Chapter 5. The Finite Volume Method for Convection-Diffusion Problems. The steady convection-diffusion equation is
|
|
- Kathleen Beasley
- 5 years ago
- Views:
Transcription
1 Chapter 5 The Finite Volume Method for Convection-Diffusion Problems Prepared by: Prof. Dr. I. Sezai Eastern Mediterranean University Mechanical Engineering Department Introduction The steady convection-diffusion equation is div( ρφu) = div( Γ gradφ) + S φ Integration over the control volume gives : n ( ρφ u ) da = n ( Γ grad φ ) da + S dv A A CV This equation represents the flux balance in a control volume. The main problem in the discretisation of the convective terms is the calculation of φ at CV faces and its convective flux across these boundaries. Diffusion process affects the distribution of φ in all directions. Convection spreads influence only in the flow direction. This sets a limit on the grid size for stable convection-diffusion calculations with central difference method. φ ME555 : Computational Fluid Dynamics 1
2 Steady one-dimensional convection and diffusion In the absence of sources, the steady convection and diffusion of a property φ in a given one-dimensional flow field u is governed by d d dφ ( ρφ u ) = ( Γ ) (5.3) dx dx dx The flow must also satisfy continuity, so d ( u) 0 dx ρ = Integrating Eqn. (5.3) over the CV φ φ ( ρuaφ) e ( ρuaφ) w = ΓA ΓA x x Integrating continuity Eqn. ( ρua) ( ρua) = 0 e ME555 : Computational Fluid Dynamics 3 w e w (5.5) (5.6) Let F = ρua convective mass flux at cell faces D = ΓA/δx diffusion conductance At cell faces: Fw = ( ρua) w Fe = ( ρua) e Γw Aw Γe Ae Dw = De = δx δx WP Using central difference approach for the diffusion terms, Eqn (5.5) becomes PE Continuity i equation becomes Fφ F φ = D ( φ φ ) D ( φ φ ) e e w w e E P w P W F F = 0 e We assume that velocity field is known F e, F w known. We need to calculate φ at faces e and w. w (5.9) (5.10) ME555 : Computational Fluid Dynamics 4
3 The Central Differencing Scheme Works well for diffusion terms. Let us use this method to compute the convective terms by linear interpolation. For a uniform grid, cell face values are: φe = ( φp + φe)/ φ = ( φ + φ )/ w W P Substituting into eqn (5.9) Fe Fw ( φp + φe) ( φw + φp) = De( φe φp) Dw( φp φw) ME555 : Computational Fluid Dynamics 5 Rearranging, Fw Fe Fw Fe Dw De φp Dw φw De φe + + = + + Fw Fe Fw Fe Dw + + De + ( Fe Fw) φp = Dw + φw + De φe which is of the form where a φ = a φ + a φ P P W W E E (5.14) a a a W E P Fw Fe Dw + De aw + ae + ( Fe Fw) This equation has the same general form as the diffusion eqn. (4.11). ME555 : Computational Fluid Dynamics 6 3
4 Example 5.1 A property φ is transported by convection and diffusion through the one dimensional domain shown below. Using central difference scheme, find the distribution of φ for (L =1, ρ = 1, Γ = 0.1) (i) Case 1: u = 0.1 m/s (use 5 CV s) (ii) Case : u =.5 m/s (use 5 CV s) Compare the results with the analytical solution. φ φo exp( ρux / Γ) 1 = φ φ exp( ρ / Γ) 1 L o ul (iii) Case 3: u =.5 m/s (0 CV s) ME555 : Computational Fluid Dynamics 7 The governing equation is: d d dφ ( ρuφ) = Γ A dx dx dx B 1 3 w 4 e φ = 1 W P E φ = 0 δx/ δx WP = δx δx PE =δx δx/ δx apφp = awφw + aeφe + Su For interior nodes: δ WP = δ PE = δ where ap = aw + ae + ( Fe Fw) SP For node : δxwp = δx/ Fw = ( ρua) w Fe = ( ρua) e For node 6: δxpe = δx/ Γ wa w Γ ea e Dw = De = δx δx WP Node a a S S PE W E P u 0 De Fe / ( Dw + Fw/ ) ( Dw + Fw / ) φa 3, 4,5 Dw + Fw / De Fe / D + F / 0 ( D F /) ( D F /) φ w w e e e e B x x x ME555 : Computational Fluid Dynamics 8 4
