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Compressible viscous flow and entropy-stable viscous fluxes
1 · Prerequisites
2 · Summary
The page defines the positive ideal-gas state domain, derives the strictly convex mathematical entropy and its entropy variables, and factors the Newtonian–Fourier viscous operator into symmetric positive-semidefinite blocks. For smooth positive states it proves the continuous entropy identity. Its selected multidimensional discretization is a periodic diagonal-norm SBP semidiscretization with an explicit symmetric entropy-conservative Euler flux; weighted mass, momentum, and energy are conserved while total mathematical entropy dissipates by the nodal viscous quadratic form. A one-dimensional orthogonal finite-volume viscous branch has an exact face entropy budget and a local second-order face-flux consistency estimate. A guarded forward-Euler loop records tube exits; on a fixed grid, a compact positive-state margin gives conditional first-order time error. The constant may depend on the grid.
3 · Logical flowchart
4 · Definitions, theorems and proofs
The ideal-gas admissible state set is open and convex
Statement
The set in Ideal-gas compressible Navier–Stokes–Fourier model is open and convex in . In particular, the arithmetic mean of any two admissible conservative states is admissible.
Facts & Assumptions
Given: An integer and the admissible set defined by and .
Proof
On the convex half-space , set . For any direction , differentiating twice along gives . Thus is convex on that half-space.
Let and lie in , and let . Then and convexity from step 1.1 gives . The strict endpoint inequalities imply , so .
The function is continuous where , so the strict inequalities and define an open set there. Step 2.1 proves convexity, and choosing proves the arithmetic-mean assertion.
Strict convexity and entropy variables for the ideal-gas Euler flux
Statement
For the ideal-gas model in Ideal-gas compressible Navier–Stokes–Fourier model, the mathematical entropy has positive-definite Hessian on . Its entropy variables are
The Euler entropy flux is compatible with , meaning , and its entropy potential is . Consequently is a local diffeomorphism at every and is strictly convex on .
Facts & Assumptions
Given: , , , , , and an admissible state with the thermodynamic functions defined in the model item.
Ideal-gas compressible Navier–Stokes–Fourier model: , , , , , , , and .
The ideal-gas admissible state set is open and convex: is open and convex, with and .
Proof
Set and . Rewriting the entropy gives . For an increment , its first internal-energy variation is , and its second directional variation is . The chain rule therefore yields . The first three terms have matrix , whose leading diagonal entry is positive and determinant is . The last term is nonnegative. If the full quadratic form is zero, then and ; the formula for then gives . Thus the Hessian is positive definite.
Differentiating the same expression for gives and . Positive definiteness from step 1.1 makes invertible, so the inverse function theorem gives a smooth local inverse near each state. Since is convex by [F2], integrating the positive Hessian along the segment between any two distinct states also gives strict convexity of on .
Fix and an arbitrary variation , and write . Differentiating the flux in [F1] gives . With and from step 2.1, the terms in cancel, and the coefficient of is , using , , and . Thus for every variation, proving directly.
Direct substitution of , , and gives . Using and , the coefficient simplifies to . This proves and completes the stated entropy-pair and potential identities.
Explicit entropy-conservative two-point Euler flux
Definition
For two admissible states , let , , and , where and are given by Strict convexity and entropy variables for the ideal-gas Euler flux. Define the symmetric two-point flux
The norm is Euclidean in the conservative-state coordinates. The convexity result The ideal-gas admissible state set is open and convex ensures .
Positive-semidefinite entropy-variable factorization of viscous flux
Statement
For , let and be arrays of entropy-variable gradients, with . Write for the momentum-variable component and for the energy-variable component. Define
and . Define the linear map
The bilinear form
has the factorization , so is a symmetric positive-semidefinite block matrix. For , its blocks give exactly the model viscous flux , and
Facts & Assumptions
Given: , the constants , , , and arbitrary arrays .
Ideal-gas compressible Navier–Stokes–Fourier model: , , , and .
Proof
For any entropy-variable gradient array , set and . The formula for in [F2] gives, when , and , since and .
For a general array , define . The displayed bilinear form expands to . Thus the linear functional in is pairing against the flux with zero mass row, momentum entries , and energy entry . By step 1.1 this is precisely the physical viscous flux when .
The linear map in the Statement gives term by term. Its adjoint therefore defines the block matrix , which is symmetric and positive semidefinite because . In particular, for the quadratic form equals , using , , , and .
Take . Then and by step 1.1. Since is symmetric, . Dividing by and adding gives exactly , proving the production identity and its nonnegativity.
