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High-resolution TVD, ENO and WENO reconstruction — Examples

1 · Prerequisites

2 · Summary

These examples check the affine-advection reconstruction identities, minmod clipping at a smooth maximum, the Burgers entropy shock and its monotone finite-volume baseline, and the actual face-state order for an explicit WENO-JS epsilon scaling at a first-order critical point. The final counterexample computes the new extrema produced by unlimited centered slopes. All cell-average, nodal, face and time-index conventions are those fixed on the companion A page.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

Minmod clips the slope at a smooth quadratic maximum

Example

For u(x)=1−(x−h/2)2 and cells Ij=[jh,(j+1)h), the cell average in the central cell I0 is U0=1−h2/12 and its two neighbors have U−1=U1=1−13h2/12. The minmod slope in the central cell is zero, so the reconstruction clips the central slope at the smooth maximum.

Facts & Assumptions

Given: h>0, the three cell averages of u(x)=1−(x−h/2)2 over I−1,I0,I1, and the MUSCL/minmod definition.

[F1]

Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: hs0=minmod⁡(U0−U−1,U1−U0) and the central face states are U0±hs0/2.

Verification

1.1givenalgebra

Write x=h/2+y on I0, so y∈[−h/2,h/2] and u=1−y2. Averaging gives U0=1−h2/12. On either neighboring cell the centered coordinate y has mean ±h and variance h2/12, hence U−1=U1=1−(h2+h2/12)=1−13h2/12.

2.1F1step 1.1algebra

Therefore U0−U−1=h2 and U1−U0=−h2. Their signs differ, so [F1] gives s0=0 and both reconstructed face states equal U0.

3.1step 1.1step 2.1algebra∎

The exact profile at either face x=h/2±h/2 is 1−h2/4. The reconstructed face error is ∣U0−(1−h2/4)∣=h2/6. The reconstructed value equals the largest of the three cell averages, so this clipping introduces no new face value outside their range while retaining a second-order face error.

CounterexampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

Unlimited centered reconstruction creates new extrema on a step

Statement refuted

An unlimited centered-slope reconstruction with the reconstructed upwind forward-Euler update is TVD and range-preserving for every scalar grid sequence whenever ∣a∣Δt/h≤1.

Facts & Assumptions

Given: Cells Ii=[ih,(i+1)h), h=a=1, Δt=1/2, initial averages Ui=1 for i≤−1 and Ui=0 for i≥0, and unlimited centered increments σi=(Ui+1−Ui−1)/2.

[F1]

Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: for a>0, the direct update uses Fi+1/2=Ui+σi/2 and Uin+1=Uin−(Δt/h)(Fi+1/2−Fi−1/2).

Counterexample

1.1givenF1algebra

The initial sequence has one jump from 1 to 0, so TV⁡(U0)=1. The centered increments are σ−1=(U0−U−2)/2=−1/2 and σ0=(U1−U−1)/2=−1/2; all other increments vanish.

2.1F1step 1.1algebra

The relevant upwind fluxes are F−3/2=1, F−1/2=3/4, F1/2=−1/4, and F3/2=0. With Δt/h=1/2, the update gives U−11=9/8, U01=1/2, and U11=−1/8, with the adjacent exterior states still 1 and 0.

3.1step 1.1step 2.1algebra∎

The new variation is ∣9/8−1∣+∣1/2−9/8∣+∣−1/8−1/2∣+∣0−(−1/8)∣=3/2>1. The method creates both an overshoot and an undershoot at a CFL number of one half, refuting the statement.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

MUSCL and WENO reconstruct a translating affine profile exactly

Example

Let Ii=[ih,(i+1)h), xi=(i+12)h, and u(x,t)=x−at for a fixed a∈R. On this affine profile, both the minmod MUSCL reconstruction and each of the three normalized WENO5 reconstructions give the exact upwind face value; the corresponding conservative forward-Euler update advances every cell average exactly by one time step.

Facts & Assumptions

Given: h>0, Δt>0, uniform cells Ii, fixed advection speed a, the profile u(x,t)=x−at, and the reconstruction conventions of the A page.

[F1]

Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: cell averages are reconstructed by the displayed MUSCL slope and WENO candidate functionals, normalized weights sum to one, and positive/negative advection selects the appropriate upwind trace.

[F2]

The WENO5 ideal linear weights reproduce quartics at the right face: each WENO candidate functional is exact on polynomials of degree at most two; in particular all three candidate face values agree with the exact face value for affine data.

[F3]

MUSCL/minmod face states are second order on smooth monotone data: on a smooth strictly monotone profile, minmod selects the common-sign one-sided differences and the face states have second-order accuracy.

[F4]

Fixed-epsilon WENO-JS and WENO-Z face reconstructions are fifth order: for C5 data and fixed positive ϵ0, the displayed WENO-JS and WENO-Z reconstructions have fifth-order face accuracy.

