From Spectral–Covariance Transfer to Validated Engineering Wall Laws: Nikuradse Roughness, Closure Discrimination, and Riblet Transport Diagnostics

This paper develops a traceable engineering reduction from a spectral–covariance reconstruction of forced incompressibleNavier–Stokes dynamics and then subjects the resulting walllaw architecture to two complementary external tests. Themathematical reduction retains the source carrier normalization, pulse-age normalization, energy-metric correction, exact frozenprojection to physical wall coordinates, and the exact pipe meanmomentum balance before any friction approximation.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-13
DOI
https://doi.org/10.5281/zenodo.22736347
Primary Topic
Model Reduction and Neural Networks
Type
preprint
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preprint

From Spectral–Covariance Transfer to Validated Engineering Wall Laws: Nikuradse Roughness, Closure Discrimination, and Riblet Transport Diagnostics

Jorge Hernando Torres Ribero
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

From Spectral–Covariance Transfer to Validated Engineering Wall Laws: Nikuradse Roughness, Closure Discrimination, and Riblet Transport Diagnostics

Jorge Hernando Torres Ribero
preprint en

Abstract

This paper develops a traceable engineering reduction from a spectral–covariance reconstruction of forced incompressibleNavier–Stokes dynamics and then subjects the resulting walllaw architecture to two complementary external tests. Themathematical reduction retains the source carrier normalization, pulse-age normalization, energy-metric correction, exact frozenprojection to physical wall coordinates, and the exact pipe meanmomentum balance before any friction approximation.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities, Peace, Justice and strong institutions
Model Reduction and Neural Networks
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