Properties of the explicit entropy-conservative flux
Statement
The flux in Explicit entropy-conservative two-point Euler flux is well-defined for every pair in , symmetric in its two states, and consistent in the sense that . It satisfies
The flux is locally Lipschitz on .
Facts & Assumptions
Given: and two states .
Explicit entropy-conservative two-point Euler flux: with , , and , use when and otherwise use .
Strict convexity and entropy variables for the ideal-gas Euler flux: is positive definite on , , and .
The ideal-gas admissible state set is open and convex: is open and convex.
Proof
If , then and [F1] gives . If , convexity [F3] keeps the segment in , and by [F2]. Hence , so the denominator in [F1] is nonzero; also . This proves well-definedness and consistency.
Exchanging and leaves fixed and changes the signs of both and . Thus the scalar correction coefficient in [F1] changes sign while its vector factor changes sign, leaving the flux unchanged. The diagonal branch is also symmetric.
Fix a state and write , for small . The entropy compatibility in [F2] implies . Symmetric Taylor expansions therefore give and , so . Positive definiteness of implies in a sufficiently small neighborhood of .
When , dotting [F1] with cancels its denominator and gives . When , step 1.1 gives , so both sides are zero.
On that neighborhood, the correction is and tends to zero on the diagonal. Differentiating the symmetric Taylor remainders gives , , , and . The quotient rule then shows that the correction derivatives are bounded and tend to zero as ; its diagonal extension is . Away from the diagonal the denominator is nonzero by step 1.1 and the formula is smooth. Thus the flux is locally Lipschitz everywhere on .
Smooth ideal-gas Navier–Stokes–Fourier entropy identity
Statement
Let be a smooth solution with values in of the unforced balance law in Ideal-gas compressible Navier–Stokes–Fourier model. Then
This is a local smooth identity; it makes no boundary discretization claim.
Facts & Assumptions
Given: A smooth positive state field satisfying the conservative equation, with , , , , and as defined by the preceding items.
Strict convexity and entropy variables for the ideal-gas Euler flux: for smooth fields.
Positive-semidefinite entropy-variable factorization of viscous flux: and the corresponding quadratic sum is .
Proof
By [F2] and the balance law [F1], . The second equality is the product rule applied componentwise to each finite-dimensional flux vector.
From the explicit entropy variables in [F2] and viscous flux in [F1], the momentum and stress-work terms cancel pointwise: . Substitution into step 1.1 and the factorization identity [F3] gives the displayed local balance and its nonpositive right-hand side.
Periodic diagonal-norm SBP compressible viscous scheme
Definition
Let be a nonempty finite periodic node set. Let be diagonal with strictly positive entries, and for each direction let satisfy the periodic diagonal-norm SBP identities
For a nodal array , means componentwise matrix multiplication, . Given admissible nodal states , write . Define the semidiscrete system
where is the two-point flux in Explicit entropy-conservative two-point Euler flux and is the viscous block matrix in Positive-semidefinite entropy-variable factorization of viscous flux. This definition specifies a periodic semidiscrete operator on ; it does not include a time discretization or a positivity limiter.
One-dimensional orthogonal finite-volume viscous scheme
Definition
Let , let be an integer, put , and partition into the open cells , , with centers . The interior face separates and for ; the two endpoints are boundary faces. This is the one-dimensional uniform specialization of Finite-volume cell-face meshes and geometric regularity: cells are convex intervals, faces are their endpoints, and the common face orientation is from left to right.
For cell states , set and define the admissible face state . Define the face block , where is the state-space block of Positive-semidefinite entropy-variable factorization of viscous flux for spatial direction one. The shared rightward viscous flux is
Set the boundary fluxes . The finite-volume viscous operator is
This is only the viscous diffusion part of the balance law; no Euler convection term or time discretization is included. The boundary convention is zero total viscous flux.
The schematic shows cells and two representative shared-flux arrows. The arrows show the rightward orientation used in the balances; the endpoint labels are the imposed zero boundary fluxes, and the omitted interior face uses the same convention. The drawing illustrates signed incidences only and is not used as a proof.
Conservation and entropy dissipation of the periodic SBP scheme
Statement
Let , , be any differentiable solution of Periodic diagonal-norm SBP compressible viscous scheme. Then every component of the weighted conservative state is constant, and
The exact entropy production is the finite nodal sum displayed above.
Facts & Assumptions
Given: The finite periodic node set, diagonal matrix , derivative matrices , and semidiscrete equation of Periodic diagonal-norm SBP compressible viscous scheme, with all nodal states remaining in .
