Recursive Attractor Survivability Diagnostics Dataset (Taylor–Green Vortex, Navier–Stokes)

This dataset contains survivability diagnostics for a 3D incompressible Navier–Stokes simulation (Taylor–Green vortex, ν=1e-3, N=48³ grid). The simulation was run under the Recursive Attractor Survivability Framework described in Broomhead (2025). Contents: S — Survivability S(t), shape (400,) tau — Recursive memory depth τ(t) Q — Energy–coherence ratio Q(t) Phi, dPhi — Spectral log-energy and its slope sigma — Spectral coherence proxy C — Global coherence operator C(T) Erec — Renormalized enstrophy E_ts — Dyadic shell energies E_n(t), shape (400, 14) dt, nu, L, N, edges — Simulation parameters Format: NumPy compressed archive (.npz). How to load: import numpy as np data = np.load("ns_admissibility_tgv.npz") S, tau, Q, C, Erec = data["S"], data["tau"], data["Q"], data["C"], data["Erec"]