Seventh South African Conference on Computational and Applied Mechanics SACAM10 Pretoria, 10−13 January 2010 c ⃝SACAM ENERGY STABILITY OF THE MUSCL SCHEME Qaisar Abbas∗,1 , Edwin van der Weide† and Jan Nordström∗,‡,2 ∗ Department of Information Technology, Scientific Computing Uppsala University, SE-751 05 Uppsala, Sweden, 1 [email protected] † Faculty of Engineering Technology University of Twente, 7500 AE Enschede, The Netherlands, [email protected] ‡ School of Mechanical, Industrial and Aeronautical Engineering University of the Witvatersrand, PO WITS 2050, Johannesburg, South Africa, and Department of Aeronautics and Systems Integration FOI, The Swedish Defence Research Agency, SE-164 90 Stockholm, Sweden, 2 [email protected] Keywords: MUSCL Scheme, Energy Estimates and Numerical Stability, Summation-by-parts Form, Artificial Dissipation Abstract We analyze the energy stability of the standard MUSCL scheme. The analysis is possible by reformulating the MUSCL scheme in the framework of summation-by-parts (SBP) operators including an artificial dissipation. The effect of different slope limiters is studied. It is found that all the slope limiters do not lead to the correct sign of the entries in the dissipation matrix. The implication of that is discussed for both linear and nonlinear scalar problems. 1 Introduction For problems involving shocks which arise in computational fluid mechanics and related areas, the MUSCL scheme [1] is a very effective approach to resolve discontinuities. This scheme ensures the monotonicity of the solution for the whole computational time and it is arguably computationally less expensive compared to relevant counterparts like the WENO schemes [2] for approximately the same accuracy. In this paper, we have reformulated the MUSCL scheme in summation-by-parts (SBP) form including an artificial dissipation operator. Related work can be found in [3, 4], where the WENO scheme has been formulated in a similar way. The SBP operators are well-established theoretically [9] and their usefulness is proven for practical applications, see [10, 11, 12]. In this work we will investigate the MUSCL scheme to see if the scheme is energy stable, i.e. stable in the L2 -norm, see [3, 4]. We consider both scalar linear and nonlinear hyperbolic problems in one dimension. Our analysis is based on theoretical as well as numerical observations. 2 The MUSCL Scheme in SBP Form Consider the unsteady one-dimensional conservation law 0 ≤ x ≤ 1, ut + f (u)x = 0, t ≥ 0. (1) Define a uniform grid xj = j∆x, j = 0, . . . , N , with ∆x = 1/N . On the grid, define a flux F (U ), where U = [U0 (t), U1 (t) . . . , UN (t)]T is the discrete approximation of the solution u in Eq. (1). The second order upwind discretization of Eq. (1) using the MUSCL approach [1] results in ) 1 ( Ut + RESi = 0, RESi = F 1 − Fi− 1 . (2) 2 ∆x i+ 2 In Eq. (2), Fi+ 1 is the flux function at the interface i + 12 . More details on the computation of 2 numerical flux function can be found in [5]. Similarly a second order discretization of the flux function in Eq. (1), obeying the SBP property [9] and with the introduction of artificial dissipation on SBP form [6] leads to Ut + D2 F = −P −1 D̃1T BM D̃1 U, (3) where D2 is the central finite difference operator on SBP form given by D2 = P −1 Q, Q + QT = B, −2 2 −1 0 1 1 .. .. .. D2 = . . . 