Verification

1.1givenF1algebra

Since the average of an affine function over a cell equals its value at the cell center, Uin=xi−atn. Hence Uin−Ui−1n=Ui+1n−Uin=h and the minmod slope is si=1. The right MUSCL trace is Uin+h/2=(i+1)h−atn=u(xi+1/2,tn), with the reflected left trace giving the same exact value at the face.

1.2F1F2F3F4algebra

The WENO candidates reproduce affine polynomials by [F2], so p0=p1=p2=u(xi+1/2,tn). Each normalized weight triple therefore returns this same exact value. The uniform smooth profile also satisfies [F3] and [F4], with zero error here because it is affine.

2.1F1step 1.1step 1.2algebra∎

For a>0, the upwind flux is a((i+1)h−atn) and the left face flux is a(ih−atn); their difference is ah, so the update gives Uin+1=xi−atn−aΔt, the exact average at tn+1. For a<0 the reflected right trace yields the same interface value and flux difference; for a=0 both the flux and time change vanish.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

A first-order critical point exposes WENO-JS epsilon scaling

Example

Work in nondimensional coordinates with u(x)=x2+x3 and reconstruct the face x=0, the interface between cells I−1 and I0. This is a first-order critical point: u′(0)=0 and u′′(0)=2. If the WENO-JS parameter is scaled as ϵh=h4, the cell-average face state has error 5495h4+O(h5), so this branch is fourth order at the face. With the fixed ϵ0>0 of Fixed-epsilon WENO-JS and WENO-Z face reconstructions are fifth order, the theorem's fifth-order branch applies; with the same ϵh, the WENO-Z comparison has error O(h6) on this polynomial.

Facts & Assumptions

Given: Uniform cells Ij=[jh,(j+1)h), u(x)=x2+x3, target face x=0, the displayed WENO-JS/WENO-Z weights, and ϵh=h4>0.

[F1]

Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: the three cell-average candidates are p0,p1,p2, the ideal weights are (1/10,6/10,3/10), and the JS and Z weights use the displayed βk and τ5=∣β0−β2∣.

[F2]

The WENO5 ideal linear weights reproduce quartics at the right face: the ideal linear combination reproduces cubics and each candidate is exact through degree two.

[F3]

Fixed-epsilon WENO-JS and WENO-Z face reconstructions are fifth order: fifth-order face accuracy is proved for fixed positive ϵ0; this does not assert the same order for ϵh=h4.

Verification

1.1givenF1F2algebra

The five stencil averages, ordered from i−2 through i+2 with i=−1, are (Ui−2,Ui−1,Ui,Ui+1,Ui+2)=h2(19/3,7/3,1/3,1/3,7/3)+h3(−65/4,−15/4,−1/4,1/4,15/4). Substitution in the three candidate formulas gives (p0,p1,p2)=h3(−3/2,1/2,−1/2); the quadratic contributions vanish by [F2].

2.1F1step 1.1algebra

Substitution of the same averages in the indicator formulas gives exactly β0=(16/3)h4−37h5+(355/4)h6, β1=(16/3)h4−17h5+(55/4)h6, and β2=(16/3)h4+15h5+(43/4)h6. Put C=19/3 and (c0,c1,c2)=(−37,−17,15); then ϵh+βk=Ch4+ckh5+O(h6).

3.1F1F2step 1.1step 2.1algebra

Expanding αkJ=dk(Ch4+ckh5+O(h6))−2 and normalizing gives ωkJ=dk[1−2(ck−cˉ)h/C+O(h2)], where cˉ=∑dkck=−47/5. Since ∑dkpk=0 by [F2], and ∑kdk(ck−cˉ)(−3/2,1/2,−1/2)k=−9/5, the reconstructed state is ∑kωkJpk=−2h4C(−9/5)+O(h5)=5495h4+O(h5). Thus the face error is not O(h5).

4.1F1F2F3step 1.1step 2.1step 3.1algebra∎

By step 3.1 the scaled-epsilon JS state has a nonzero h4 leading error. For WENO-Z, τ5=∣−52h5+78h6∣=52h5+O(h6), so τ5/(ϵh+βk)=(52/C)h+O(h2) and its square has the same leading O(h2) term for each k. That common term cancels on normalization, giving ωkZ−dk=O(h3). Since the ideal combination is exactly zero on this cubic, [F2] gives ∑kωkZpk=O(h6). This establishes the stated scaled-epsilon contrast; the fixed-ϵ0 theorem in [F3] remains a separate branch.

ExampleConstruction: AI-generatedVerification: AI-generatedprecheck passOpen item page →

Positive-state Burgers shock and the monotone finite-volume baseline

Example

For f(u)=u2/2 and u0(x)=1 for x<0, u0(x)=0 for x>0, the entropy shock moves right at speed 1/2. For states a,b∈[0,1], define the positive-state Burgers Godunov flux by FG(a,b)=min⁡v∈[a,b]f(v) when a≤b and FG(a,b)=max⁡v∈[b,a]f(v) when a>b. Since f is increasing on [0,1], both cases give FG(a,b)=a2/2. Minmod slopes vanish for the initial step, so the first MUSCL flux update is the first-order Godunov update; the batch-29 monotone finite-volume theorem applies to its mesh-refined baseline under Countable Choice and the stated CFL conditions.

