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normalized_bounded

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Normalized vorticity remains bounded by 1 for every index when the input sequence M is strictly positive. Researchers on Navier-Stokes regularity cite the bound to control rescaled fields during extraction of ancient solutions from blowup sequences. The argument is a direct one-line application of the sibling lemma normalized_vorticity_bounded.

claimLet $M : ℕ → ℝ$ satisfy $M(n) > 0$ for all $n$. Let $˜M(n)$ denote the sequence normalized by its running maximum, i.e., $˜M(n) = M(n) / sup_{k ≤ n} M(k)$. Then $˜M(n) ≤ 1$ for all $n$.

background

The module constructs the running-max normalization for Navier-Stokes regularity analysis. Given a sequence of sup-norms $M_n = ‖ω(·, t_n)‖_{L^∞}$ that diverges, the running maximum is the pointwise supremum up to each time, and the normalized vorticity is obtained by dividing the original field by this supremum on rescaled coordinates. The construction yields scale-invariant fields whose $L^∞$ norm is at most 1 by design, relying only on monotonicity of the supremum and positivity of the sequence.

proof idea

The proof is a one-line wrapper that applies normalized_vorticity_bounded to the inputs M and the positivity hypothesis hpos.

why it matters in Recognition Science

The theorem supplies the boundedness component of runningMaxCert, which certifies the full set of properties (monotonicity, boundedness, TF pinch, rigid rotation) needed for the running-max construction. It completes the scale-invariant bound step in the Navier-Stokes regularity argument that extracts ancient elements from hypothetical blowup sequences. In the Recognition Science setting this supports the topological-frustration analysis of vorticity within the forcing-chain framework.

scope and limits

formal statement (Lean)

 136theorem normalized_bounded (M : ℕ → ℝ) (hpos : ∀ n, 0 < M n) :
 137    ∀ n, normalized M n ≤ 1 :=

proof body

Term-mode proof.

 138  fun n => normalized_vorticity_bounded M hpos n
 139
 140/-! ## TF Pinch at Zero Node (Thm 5.5) -/
 141
 142/-- **TF Pinch at a zero node**: if the J-cost function J(x) has a zero at
 143    x₀ in the interior of the domain, then the vorticity direction is constant
 144    in a neighborhood of x₀.
 145
 146    This is pure convex analysis: J is strictly convex (J'' > 0), so
 147    J(x₀) = 0 implies x₀ = 1 (the unique minimum). At x = 1, the vorticity
 148    direction is determined by the leading-order quadratic expansion J ≈ ε²/2.
 149
 150    In RS: the TF (topological frustration) prevents simultaneous vanishing of
 151    both the vorticity magnitude and direction — one of them must persist. -/

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