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Hydrostatic HLL face fluxes converge under strong state and velocity convergence
Statement
Assume the Axiom of Countable Choice . Let tend to zero, set , , , and , and let be locally Lipschitz. On each cell-time slab let be piecewise constant, with , when , and . Suppose and strongly in , almost everywhere where , and are locally uniformly bounded. Let for , with face flux from Hydrostatic reconstruction with a dry-safe HLL flux and one-sided pressure corrections and bed samples . Then The primitive-velocity convergence and uniform velocity bound are included because the assigned wave-speed formula uses on wet states, which is not continuous at a dry state as a function of alone.
Facts & Assumptions
Given: , the mesh and bed convention, local uniform bounds, strong local convergence of , and the hydrostatic HLL face flux.
is countable choice (The Axiom of Countable Choice ()).
Hydrostatic reconstruction with a dry-safe HLL flux and one-sided pressure corrections: adjacent states are reconstructed by clipping depths against the maximum bed; face velocities are retained when reconstructed depth is positive and set to zero at reconstructed dry states; the HLL speed and flux branches are the displayed ones.
Under , spatial translations are continuous in ( in as , for ).
Under , bounded measurable subsets of have finite measure (Lebesgue measure is sigma-finite, and every metrically bounded subset of has finite outer measure).
Strong convergence implies convergence in measure; every sequence converging in measure has a subsequence converging almost everywhere (Convergence in L^1(mu) implies convergence in measure, Riesz's subsequence theorem for convergence in measure).
Almost-everywhere convergence under an integrable majorant implies convergence (Dominated convergence).
Proof
Fix a compact space-time rectangle and a fixed compact enlargement containing and every spatial translate for all sufficiently large . Choose local bounds so that and on . Strong local convergence implies the same bounds for almost everywhere on : the sum tends to zero in , so [F4] gives a subsequence on which both converge almost everywhere and inherit the bounds. By [F3], multiplying by the indicator of therefore gives functions. The given strong convergence gives and in . Applying [F2] to the cutoffs shows and in . Strong local convergence on and the triangle inequality then show and in as well.
Let be a Lipschitz constant for on the spatial enlargement, and write and . Hydrostatic clipping changes either adjacent depth by at most . Take any subsequence of . By step 1.1, the sum of the four errors converges to zero in measure on ; [F4] gives a further subsequence on which all four errors tend to zero almost everywhere. At a point where , both cell depths are eventually larger than , so neither reconstructed state is clipped to zero and both reconstructed velocities converge to . At a point where , the hypothesis gives and both cell velocities converge to zero; resetting either velocity to zero when its reconstructed depth vanishes therefore still gives convergence to . The reconstructed depths in both states converge to as well. Thus along this common further subsequence both reconstructed primitive states converge almost everywhere to . Since the starting subsequence was arbitrary, every subsequence has such a further subsequence.
At a wet limit state , the two face inputs tend to the same ; the left, middle and right HLL branches all tend to , and the formulas agree at the branch boundaries. At a dry limit state, and , so both reconstructed fluxes and both speeds tend to zero. In the middle branch write , ; its equivalent formula is . Here , which tends to zero at the dry state, while tend to zero. The outer branches also tend to zero. Thus along the almost-everywhere convergent further subsequence from step 2.1, the HLL flux tends almost everywhere to . The same equivalent formula and give a uniform bound on the fluxes on .
Starting from any subsequence, step 2.1 supplies a further subsequence with almost-everywhere convergence of both face inputs; step 3.1 gives almost-everywhere convergence of its fluxes and a uniform bound. Since has finite measure by [F3], [F5] yields convergence along that further subsequence. If the full flux sequence failed to converge in , some subsequence would stay a fixed positive distance from , contradicting this property. Therefore in . As was arbitrary, the convergence is local.
Depends on
- One-dimensional shallow-water balance law with a dry-state convention
- Hydrostatic reconstruction with a dry-safe HLL flux and one-sided pressure corrections
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Dominated convergence
- Convergence in L^1(mu) implies convergence in measure
- Lebesgue measure is sigma-finite, and every metrically bounded subset of $\mathbb{R}^n$ has finite outer measure
- Riesz's subsequence theorem for convergence in measure
- $\|\tau_h f - f\|_p \to 0$ in $L^p(\mathbb{R}^n)$ as $h \to 0$, for $1 \le p < \infty$
Used by
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Sources
- H. De Sterck and P. Ullrich, Introduction to Computational PDEs, Chapter 3 (standard reference, not scraped)
- Benjamin Seibold, MIT OpenCourseWare 18.336, Lecture 19: Finite Volume Methods (standard reference, not scraped)
- E. Audusse, F. Bouchut, M.-O. Bristeau, R. Klein, and B. Perthame, A Fast and Stable Well-Balanced Scheme with Hydrostatic Reconstruction for Shallow Water Flows (standard reference, not scraped)