5 The resulting system of equations are a a φ Su P E a W a a 3 p3 E 3 φ3 Su3 aw a 4 p a 4 E 4 φ 4 Su 4 = aw a i p a i E φ i i Sui aw a n Sun p a φ E n n n a n 1 Sun 1 W a φ n 1 p n 1 Solve the system of equations using Tri-diagonal matrix algorithm (TDMA) for φ, φ 3, φ 4, φ n-1, where (n = 7) ME555 : Computational Fluid Dynamics 9 The solution for case 1 is: φ1 1 φ φ φ4 = φ φ φ7 Exact solution is:.7183 exp( x) φ( x) = ME555 : Computational Fluid Dynamics 10 Comparison of the numerical result with the analytical solution. 5
6 The solution for case : (u =.5 m/s, 5 CV s) Comparison of the numerical result with the analytical solution. The solution appears to oscillate about the exact solution. ME555 : Computational Fluid Dynamics 11 The solution for case 3: (u =.5 m/s, 0 CV s) Comparison of the numerical result with the analytical solution. Grid refinement has reduced the F/D ratio from 5 to 1.5. Central difference scheme yields accurate results when F/D ratio is low. ME555 : Computational Fluid Dynamics 1 6
7 Properties of Discretisation Schemes The numerical results will only be physically realistic when the discretisation scheme has certain fundamental properties. The most important ones are: Conservativeness Boundednes Transportiveness ME555 : Computational Fluid Dynamics Conservativeness To ensure conservation of φ for the whole solution domain the flux of φ leaving a CV across a certain face must be equal to the flux of φ entering the adjacent CV through the same face. To achieve this the flux through a common face must be represented in a consistent manner (by one and the same expression) in adjacent CV s. ME555 : Computational Fluid Dynamics 14 7
8 Example of consistent specification of diffusive fluxes Γw( φ φ1) Γe( φ3 φ) δ x δ x Flux entering CV Flux leaving CV An overall flux balance may be obtained by summing the net flux through each CV ( φ φ1) ( φ3 φ) ( φ φ1) Γe 1 qa e w δx + Γ Γ δx δx ( φ4 φ3) ( φ3 φ) ( φ4 φ3) + Γe3 Γ w3 qb w4 qb qa δx δx + Γ = δx Γ e1 = Γ w, Γ e = Γ w3 and Γ e3 = Γ w4 Fluxes across CV faces are expressed in consistent manner, fluxes cancel out in pairs when summed over the entire domain. ME555 : Computational Fluid Dynamics 15 Flux Consistency ensures conservation of φ over the entire domain for the central difference formulation of the diffusive flux. Inconsistent flux interpolation formulae give rise to unsuitable schemes that do not satisfy overall conservation. For nodes 1, and 3 quadratic function 1 is used. For nodes, 3 and 4 quadratic function is used. If gradient of 1 gradient of at cell face flux leaving CV will not be equal to flux entering CV 3 overall conservation is not satisfied. ME555 : Computational Fluid Dynamics 16 8
9 ) Boundedness The sufficient condition for a convergent iterative method is a nb 1 at all nodes a P < 1 at one node at least a P = ap Sp (5.) If eqn. (5.) is satisfied, resulting matrix coefficients are diagonally dominant. For diagonal dominance, (a P S p ) should be large and S p < 0. Diagonal dominance is a desirable feature for satisfying the boundedness criterion. This states that in the absence of sources the internal nodal values of φ should be bounded by its boundary values. In a steady conduction problem without sources for which the boundary temperatures are 00 and 500 o C, all interior values of T should be between these temperatures. ME555 : Computational Fluid Dynamics 17 Another essential requirement for boundedness is that all coefficients of the discretised equations should have the same sign. If the discretisation scheme does not satisfy the boundedness criteria the solution may not converge at all. Or if it converges it will contain wiggles. (See case of Example 5.1). In case most of the a E values were negative (Table 5.3). Table 5.3 Node a E Fe ΓeAe ρueae = De = δ x PE ME555 : Computational Fluid Dynamics 18 9