Periodic diagonal-norm SBP compressible viscous scheme: is positive diagonal, , , and the semidiscrete residual is the sum of the symmetric two-point Euler flux differencing term and the divergence of .
Positive-semidefinite entropy-variable factorization of viscous flux: the full spatial-state block matrix is symmetric positive semidefinite.
Proof
For each direction set . Then [F1] gives and . Skew-symmetry also gives .
Multiply the semidiscrete equation by and sum in . For the Euler term, each component of is zero: swapping and changes the sign of by step 1.1 and leaves the flux unchanged by [F2]. The viscous term sums to for each nodal flux array . Thus the weighted sum of each conservative component has derivative zero.
By the chain rule, the Euler contribution to the entropy derivative is . Skew-symmetry and flux symmetry [F2], after swapping the two indices in one copy of the sum, rewrite this as . The entropy-potential identity [F2] changes the summand to . Both its row sum and column sum vanish by step 1.1, so the total Euler contribution is zero.
For each viscous direction, by . Applying this to shows that the remaining entropy derivative is exactly the negative sum in the Statement. Each nodal quadratic form is nonnegative by [F3], and each weight is positive; hence the derivative is nonpositive.
Second-order face-flux consistency for the one-dimensional viscous operator
Statement
Let have image in a compact subset of . On the uniform mesh in One-dimensional orthogonal finite-volume viscous scheme, sample . For every interior face , the defined viscous face flux satisfies
for all sufficiently small , where depends on , the fixed physical parameters, and the compact positive-state set, but not on or . This is a face-flux consistency estimate only; it asserts no mesh-uniform solution error bound.
Facts & Assumptions
Given: , a field whose image lies in a compact subset of , and the uniform cell centers and face flux formula of One-dimensional orthogonal finite-volume viscous scheme.
Positive-semidefinite entropy-variable factorization of viscous flux: is the smooth state block representing the one-direction viscous flux on .
Proof
Fix an interior face and put . Taylor's theorem at gives , uniformly in the face index because is bounded. Averaging the two expansions and using [F1] yields .
In [F3], , , and . These functions and are smooth on , where , so is on with bounded third derivative. Its centered Taylor expansion gives uniformly in .
The block is smooth on by its explicit finite-dimensional factorization [F2], hence has bounded first derivatives on a compact neighborhood of the image of . Step 1.1 gives . Multiplying this by the bounded difference quotient in step 1.2 and using [F1] proves the stated face-flux estimate.
Entropy dissipation of the orthogonal finite-volume viscous scheme
Statement
Every differentiable solution of One-dimensional orthogonal finite-volume viscous scheme with satisfies
The endpoint viscous fluxes vanish by definition, and the face quadratic forms use the state block .
Facts & Assumptions
Given: , an integer , , differentiable cell states , and the finite-volume viscous ODE in One-dimensional orthogonal finite-volume viscous scheme.
Positive-semidefinite entropy-variable factorization of viscous flux: each one-direction state block is symmetric positive semidefinite.
Proof
For the total conservative state, substitute [F1] and telescope the finite sum: .
Since by [F3], the chain rule gives . Substituting [F1] cancels the factors and gives .
At each interior face , the two adjacent cells contribute . The boundary terms vanish by [F1]. Substituting the face flux gives the exact negative sum in the Statement.
Each midpoint state is admissible by the convexity built into [F1], and its matrix block is positive semidefinite by [F2]. Thus every face term is nonnegative because . This proves the entropy inequality, including the case when the face sum is empty.
Finite-budget positive-tube forward Euler loop
Definition
Fix the periodic SBP data and semidiscrete vector field of Periodic diagonal-norm SBP compressible viscous scheme. Let be a specified compact set, let the initial nodal vector satisfy for every node, let , and let be a finite step budget. The algorithm returns a nodal vector, a status, and an accepted-step count.
-
For , compute the candidate
-
If for every node , accept it by setting and continue.
-
If any candidate node lies outside , return immediately. Here is the last accepted vector and is the number of accepted steps; the rejected candidate is the one-based stage .
-
If all candidates are accepted, return . For , return .
The membership test is applied nodewise to the compact positive-state tube. A returned left_positive_tube status records a failed candidate and makes no claim that the prescribed step budget was completed.
Termination of the finite-budget guarded forward-Euler loop
Statement
For every valid input to Finite-budget positive-tube forward Euler loop with finite budget , the loop returns after at most candidate evaluations. Its accepted-step count is between and , and every returned nodal state lies in the specified compact tube .
Facts & Assumptions
Given: The algorithm inputs , , a finite integer budget , and a compact tube as specified in the algorithm definition.
