primary operators: (1) The Local Graph Expansion Operator ($\mathcal{G}_{n \to n+1}$) that expands the system via a single-step, increment-wise structural equation while securing read-only subspace inheritance and a universal diagonal path-interaction invariant. (2) The Local Metric and Probabilistic Pruning Operator ($\mathcal{M}_\chi \circ \mathcal{E}_{\text{local}}$) that utilizes the historical sub-graph spectral radius to decouple parameter deadlocks and enforces a maximum-entropy Boltzmann pruning probability under the strictly discrete Forced Spin-1 rule. (3) The Final Allocation Operator No. 3 that introduces a 5-node non-homogeneous pentagonal lattice to break parity symmetry, spontaneously instantiating universal NAND logic to achieve complete Turing completeness. Furthermore, we establish a rigorous Chapman-Enskog asymptotic expansion, proving that our algebraic master equation precisely degenerates into the standard Navier-Stokes equations under the continuum limit. Numerical empirical simulations demonstrate that under zero artificial constraints, the macroscopic coherence order parameter $\Phi(N)$ is strictly locked within a robust time-delay Lyapunov attractor envelope $[0.75, 1.00]$, breaking away from the 0.5 disordered baseline. Multidimensional scaling reconstructions visually confirm that un-pruned chiral core paths spontaneously condense into a highly connected, bounded toroidal attractor loop representing the topological manifold confinement of a vortex filament, centripetally enveloped by a diffuse chaotic shell. This methodology provides a solid, axiomatic mathematical foundation for computing complex fluid behaviors entirely from discrete networks.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-27
DOI
https://doi.org/10.5281/zenodo.21618867
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

Emergence Inevitability and Algebraic Computational Methods of Turbulence Based on Discrete Microscopic Causal Statistics and Multidimensional Manifold Reconstruction

Yue Lu
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Emergence Inevitability and Algebraic Computational Methods of Turbulence Based on Discrete Microscopic Causal Statistics and Multidimensional Manifold Reconstruction

Yue Lu
article en

Abstract

The classical Navier-Stokes equations formulate fluid dynamics within a continuous continuum medium, which inherently encounters mathematical singularities (blow-up) and divergence difficulties under extreme turbulent conditions. To bypass these continuous barriers, this paper establishes an entirely new discrete dynamical paradigm derived from Status-Relational Entropy (SRE) dynamics and multidimensional metric scaling. We reconstruct continuous fluid media as a statistical information network governed purely by local topological invariants and causal correlations among massive discrete microscopic states. The framework is driven by a composite functorial chain of three primary operators: (1) The Local Graph Expansion Operator ($\mathcal{G}_{n \to n+1}$) that expands the system via a single-step, increment-wise structural equation while securing read-only subspace inheritance and a universal diagonal path-interaction invariant. (2) The Local Metric and Probabilistic Pruning Operator ($\mathcal{M}_\chi \circ \mathcal{E}_{\text{local}}$) that utilizes the historical sub-graph spectral radius to decouple parameter deadlocks and enforces a maximum-entropy Boltzmann pruning probability under the strictly discrete Forced Spin-1 rule. (3) The Final Allocation Operator No. 3 that introduces a 5-node non-homogeneous pentagonal lattice to break parity symmetry, spontaneously instantiating universal NAND logic to achieve complete Turing completeness. Furthermore, we establish a rigorous Chapman-Enskog asymptotic expansion, proving that our algebraic master equation precisely degenerates into the standard Navier-Stokes equations under the continuum limit. Numerical empirical simulations demonstrate that under zero artificial constraints, the macroscopic coherence order parameter $\Phi(N)$ is strictly locked within a robust time-delay Lyapunov attractor envelope $[0.75, 1.00]$, breaking away from the 0.5 disordered baseline. Multidimensional scaling reconstructions visually confirm that un-pruned chiral core paths spontaneously condense into a highly connected, bounded toroidal attractor loop representing the topological manifold confinement of a vortex filament, centripetally enveloped by a diffuse chaotic shell. This methodology provides a solid, axiomatic mathematical foundation for computing complex fluid behaviors entirely from discrete networks.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 9%
Model Reduction and Neural Networks
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