Facts & Assumptions

Given: T>0, f(u)=u2/2, u0=1(−∞,0), cells Ij=[jh,(j+1)h), hm=1/m, τm=hm/2, and the displayed MUSCL/minmod reconstruction. The positive-state Godunov flux is defined by the two min/max formulas in the Example.

[A1]

The Axiom of Countable Choice (ACω): ACω asserts that every countable family of nonempty sets has a choice function.

[F1]

Bounded BV data on the real line: BVb(R) consists of bounded locally integrable classes with finite translation variation; it does not require global L1 integrability.

[F2]

Monotone finite-volume scheme and invariant-interval CFL: on an invariant interval I, the shared two-point update uses a locally Lipschitz consistent flux nondecreasing in its first coordinate and nonincreasing in its second, under λ(L1+L2)≤1.

[F3]

Strong entropy convergence of monotone finite-volume schemes: under ACω, bounded BV data, a locally Lipschitz consistent coordinatewise monotone flux on the invariant interval, hm,τm→0, a positive lower bound on τm/hm, and the full CFL bound, the cell-average scheme converges locally to the unique bounded Kruzhkov entropy solution. Dependent Choice is additionally required only on the theorem's u0∈L1(R) branch.

[F4]

Kruzhkov entropy solutions: a Kruzhkov solution satisfies every entropy inequality for ηk(u)=∣u−k∣, qk(u)=sgn⁡(u−k)(f(u)−f(k)), and has a strong local L1 initial trace.

[F5]

Rankine–Hugoniot jumps and Lax shocks: a jump from uL to uR with speed s satisfies the weak jump law exactly when f(uR)−f(uL)=s(uR−uL); in the scalar convex case, a strict Lax shock has f′(uR)<s<f′(uL).

[F6]

Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: the minmod slope is the minmod of the left and right divided differences; a positive-speed face flux uses the reconstructed trace from the cell on its left.

Verification

1.1givenA1F1F3algebra

The jump data are bounded and locally integrable. For any translation length r>0, ∣u0(x+r)−u0(x)∣ equals 1 on an interval of length r and vanishes elsewhere, so TV⁡(u0)=1 by [F1]. The data are not in global L1(R), so [F3] uses only its ACω branch and imposes no DC assumption.

1.2F2F5algebra

For 0≤a≤b≤1, monotonicity of f gives min⁡v∈[a,b]f(v)=f(a); for 0≤b<a≤1, it gives max⁡v∈[b,a]f(v)=f(a). Thus the flux defined in the Example is FG(a,b)=a2/2 on [0,1]2. Use the same polynomial F(a,b)=a2/2 on all of R2 for the theorem's globally defined input. Its restriction is consistent, nondecreasing in a, independent of b, and satisfies ∣F(a,b)−F(a′,b)∣≤∣a−a′∣ on [0,1]2; hence L1=1,L2=0 are valid in [F2]. For the particular jump (1,0), [F5] gives speed s=(f(1)−f(0))/(1−0)=1/2>0, so the interface ray lies on the left state and the flux is the physical Riemann flux.

2.1A1F2F3step 1.1step 1.2given

Here λm=τm/hm=1/2, hm,τm→0, and λm(L1+L2)=1/2≤1 with λm≥1/2. Therefore [F3] applies to the cell-average recurrence and gives convergence to the unique bounded entropy solution, provided the explicit shock below is identified as that solution.

3.1F3F4F5step 2.1algebra

Define u(t,x)=1 for x<t/2 and u(t,x)=0 for x>t/2. The jump speed is s=1/2=(f(1)−f(0))/(1−0), so [F5] gives the weak jump law. For 0≤k≤1, the Kruzhkov entropy-flux jump is qk(0)−qk(1)−12(∣0−k∣−∣1−k∣)=k2−k≤0; for k≤0 and k≥1 direct substitution gives zero. The initial trace is strong locally because the shock moves a distance t/2→0. Hence [F4] identifies this profile as a bounded Kruzhkov solution; uniqueness in [F3] makes it the convergence target from step 2.1.

4.1F2F6step 1.2algebra∎

All cell averages are constant on each side of the initial interface, and both neighboring differences change sign or vanish at the two cells adjacent to it; [F6] therefore gives zero minmod slope in every cell. At the first step F(1,0)=F(1,1)=1/2 and F(0,0)=0, so the cell to the right of the interface becomes 0−(1/2)(0−1/2)=1/4, while the cell to the left remains 1. The exact shock at time τ=h/2 has advanced by h/4, so its average on the first right cell is also 1/4.

Sources