Finite-budget positive-tube forward Euler loop: each candidate is either rejected with immediate return of the last accepted vector, or accepted after a nodewise test; after acceptances, the algorithm returns completed.
Proof
If , branch 4 of [F1] returns the input immediately, with zero evaluations and zero accepted stages.
Suppose . At each visited stage , branch 3 returns immediately if the candidate fails, so no rejected stage repeats; the algorithm reports accepted steps because follows exactly earlier acceptances. Otherwise branch 2 accepts one candidate, so the next stored iterate follows one more acceptance. If all candidates are accepted, branch 4 returns completed with count . Thus the loop has at most evaluations and the accepted count lies in .
The initial nodal vector is in by the algorithm input. Each later vector becomes the stored state only after every node passes membership in ; branch 3 returns the preceding stored vector. Therefore every returned nodal state lies in .
Conditional fixed-grid forward-Euler error bound
Statement
Fix the finite SBP grid of Periodic diagonal-norm SBP compressible viscous scheme and its semidiscrete vector field . Let , let be compact, and suppose the exact semidiscrete solution starts from and satisfies for every node and every . For any finite step count with , the guarded algorithm Finite-budget positive-tube forward Euler loop completes all steps for sufficiently small , and
Here the norm is Euclidean on the finite nodal state vector. The constant may depend on the fixed grid, coefficient parameters, tube, and exact trajectory; no mesh-uniform estimate is asserted.
Facts & Assumptions
Given: A fixed finite periodic SBP grid; from the semidiscrete equation; a compact tube ; a continuously differentiable exact trajectory that remains in its interior; exact initial data ; , , and .
Periodic diagonal-norm SBP compressible viscous scheme: is a finite sum of the two-point fluxes , entropy-variable gradients, and state-dependent blocks on the fixed node set.
Properties of the explicit entropy-conservative flux: each two-point flux is locally Lipschitz on .
Positive-semidefinite entropy-variable factorization of viscous flux: the block entries of are formed from smooth functions and and the fixed coefficients .
Finite-budget positive-tube forward Euler loop: the algorithm uses and accepts a candidate exactly when every node lies in .
Termination of the finite-budget guarded forward-Euler loop: for a finite budget , the algorithm returns after at most candidate evaluations.
Proof
The formula [F6] is smooth on : , , , and have positive denominators and logarithm arguments there. Thus is locally Lipschitz. Together with [F2] and [F3], this makes every term in the finite sum [F1] locally Lipschitz on the open finite-dimensional domain , so is locally Lipschitz and continuous there. Compactness of gives a bound . Cover this compact set by finitely many smaller balls whose doubled balls lie in local Lipschitz neighborhoods, and let be the smallest smaller-ball radius. Any pair at distance less than lies together in one of those larger balls, so its Lipschitz quotient is bounded by the largest local constant. For pairs at distance at least , boundedness gives quotient at most . Thus a finite Lipschitz constant holds on .
For an exact time and , the integral equation and the bound in step 1.1 give the one-step defect , with . Indeed, , while .
The unguarded Euler recurrence, as long as each preceding candidate has been accepted, therefore satisfies , where and . Summing the geometric bound and using yields with, for example, when and when .
The continuous exact trajectory is compactly contained in over the compact time interval, so there is a positive minimum distance from every nodal exact state to . Choose so small that . Inductively, after an accepted stage the recurrence in step 3.1 bounds the next candidate's distance from the exact next state by less than , so every candidate node remains in and [F4] accepts it. This proves all steps are accepted; [F5] confirms finite return within the stated budget. The estimate in step 3.1 then holds for every returned stage.
5 · Examples, counterexamples and false statements
None yet.
Sources
- F. Ismail and P. L. Roe, Affordable, entropy-consistent Euler flux functions II: Entropy production at shocks, Journal of Computational Physics 228 (2009), 5410–5436, complete paper
- Pablo Fernandez, Ngoc-Cuong Nguyen, and Jaime Peraire, Entropy-stable hybridized discontinuous Galerkin methods for the compressible Euler and Navier-Stokes equations, complete author preprint
- Eitan Tadmor, Entropy Stable Schemes, Chapter 18, Handbook of Numerical Methods for Hyperbolic Problems: Basic and Fundamental Issues, 2016, complete chapter
- R. Eymard, T. Gallouët, and R. Herbin, Finite Volume Methods, January 2019 updated author text, complete manuscript
- Jérôme Droniou, Finite volume schemes for diffusion equations: introduction to and review of modern methods, 2014, complete preprint