10 3) Transportiveness The transportiveness property of a fluid flow can be illustrated by considering a constant source of φ at a point P F ρu Pe = = D Γ / δ x cell Peclet number Distribution of φ in the vicinity of a source at different Peclet numbers. Lines represent contours of constant φ. For no convection and pure diffusion Pe = 0 For no diffusion and pure convection Pe, φ E = φ P influenced only by P. E is ME555 : Computational Fluid Dynamics 19 Assesment of the Central Differencing Scheme for Convection Diffusion Problems Conservativeness The central differencing scheme uses consistent expressions to evaluate convective and diffusive fluxes at the CV faces. The scheme is conservative. ME555 : Computational Fluid Dynamics 0 10
11 Boundedness (i) The internal coefficients of discretised scalar transport equation (5.14) are aw ae ap Fw Fe Dw + De aw + ae + ( Fe Fw) (F e F w ) = 0 from continuity a P = a W + a E Thus, convergence criteria (5.) is satisfied by the central difference scheme. In the example of section 5.3: For case : Pe = 5 oscillatory For case 1 and 3: Pe < ME555 : Computational Fluid Dynamics 1 (ii) a E = D e F e / Fe For ae > 0 < De Fe or = Pee < to have positive ae. D e If Pe > CD scheme violates boundedness gives physically unrealistic solutions. ME555 : Computational Fluid Dynamics 11
12 Transportiveness The CD scheme does not recognise the direction of the flow or the strength of convection relative to diffusion. Thus, it does not posses the transportiveness property at high Pe. Accuracy The CD scheme is stable and accurate only if Pe = F/D <. The CD scheme satisfies this criteria for low Re numbers or for small grid spacings. Thus, CD scheme is not a suitable discretisation practice for general purpose flow calculations. ME555 : Computational Fluid Dynamics The upwind differencing scheme The scheme takes into account the flow direction, φ at cell face = φ at upstream node formulation is used When the flow is in the positive direction, u w >0, u e >0 (F w >0, F e >0), the upwind scheme sets φ w = φ W and φ e = φ P (5.5) ME555 : Computational Fluid Dynamics 4 1
13 The discretised equation (5.9) becomes Feφ P Fwφw = De( φe φp) Dw( φp φw) (5.6) Which can be rearranged as D + D + F φ = D + F φ + D φ to give ( ) ( ) w e e P w w W e E ( ) ( ) ( ) Dw + Fw + De + Fe Fw φ P = Dw + Fw φw + DeφE (5.7) When the flow is in the negative direction, u w <0, u e <0(F w <0, F e <0), the scheme takes φw = φp and φe = φe (5.8) Now the discretised euqation is Fφ F φ = D ( φ φ ) D ( φ φ ) e E w P e E P w P W (5.9) or ( ) ( ) ( ) Dw + De Fe + Fe Fw φ P = DwφW + De Fe φe (5.30) ME555 : Computational Fluid Dynamics 5 the equations (5.7) and (5.30) can be written in the usual general form a φ = a φ + a φ (5.31) P P W W E E with central coefficient ap = aw + ae + ( Fe Fw) and neighbour coeffcients F w >0, F e >0 D w + F w D e F w <0, F e <0 D w D e - F e A form of notation for neighbour coefficients of the upwind differencing method that covers both flow directions is: a w aw a e ae D w + max(f w,0) D e + max(0, F e ) ME555 : Computational Fluid Dynamics 6 13
14 Example 5. Solve the problem considered in example 5.1 using the upwind differencing scheme for (i) u = 0.1 m/s, (ii) u =.5 m/s ( ) with the coarse five-point grid. ME555 : Computational Fluid Dynamics 7 The governing equation is: d d dφ ( ρuφ) = Γ dx dx dx A B 1 3 w 4 e φ = 1 W P E φ = 0 δx/ δx WP = δx δx PE =δx δx/ δx apφp = awφw + aeφe + Su For interior nodes: δ WP = δ PE = δ where ap = aw + ae + ( Fe Fw) SP For node : δxwp = δx/ Fw = ( ρua) w Fe = ( ρua) e For node 6: δxpe = δx/ Γ wa w Γ ea e Dw = De = δx δx WP PE Node a a S S W E P u x x x 0 De + max(0, Fe) ( Dw + max( Fw,0)) ( Dw + max( Fw,0)) φa 3, 4,5 Dw + max( Fw,0) De + max(0, Fe) D + max( F,0) 0 ( D + max(0, F )) ( D + max(0, F )) φ w w e e e e B ME555 : Computational Fluid Dynamics 8 14
15 u = 0.l m/s: ME555 : Computational Fluid Dynamics 9 u =.5 m/s Upwind scheme produced a much more realistic solution compared with central difference scheme. However, the solution is not very close to the exact value. ME555 : Computational Fluid Dynamics 30 15
