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High-resolution TVD, ENO and WENO reconstruction — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Absolute Continuity and the Sharp Fundamental Theorem of Calculus
- Approximation and Compactness in C(K)
- Areas of Elementary Plane Figures
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Variation and the Riemann–Stieltjes Integral
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Conservative finite-volume weak limits
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Convex and Semicontinuous Functions on Rⁿ
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Darboux, L'Hôpital, and Taylor's Theorem
- Density Separability and Convolution in Lᵖ
- Determinants of Matrices over a Commutative Ring
- Differentiation of Monotone Functions and the Vitali Covering Theorem
- Distributions Test Functions and Differentiation
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability and the Probabilistic Method
- Foundations of the Real Numbers for Analysis
- Fubini and Change of Variables
- Fundamental Trigonometric Identities
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Harmonic Functions and Mean Values in Rn
- Heat Equation Maximum Principles Duhamel and Smoothing
- High-resolution TVD, ENO and WENO reconstruction
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Improper and Parameter-Dependent Multiple Integrals
- Improper Integrals
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Lebesgue Measure on Euclidean Space
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- Modes of Convergence Egorov and Lusin
- Monotone finite-volume entropy convergence
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Partial Differential Equations and Characteristics
- Pointwise Extended-Real Function Products
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Product Measures and the Fubini Tonelli Theorems
- Properties of the Integral and the Working FTC
- Regular Surfaces and Surface Integrals
- Relations, Functions, and Quotients
- Rellich Kondrachov and Sobolev Compactness
- Riemann solvers for hyperbolic systems
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Scalar Conservation Laws and Entropy Solutions
- Schwartz Space and the Plancherel Theorem
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Sine, Cosine, and the Definition of Pi
- Smooth Partitions of Unity and Exhaustions
- Subspaces, Products, and Quotients
- Suprema and Infima
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Derivative and the Mean Value Theorems
- The Divergence Theorem and Classical Stokes
- The Exponential Function
- The Fundamental Theorems of Calculus
- The Heat Kernel and the Cauchy Problem
- The Inverse and Implicit Function Theorems
- The Inverse Function Theorem Completed
- The Lebesgue and Riemann Integrals Compared
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Maximal Function and Lebesgue Differentiation
- The Radon Nikodym Theorem and Lebesgue Decomposition
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
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
Minmod clips the slope at a smooth quadratic maximum
Example
For and cells , the cell average in the central cell is and its two neighbors have . The minmod slope in the central cell is zero, so the reconstruction clips the central slope at the smooth maximum.
Facts & Assumptions
Given: , the three cell averages of over , and the MUSCL/minmod definition.
Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: and the central face states are .
Verification
Write on , so and . Averaging gives . On either neighboring cell the centered coordinate has mean and variance , hence .
Therefore and . Their signs differ, so [F1] gives and both reconstructed face states equal .
The exact profile at either face is . The reconstructed face error is . 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.
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 .
Facts & Assumptions
Given: Cells , , , initial averages for and for , and unlimited centered increments .
Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: for , the direct update uses and .
Counterexample
The initial sequence has one jump from to , so . The centered increments are and ; all other increments vanish.
The relevant upwind fluxes are , , , and . With , the update gives , , and , with the adjacent exterior states still and .
The new variation is . The method creates both an overshoot and an undershoot at a CFL number of one half, refuting the statement.
MUSCL and WENO reconstruct a translating affine profile exactly
Example
Let , , and for a fixed . 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: , , uniform cells , fixed advection speed , the profile , and the reconstruction conventions of the A page.
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.
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.
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.
Fixed-epsilon WENO-JS and WENO-Z face reconstructions are fifth order: for C5 data and fixed positive , the displayed WENO-JS and WENO-Z reconstructions have fifth-order face accuracy.
Verification
Since the average of an affine function over a cell equals its value at the cell center, . Hence and the minmod slope is . The right MUSCL trace is , with the reflected left trace giving the same exact value at the face.
The WENO candidates reproduce affine polynomials by [F2], so . 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.
For , the upwind flux is and the left face flux is ; their difference is , so the update gives , the exact average at . For the reflected right trace yields the same interface value and flux difference; for both the flux and time change vanish.