2∆x −1 0 1 −2 2 , P −1 P = PT, 2 1 1 .. = . ∆x 1 (4) , 2 and B = diag(−1, 0, . . . , 0, 1). The term −P −1 D̃1T BM D̃1 U in Eq. (3) is an artificial dissipation operator. It will be shown below that the matrix BM can be constructed such that Eq. (3) corresponds to the standard second order MUSCL formulation [1] which means that the fore 1 is a two point difference operator and mulations given by Eq. (2) and Eq. (3) are equivalent. D the matrix BM is a diagonal matrix, see Eq. (5). b0 0 −1 1 b1 −1 1 . . . e1 = . . . (5) , B = D . . . . M bN −1 −1 1 0 0 −1 1 2.1 Explicit Form of BM At an interior point i, we have } { e 1 U = − 1 (bi−1 ∆Ui−1 − bi ∆Ui ), e 1T BM D −P −1 D ∆x i (6) where ∆Ui = Ui+1 − Ui . In combination with the central discretization of the convective term, this leads to the following formulation of the residual for an internal node ∆xRESi = 1 (Fi+1 − Fi−1 ) + bi−1 ∆Ui−1 − bi ∆Ui . 2 (7) For the boundary nodes x0 and xN , the residuals are ∆xRES0 = ∆F0 − Pe0−1 b0 ∆U0 , ∆xRESN = ∆FN −1 + PeN−1 bN −1 ∆UN −1 , (8) where Pe0−1 = PeN−1 = 2. Comparing Eqs. (2) and (7), it is clear that both schemes are identical if 1 bi ∆Ui = (Fi+1 + Fi ) − Fi+ 1 . (9) 2 2 It can be shown that the bi in Eq. (9) becomes { ) } ( 1 ϕi ψi+1 ψi+1 ϕi bi = − + ARi+ 1 ,i+1 − ALi+ 1 ,i , (10) Ai+ 12 1 − 2 2 2 2 2 2 2 where ϕi and ψi+1 are the slope limiters involved in the fluxes. They are related in the following way. ( ) ( ) ∆Ui−1 1 ∆Ui−1 ∆Ui−1 ϕi = ϕ (ri ) = ϕ = ϕ = ψi . (11) ∆Ui ∆Ui ri ∆Ui ∂F Also A = ∂U is a Jacobian matrix evaluated at the Roe average states. The property of a Roe average state is that f2 − f1 = ARoe (u2 − u1 ). 2.2 Energy Stability In this section we define the two versions of the energy stability, that we will work with in the analysis below. Definition. Consider Eq. (3) and Eq. (12). The scheme defined by Eq. (3) is pointwise energy stable if bi ≥ 0 for all i = 0, 1 . . . , N . The scheme defined by Eq. (3) is energy stable in the mean if (DU )T BM (DU ) ≥ 0, where DU = [(DU )0 , (DU )1 , . . . , (DU )N ]T . Remark. Pointwise energy stable schemes lead to energy stable schemes in the mean. The reverse is not true. 2.3 Energy Estimates To investigate whether the scheme defined in Eq. (3) is energy stable or not, we start by considering the linear constant coefficient case with F = aU and use the energy method. Multiplying Eq. (3) with U T P , adding its transpose and using Eq. (4) leads to d e 1 U )T BM (D e 1 U ). ||U ||2P + aU T BU = −2(D dt (12) where ||U ||2P = U T P U . For a bounded solution and energy stability we must have dtd ||U ||2P ≤ 0. The boundary terms U T BU = U02 − UN2 can be bounded using the SAT boundary treatment [8] and are ignored from now on. The right-hand side of Eq. (12) is negative if the matrix BM is positive semi-definite. The matrix BM for a linear problem becomes bi = } 1{ |A| − ϕi A+ + ψi+1 A− , 2 (13) where A+ contains the positive eigenvalues of A and A− the negative ones, A+ = 1 (A + |A|) , 2 1 (A − |A|) . 2 A− = (14) For a scalar problem with F = aU , Eq. (13) reduces to 1 bi = a {1 − ϕi } , 2 a > 0, and bi = 1 |a| {1 − ψi+1 } , 2 a < 0. (15) From the theory of the slope limiters [7] we have that 0 ≤ ϕi , ψi+1 ≤ 2. It is obvious that any limiter which takes values greater than 1, will lead to bi ≤ 0 in the computational domain and hence no pointwise energy stability. In [3, 4], the authors modified the WENO scheme by correcting this anomaly of the scheme. We will discuss below whether that is necessary and meaningful. 3 Numerical Results Consider Eqs. (10), (11) and (12). It is obvious that the sign of bi depends on the slope limiters involved in the MUSCL scheme. If the solution is smooth, we have ϕi = ψi+1 = 1, and for all A, bi will be zero. For problems with discontinuities , we could have 0 ≤ ϕi , ψi+1 ≤ 2, which decides the sign of bi in non-smooth regions. We consider a linear problem (f = u) first with a step discontinuity as initial data and analyze four different limiters. All the results are shown for N = 80 and t = 0.3. In Figures 1–4 we have shown the minimum of bi and −(D1 U )T BM (D1 U ) at each time step for minmod, VanLeer, superbee and MC limiters. The minmod limiter have bi ≥ 0 for all time and hence