16 5.6.1 Assessment of the upwind differencing scheme Conservativeness the upwind differencing scheme utilises consistent expressions to calculate fluxes through cell faces: therefore it can be easily shown that the formulation is conservative Boundedness the coefficients of the discretised equation are always positive and satisfy the requirements for boundedness F e F w = 0 a P = a W + a E Stable iterative solution All coefficients are positive No wiggles in Coefficient matrix is diagonally dominant solution Transportiveness The scheme accounts for the direction of the flow so transportiveness is build into the formulation. ME555 : Computational Fluid Dynamics 31 Accuracy the scheme is based on the backward differencing formula so the accuracy is only first order on the basis of the Taylor series truncation error (see Appendix A): A major drawback of the scheme: it produces erronous results when the flow is not aligned with the grid lines. φ is smeared error has a diffusion-like appearance false diffusion ME555 : Computational Fluid Dynamics 3 16
17 Consider pure convection without diffusion and no source term. the true solution is: all nodes above diagonal should be 100 all nodes below diagonal should be 0 Upwind method is not suitable for accurate flow calcualtions ME555 : Computational Fluid Dynamics The hybrid differencing scheme Central differencing scheme: accurate to second order Not transportive Upwind differencing scheme: accurate to first order is transportive Hybrid difference scheme uses: central difference scheme for Pe < upwind difference scheme in which diffusion has been set to zero for Pe For a west face F ( ρu) w w Pew = = Dw Γ w/ δ x (5.35) WP The hybrid differencing formula for the net flux through the west face is as follows: 1 1 qw = Fw 1+ φw + 1 φp for < Pew < Pew Pew (5.36) q = F φ for Pe w w W w q = F φ for Pe w w P w ME555 : Computational Fluid Dynamics 34 17
18 The general form of the discretised equation is a φ = a φ + a φ (5.37) P P W W E E The central coefficient is given by a = a + a + ( F F ) P W E e w After some re-arrangement it is easy to establish that the neighbour coefficients for the hybrid differencing scheme for steady one - dimensional convection diffusion can be written as follows: a W Fw Fe max Fw, Dw +,0 max Fe, De,0 a E ME555 : Computational Fluid Dynamics 35 Example 5. Solve the problem considered in case of example 5.1 using the hybrid scheme for u=.5 m/s. Compare a 5 node solution with a 5 node solution ME555 : Computational Fluid Dynamics 36 18
19 Comparison with the analytical solution The numerical results are compared with the analytical solution in table 5.9 ME555 : Computational Fluid Dynamics Assessment of the hybrid differencing scheme Is fully conservative Is unconditionally bounded (since the coefficients are always positive) Satisfies the transportiveness property Produces physical realistic solutions Highly stable compared with higher order scheme Is only first order accurate ME555 : Computational Fluid Dynamics 38 19
20 Hybrid differencing scheme for multi-dimensional convection-diffusion The discretised equation that covers all cases is given by a φ = a φ + a φ + a φ + a φ + a φ + a φ P P W W E E S S N N B B T T with central coefficient ap = aw + ae + as + an + ab + at +ΔF ME555 : Computational Fluid Dynamics 39 The coefficient of this equation for the hybrid differencing scheme are as follows: One-dimensional flow two-dimensional flow three-dimensional flow aw max[fw,(dw+fw/),0] max[fw,(dw+fw/),0] max[fw,(dw+fw/),0] ae max[-fe,(de-fe/),0] max[-fe,(de-fe/),0] max[-fe,(de-fe/),0] as - max[fs,(ds+fs/),0] max[fs,(ds+fs/),0] an - max[-fn,(dn-fn/),0] max[-fn,(dn-fn/),0] ab - - max[fb,(db+fb/),0] at - - max[-ft,(dt-ft/),0] ΔF Fe-Fw Fe-Fw+Fn-Fs Fe-Fw+Fn-Fs+Ft-Fb ME555 : Computational Fluid Dynamics 40 0