A first-order critical point exposes WENO-JS epsilon scaling
Example
Work in nondimensional coordinates with and reconstruct the face , the interface between cells and . This is a first-order critical point: and . If the WENO-JS parameter is scaled as , the cell-average face state has error , so this branch is fourth order at the face. With the fixed of Fixed-epsilon WENO-JS and WENO-Z face reconstructions are fifth order, the theorem's fifth-order branch applies; with the same , the WENO-Z comparison has error on this polynomial.
Facts & Assumptions
Given: Uniform cells , , target face , the displayed WENO-JS/WENO-Z weights, and .
Finite-volume MUSCL, nodal ENO2 and WENO5 reconstruction conventions: the three cell-average candidates are , the ideal weights are , and the JS and Z weights use the displayed and .
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.
Fixed-epsilon WENO-JS and WENO-Z face reconstructions are fifth order: fifth-order face accuracy is proved for fixed positive ; this does not assert the same order for .
Verification
The five stencil averages, ordered from through with , are . Substitution in the three candidate formulas gives ; the quadratic contributions vanish by [F2].
Substitution of the same averages in the indicator formulas gives exactly , , and . Put and ; then .
Expanding and normalizing gives , where . Since by [F2], and , the reconstructed state is . Thus the face error is not .
By step 3.1 the scaled-epsilon JS state has a nonzero leading error. For WENO-Z, , so and its square has the same leading term for each . That common term cancels on normalization, giving . Since the ideal combination is exactly zero on this cubic, [F2] gives . This establishes the stated scaled-epsilon contrast; the fixed- theorem in [F3] remains a separate branch.
Positive-state Burgers shock and the monotone finite-volume baseline
Example
For and for , for , the entropy shock moves right at speed . For states , define the positive-state Burgers Godunov flux by when and when . Since is increasing on , both cases give . 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: , , , cells , , , and the displayed MUSCL/minmod reconstruction. The positive-state Godunov flux is defined by the two min/max formulas in the Example.
The Axiom of Countable Choice (): asserts that every countable family of nonempty sets has a choice function.
Bounded BV data on the real line: consists of bounded locally integrable classes with finite translation variation; it does not require global integrability.
Monotone finite-volume scheme and invariant-interval CFL: on an invariant interval , the shared two-point update uses a locally Lipschitz consistent flux nondecreasing in its first coordinate and nonincreasing in its second, under .
Strong entropy convergence of monotone finite-volume schemes: under , bounded BV data, a locally Lipschitz consistent coordinatewise monotone flux on the invariant interval, , a positive lower bound on , 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 branch.
Kruzhkov entropy solutions: a Kruzhkov solution satisfies every entropy inequality for , , and has a strong local initial trace.
Rankine–Hugoniot jumps and Lax shocks: a jump from to with speed satisfies the weak jump law exactly when ; in the scalar convex case, a strict Lax shock has .
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
The jump data are bounded and locally integrable. For any translation length , equals on an interval of length and vanishes elsewhere, so by [F1]. The data are not in global , so [F3] uses only its branch and imposes no DC assumption.
For , monotonicity of gives ; for , it gives . Thus the flux defined in the Example is on . Use the same polynomial on all of for the theorem's globally defined input. Its restriction is consistent, nondecreasing in , independent of , and satisfies on ; hence are valid in [F2]. For the particular jump , [F5] gives speed , so the interface ray lies on the left state and the flux is the physical Riemann flux.
Here , , and with . 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.
Define for and for . The jump speed is , so [F5] gives the weak jump law. For , the Kruzhkov entropy-flux jump is ; for and direct substitution gives zero. The initial trace is strong locally because the shock moves a distance . Hence [F4] identifies this profile as a bounded Kruzhkov solution; uniqueness in [F3] makes it the convergence target from step 2.1.
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 and , so the cell to the right of the interface becomes , while the cell to the left remains . The exact shock at time has advanced by , so its average on the first right cell is also .
Sources
- Andrew K. Henrick, Tariq D. Aslam and Joseph M. Powers, Mapped weighted essentially non-oscillatory schemes: Achieving optimal order near critical points
- Guang-Shan Jiang and Chi-Wang Shu, Efficient Implementation of Weighted ENO Schemes
- Jérôme Droniou, A Beginner’s Course in Finite Volume Approximation of Scalar Conservation Laws, complete lecture notes