is pointwise stable. All other limiters lead to bi < 0 at few points near the discontinuity. It means that these limiters do not lead to pointwise stability. It is also found that −(D1 U )T BM (D1 U ) ≤ 0 for all limiters for the whole computational time which gives energy stability in the mean. 0 0.5 0 −0.5 −1 0 50 100 150 Number of time steps (a) min(bi ) per time step 200 2 Initial soluton Numerical solution Exact solution −0.1 solution vector dissipation / time step min(bi) / time step 1 −0.2 −0.3 −0.4 1.5 1 −0.5 −0.6 0 50 100 150 Number of time steps (b) −(D1 U )T BM (D1 U ) 200 0.5 0 0.2 0.4 x 0.6 0.8 1 (c) solution, N = 80, t = 0.3 Figure 1: Results from the MUSCL in SBP form using the minmod limiter, f = u. 2 Next we consider the Burger equation with f = u2 in Eq. (1) and repeat the same analysis with the minmod, VanLeer and MC limiters. It is found that all the tested limiters have some 0 0.5 0 −0.5 −1 0 50 100 150 Number of time steps −0.2 −0.3 −0.4 50 100 150 Number of time steps 1.5 1 0.5 0 200 (b) −(D1 U )T BM (D1 U ) (a) min(bi ) per time step Initial soluton Numerical solution Exact solution −0.5 −0.6 0 200 2 −0.1 solution vector dissipation / time step min(bi) / time step 1 0.2 0.4 x 0.6 0.8 1 (c) solution, N = 80, t = 0.3 Figure 2: Results from the MUSCL in SBP form using the Van Leer limiter, f = u. 0 0.5 0 −0.5 −1 0 50 100 150 Number of time steps −0.2 −0.3 −0.4 1.5 1 −0.5 50 100 150 Number of time steps 0.5 0 200 (b) −(D1 U )T BM (D1 U ) (a) min(bi ) per time step Initial soluton Numerical solution Exact solution −0.1 −0.6 0 200 2 solution vector dissipation / time step min(bi) / time step 1 0.2 0.4 x 0.6 0.8 1 (c) solution, N = 80, t = 0.3 Figure 3: Results from the MUSCL in SBP form using the Superbee limiter, f = u. 0 0.5 0 −0.5 −1 0 50 100 150 Number of time steps −0.2 −0.3 −0.4 50 100 150 Number of time steps 1.5 1 0.5 0 200 (b) −(D1 U )T BM (D1 U ) (a) min(bi ) per time step Initial soluton Numerical solution Exact solution −0.5 −0.6 0 200 2 −0.1 solution vector dissipation / time step min(bi) / time step 1 0.2 0.4 x 0.6 0.8 1 (c) solution, N = 80, t = 0.3 Figure 4: Results from the MUSCL in SBP form using the MC limiter, f = u. 0 0.5 0 −0.5 −1 0 50 100 150 Number of time steps (a) min(bi ) per time step 200 2 Initial soluton Numerical solution Exact solution −0.1 solution vector dissipation / time step min(bi) / time step 1 −0.2 −0.3 −0.4 1.5 1 −0.5 −0.6 0 50 100 150 Number of time steps (b) −(D1 U )T BM (D1 U ) 200 0.5 0 0.2 0.4 x 0.6 0.8 (c) solution, N = 80, t = 0.3 Figure 5: Results from the MUSCL in SBP form using the minmod limiter, f = u2 2 . 1 0 0.5 0 −0.5 −1 0 50 100 150 Number of time steps −0.2 −0.3 −0.4 50 100 150 Number of time steps 1.5 1 0.5 0 200 (b) −(D1 U )T BM (D1 U ) (a) min(bi ) per time step Initial soluton Numerical solution Exact solution −0.5 −0.6 0 200 2 −0.1 solution vector dissipation / time step min(bi) / time step 1 0.2 0.4 0 −0.5 −1 0 50 100 150 Number of time steps 2 −0.2 −0.3 −0.4 50 100 150 Number of time steps Initial soluton Numerical solution Exact solution 1.5 1 0.5 0 200 (b) −(D1 U )T BM (D1 U ) (a) min(bi ) per time step 0.2 0.4 0 −0.5 −1 0 50 100 150 Number of time steps (a) min(bi ) per time step 200 0.6 0.8 1 u2 2 . 