21 In the above expressions the value of F and D are calculated with the following formulae Face w e s n b t F (ρu) w A w (ρu) e A e (ρu) s A s (ρu) n A n (ρu) b A b (ρu) t A t D Γ w A w /δx WP Γ e A e /δx PE Γ s A s /δy SP Γ n A n /δy PN Γ b A b /δz PN Γ t A t /δz PT ME555 : Computational Fluid Dynamics 41 The Power Law Scheme Is a more accurate approximation to the 1-D exact solution Produces better results than the hybrid scheme for Pe > 10 diffusion is set to zero for 0 < Pe < 10 the flux is evaluated by a polynomial expression ME555 : Computational Fluid Dynamics 4 1
22 For example, the net flux per unit area at the west control volume face is evaluated using qw = Fw φw βw( φp φw ) for 0 < Pe< 10 (5.44a) where β ( ) 5 w = Pew Pew (5.44b) and q = F φ for Pe > 10 w w W w The coefficients of the one-dimensional descretised equation utilising the power-law scheme for steady one-dimensional convection-diffusion are given by Central coefficient: ap = aw + ae + ( Fe Fw ) and a W 5 ( ) + [ ] D max w 0, Pew max Fw, 0 ME555 : Computational Fluid Dynamics 43 a E 5 ( ) + [ ] D max e 0, Pee max Fe, Higher order differencing schemes for convectiondiffusion problems Hybrid and Upwind schemes are Stable Obey the transportiveness requirement But have first order accuracy Are prone to numerical diffusion errors Such errors can be minimized by employing higher order discretisations. CentralDifference scheme is second order accurate but is not stable. Formulations that do not take into account the flow direction are unstable For more accuracy: use higher order schemes, which preserve upwinding for stability ME555 : Computational Fluid Dynamics 44
23 5.9.1 Quadratic upwind differencing scheme: the QUICK scheme The quadratic upstream interpolation for convective kinetic (QUICK) scheme of Leonard(1979) uses a three-point upstream-weighted upstream quadratic interpolation for cell face values. The face value of φ is obtained from a quadratic function through two bracketing nodes (on each side of the face) and a node on the upstream side (Fig. 5.17) Two upstream nodes and one downstream node is used to calculate the face value of φ ME555 : Computational Fluid Dynamics 45 It can be shown that for a uniform grid the value of φ at the cell face between two bracketing nodes i and i-1, and upstream node i- is given by the following formula: φ face = φi 1+ φi φi (5.45) When u w > 0, the bracketing nodes for the west face w are W and P, the upstream node is WW (Figure 5.17), and φw = φw + φp φ (5.46) WW When u e > 0, the bracketing nodes for the east face e are P and E, the upstream node is W,so φe = φp + φe φw (5.47) The diffusion terms may be evaluated using the gradient of the appropriate parabola. It is interesting to note that on a uniform grid this practice gives the same expressions as central differencing for diffusion. ME555 : Computational Fluid Dynamics 46 3
24 If Fw>0 and Fe>0 and if we use equations ( ) for the convective terms and central differencing for the diffusion terms, the discretised form of the one-dimensional convection-diffusion transport equation(5.9) may be written as Feφ e Fwφw= De( φe φp) Dw( φp φw) (5.9) Fe φp + φe φw Fw φw + φp φww De( E P) Dw( P W ) = φ φ φ φ which can be rearranged to give Dw Fw + De + F e φp = Dw + Fw + F e φw + De F e φe FwφWW (5.48) This is now written in the standard form for discretised equations ap φp = aw φw + ae φe + aww φww (5.49) where ME555 : Computational Fluid Dynamics 47 For Fw < 0 and Fe < 0 the flux across the west and east boundaries is given by the expressions φw = φp + φw φe (5.50) 50) φe = φe + φp φee Substitution of these two formulae for the convective terms in the discretised convection-diffusion equation (5.9) together with central differencing for the diffusion terms leads, after re-arrangement as above, to the following coefficients: ME555 : Computational Fluid Dynamics 48 4
25 General expressions, valid for positive and negative flow directions, can be obtained by combining the two sets of coefficients above. The QUICK scheme for one-dimensional convection-diffusion problems can be summarised as follows: apφp = awφw + aeφe + awwφww + aeeφee (5.51) With central coefficient ap = aw + ae + aww + aee + ( Fe Fw) And neighbour coefficients where α w =1 for F w > 0 and α e =1 for Fe > 0 α w =0 for F w < 0 and α e =0 for Fe < 0 ME555 : Computational Fluid Dynamics 49 Example 5.4 Using the QUICK scheme solve the problem considered in example 5.1 for u=0. m/s on a five-point grid. Compare the quick solution with the exact and central differencing solution. A B 1 3 w 4 e φ = 1 W P E φ = 0 δx/ δx WP = δx δx PE =δx δx/ Boundary Points : δx Consider node. φ w = φ A To calculate φ e : φ w is needed. But there is no φ w use linear interpolation to create a mirror node at δx/ to the west of boundary A. ME555 : Computational Fluid Dynamics 50 5
26 Mirror Node Domain boundary Node It can be easily shown that the linearly extrapolated value at the minor node is given by (5.5) φ = φ φ 0 A P The extrapolation to the mirror node has given us the required W node for the formula (5.47) that calculates φ e at the east face of control volume : φe = φp + φe ( φa φp) = φp + φe φa (5.53) ME555 : Computational Fluid Dynamics 51 At the boundary nodes the gradients in diffusive flux terms can be evaluated using central difference scheme similar to calculation of diffusion terms in interior nodes. ME555 : Computational Fluid Dynamics 5 6
27 with The discretised equations for nodes, 3 and 6 are now written to fit into the standard form to give: a φ = a φ + a φ + a φ + S (5.59) The solution is P P WW WW W W E E u a = a + a + a + a + ( F F ) S P W E WW EE e w P φ φ φ 4 = φ φ (5.60) ME555 : Computational Fluid Dynamics 53 ME555 : Computational Fluid Dynamics 54 7
28 5.9. Assessment of the QUICK scheme The scheme: Uses consistent quadratic profiles is conservative Is based on a quadratic function has 3 rd order truncation error Is based on upstream and 1 downstream nodes has transportiveness a P = Σ a nb if flow field satisfies continuity desirable for boundedness ae and aw may not be positive aww and aee are negative If uw > 0 and ue > 0 : Fe 8 Then ae = De 3 / 8 Fe becomes negative for Pee = > De 3 Gives rise to stability problems and unbounded solutions. QUICK scheme is conditionally stable Involves φ WW and φ EE which are not immediate-neighbour nodes ME555 : Computational Fluid Dynamics Stability problems of the QUICK scheme and remedies May have negative main coefficients can be unstable Also other higher order schemes may be oscillatory and unstable under certain conditions In this case use: Method of deferred correction In this method the cell face value φ f is formulated as the sum of the upwind value and other higher order terms which are evaluated at the previous iteration. u o φ = φ +Δφ f f f HO o o o Δ φ f = φ f φ f where: u φ f = φ f value to be computed by 1 st order upwind method 0 φ HO f = φ f value computed by high order scheme from previous old values 0 φ u = φ value computed by 1 st order upwind method from previous old values f f u ME555 : Computational Fluid Dynamics 56 8