2 Initial soluton Numerical solution Exact solution −0.1 solution vector dissipation / time step min(bi) / time step 0 0.5 x (c) solution, N = 80, t = 0.3 Figure 7: Results from the MUSCL in SBP form using the Superbee limiter, f = 1 1 −0.5 −0.6 0 200 0.8 u2 2 . −0.1 solution vector dissipation / time step min(bi) / time step 0 0.5 0.6 (c) solution, N = 80, t = 0.3 Figure 6: Results from the MUSCL in SBP form using the Van Leer limiter, f = 1 x −0.2 −0.3 −0.4 1.5 1 −0.5 −0.6 0 50 100 150 Number of time steps (b) −(D1 U )T BM (D1 U ) 200 0.5 0 0.2 0.4 x 0.6 0.8 1 (c) solution, N = 80, t = 0.3 Figure 8: Results from the MUSCL in SBP form using the MC limiter, f = u2 2 . bi < 0 but the minmod limiter is almost zero for all time leading to pointwise stability, see Figures 5–8. It can also be seen that all schemes are stable in the mean. It is not clear whether pointwise stability is necessary or if stability in the mean is enough. If we replace bi < 0 in the matrix BM with bi = 0 at each time step, we find that it leads to an additional and excessive amount of dissipation in the discontinuity/shock region, see Table 1 for l2 -error of solutions. By demanding the pointwise stability, clearly the sharpness of the shock decreases. Table 1: Analysis of different limiters for the linear problem (a = 1, t = 0.3) 4 Limiters l2 -error, min(bi ≤ 0) l2 -error, min(bi = 0) minmod Van Leer Superbee MC 0.0711 0.0578 0.0400 0.0504 0.0711 0.0642 0.0634 0.0639 Conclusion We have expressed the MUSCL scheme as a combination of an SBP operator and an artificial dissipation operator. This form allows us to use the energy method for analyzing stability. Our main interest was to look at the behavior of dissipation matrix BM in Eq. (5), which is crucial for the stability of the scheme and also influence the sharpness of the shock. As the matrix depends on the slope limiters of the MUSCL scheme, it was found most of the tested limiters except minmod limiter do not lead to pointwise stability while all limiters are stable in the mean. By making the schemes pointwise stable by replacing bi < 0 in the matrix BM with bi = 0 resulted in an additional and excessive dissipation for all the limiters. It was shown that the error in the calculations increased and the sharpness of the shock decreased. This procedure was used in [3, 4] but seems questionable. References [1] B. van Leer. Towards the Ultimate Conservative Difference Scheme, V. A Second Order Sequel to Godunov’s Method, J. Comput. Phys. 32:101–136, 1979. [2] G. Jiang, and C.W. Shu. Efficient implementation of weighted ENO schemes, J. Comput. Phys.126:202–228, 1996. [3] N.K. Yamaleev, and M.H. Carpenter. Third-order energy stable WENO scheme, J. Comput. Phys. 228:3025–3047, 2009. [4] N.K. Yamaleev, and M.H. Carpenter. A systematic methodology for constructing highorder energy stable WENO schemes, J. Comput. Phys. 228:4248–4272, 2009. [5] Q. Abbas, E. van der Weide, and J. Nordström. Accurate and stable calculations involving shocks using a new hybrid scheme. AIAA Paper No. 2009–3985, 2009. [6] K. Mattsson, M. Svärd, and J. Nordström. Stable and Accurate Artificial Dissipation. J. Sci. Comput. 21(1):57–79, 2004. [7] M. Berger, M.J. Aftosmis, and S.M. Murman. Analysis of slope limiters on irregular grids, 43rd AIAA Aerospace Sciences Maeeting, Jan 10–13, 2005. [8] M. H. Carpenter, D. Gottlieb, and S. Abarbanel, The stability of numerical boundary treatments for compact high-order finite difference schemes, J. Comput. Phys. 108(2):272-295, 1994. [9] M. H. Carpenter, J. Nordström, and D. Gottlieb, A Stable and Conservative Interface Treatment of Arbitrary Spatial Accuracy, J. Comput. Phys. 148(2):341–365, 1999. [10] M. Svärd, M. H. Carpenter, and J. Nordström, A Stable High-Order Finite Difference Scheme for the Compressible Navier-Stokes Equations, far-field boundary conditions, J. Comput. Phys. 225(1):1020–1038, 2007. [11] M. Svärd, and J. Nordström, A Stable High-Order Finite Difference Scheme for the Compressible Navier-Stokes Equations: Wall Boundary Conditions, J. Comput. Phys. 227:4805-4824, 2008. [12] J. Nordström, J. Gong, E. van der Weide, and M. Svärd, A Stable and Conservative High Order Multi-block Method for the Compressible Navier-Stokes Equations, J. Comput. Phys. 228:9020–9035, 2009.

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