29 Let us apply the deferred correction method to QUICK scheme. For uw > 0 QUICK scheme is φ w = φ W + φ P φ WW This can be written as 1 φw = φw + [ 3 φp φw φww] For Fw > 0 8 u φ f Δφ 0 f is added to source term Similarly: 1 φ = φ + [ 3 φ φ φ ] For F > 0 e P E P W e 8 1 φw = φp + [ 3 φw φp φe] For Fw < φe = φe + [ 3 φp φe φee] For Fe < 0 8 Su (5.6) ME555 : Computational Fluid Dynamics 57 The discretisation equation takes the form apφp = awφw + aeφe + Su (5.63) The central coefficient is a P = a W + a E + ( F e F w) (5.64) where a a S w e u D + max( F,0) D + max(0, F ) w w e e max[ Fw,0]( φw φw) max[ Fw,0]( φw φp) + max[ F,0]( φ φ ) max[ F,0]( φ φ ) e e E e e P (5.65) Note that a w and a e are the same as that of the upwind method. The advantage of this approach is that the coefficients are always positive and now satisfy the requirements for conservativeness, boundedness and transportiveness ME555 : Computational Fluid Dynamics 58 9
30 5.9.4 general comments on the QUICK differencing scheme QUICK scheme Has greater accuracy than central, upwind or power schemes Retains the upwind weighted characteristics Resultant false diffusion is small Can give (minor) undershoots and overshoots (see Fig. 5.0) ME555 : Computational Fluid Dynamics 59 To prevent this problem use: 1. Limiters Limit the scheme to have the face value φ f to be between certain values (ULTRA SHARP). Total variation diminishing schemes (TVD) ME555 : Computational Fluid Dynamics 60 30
31 Homework y H Consider a fluid at a uniform temperature T i entering a channel whose surface is maintained at a different temperature T s. A Thermal boundary layer along the tube developes, after which the form of the temperature profile does not change. Assume that the flow profile is constant in the channel where the velocities are given by u y = 1 1 and v = 0 umax H where u max = 1.5u mean. The energy equation is ( ρ ut) ( ρ vt) k T k T + = + x y x cp x y cp y Find the temperature profile in the channel for Re = ρu mean H/μ = 10, Pr = μc p /k = 1. Use L x /H = 5, where L x is the length of the solution domain. Use UPWIND method. (Note: k/c p = μ/pr for fluids.). Also, choosing ρ = 1, find μ from Re relation. Take T in = 0, T walls = 100, u mean = 1 m/s ME555 : Computational Fluid Dynamics 61 Generalisation of Upwind-biased Schemes For convection terms, an estimate of φ value at the faces of a CV is required. Consider east face, assuming u e > 0 1) Standard Upwind Differencing Scheme (UD) φ P φ e = φ P φ e WW W w P e E EE UPWIND v e The face value of φ is taken to be equal to the value of the upstream node; φ e = φ P (5.66) ME555 : Computational Fluid Dynamics 6 31
32 Generalisation of Upwind-biased Schemes ) Linear Upwind Differencing Scheme (LUD) also called the second order upwind differencing scheme (SOU) φ e = φ P +(φ P φ W ) / φ W φ P φ e WW W w SOU (LUD) P φ is assumed to vary linearly between W and e. Then φ e is found by extrapolating the two upstream node values φ W and φ P to face e. ( φ P φ W ) δ x φe = φp + δ x (5.67) 1 = φp + ( φp φw ) The term ½(φ P φ W ) can be thought as a second order correction to the standard upwind scheme. e v e E EE ME555 : Computational Fluid Dynamics 63 Generalisation of Upwind-biased Schemes 3) Central Differencing Scheme (CD) φ e = (φ P + φ W ) / φp φ e φ E WW W w P e E EE CENTRAL u e The value of φ is assumed to vary linearly between the two nodes straddling the face, that is; ( φ P + φ E ) φ e = (5.68) or 1 φe = φp + ( φe φp ) ME555 : Computational Fluid Dynamics 64 (5.69) 3
33 Generalisation of Upwind-biased Schemes 4) QUICK Scheme φ e = 6/8φ P + 3/8φ E 1/8φ W ) φ P φ e φ W φ E w e WW W P E EE QUICK The scheme is based on the assumption that φ varies in terms of a second degree polynomial between two upstream (W and P) and the downstream node E φe = φp + φe φ (5.70) W or φe = φp + (3 φe φp φw ) (5.71) 8 u e ME555 : Computational Fluid Dynamics 65 Generalisation of Upwind-biased Schemes All higher order schemes can be expressed in the form: 1 φe = φp + ψ ( φe φp ) ψ = an appropriate p function (5.7) Convective flux at face e is F e φ e For a higher order scheme convective flux consist of two parts: 1) Upwind flux, Feφ P ) Additional flux, Feψ(φ E φ P)/ Additional flux is connected to the gradient of φ at face e, as indicated by (φ E φ P ) ME555 : Computational Fluid Dynamics 66 33
34 Generalisation of Upwind-biased Schemes ψ = 0 for UD scheme ψ = 1 for CD scheme LUD scheme may be written as 1 φp φ W φe = φp + ( φe φp) (5.73) φe φp φp φ W ψ = for LUD scheme (5.74) φe φp QUICK scheme may be written as 1 φp φ 1 W φe = φp ( φe φp) (5.75) φe φp 4 φp φ W 1 ψ = 3 + for QUICK scheme (5.76) φe φp 4 ME555 : Computational Fluid Dynamics 67 Generalisation of Upwind-biased Schemes let φp φw r = φ φ E P r = ratio of upwind-side gradient to downwind-side gradient ψ is a function of r: ψ = ψ(r) ) A higher order convection scheme can be written as 1 φe = φp + ψ ()( r φe φp ) ψ = 0 for UD scheme ψ = 1 for CD scheme φp φ W ψ = for LUD scheme φe φp φp φ W 1 ψ = 3 + for QUICK scheme φe φp 4 (5.77) (5.78) ME555 : Computational Fluid Dynamics 68 34
35 All of the above expressions assume that the flow direction is positive (from west to east). Similar expressions exist for negative flow direction. In that case, r is still the ratio of upwind-side gradient to downwind-side gradient. ME555 : Computational Fluid Dynamics 69 Total Variation and TVD Schemes UD scheme is the most stable scheme (no wiggles) CD and QUICK have higher order accuracy but give rise to wiggles under certain conditions. Our aim is to find a convection scheme with a higherorder accuracy but without wiggles. The desirable property for a stable, non-oscillatory, higher order scheme is monotonicity preserving. For a scheme to preserve to preserve monotonicity: 1. It must not create local extrema. The value of an existing local minimum must be non-decreasing and that of a local maximum must be non-increasing. Monotonicity preserving schemes do not create new undershoots and overshoots. ME555 : Computational Fluid Dynamics 70 35
36 Total Variation and TVD Schemes Consider the discrete data set shown in the figure. The total variation of this data set is TV ( φ) = φ φ1 + φ3 φ + φ4 φ3 + φ5 φ4 = φ φ + φ φ For monotonicity, this TV must not increase with time. (5.79) ME555 : Computational Fluid Dynamics 71 Total Variation and TVD Schemes In other words TV must diminish with time. Hence, the term total variation diminishing or TVD. Originally TVD was developed for time-dependent flows. For TVD: TV(φ n+1 ) TV(φ n ) where n refer to time step. In the next section we show how TVD is also linked to desirable behaviour of discretisation schemes for steady convection-diffusion problems. ME555 : Computational Fluid Dynamics 7 36
37 Criteria for TVD Schemes Necessary and sufficient conditions for a scheme to be TVD 1) For 0 < r < 1 ψ(r) r ) For r 1 ψ(r) UD scheme is TVD LUD scheme is not TVD for r > CD scheme is not TVD for r < 0.5 QUICK scheme is not TVD for r < 3/7 and r > 5 ME555 : Computational Fluid Dynamics 73 Requirement for Second Order Accuracy For second order accuracy, the flux limiter function ψ should pass through the point (1, 1) in the r ψ diagram. Range of possible second-order schemes is bounded by the CD and LUD schemes: For 0 < r < 1 for TVD to be second order r ψ(r) 1 For r 1 for TVD to be second order 1 ψ(r) r Region for a second-order TVD scheme ME555 : Computational Fluid Dynamics 74 37
38 Symmetry Property for Limiter Functions Symmetry Property for limiter functions: ψ () r ψ (1/ r) r = (5.80) A limiter function that satisfies the symmetry property ensures that backward and forward-facing gradients are treated in the same fashion without the need for special coding. ME555 : Computational Fluid Dynamics 75 Flux Limiter Functions Name Limiter function Source Van Leer r+ r 1+ r Van Leer (1974) Van Albada r+ r 1+ r Van Albada et al. (198) Min-Mod min( r,1) if r > 0 ψ ( r) = 0 if r 0 Roe (1985) SUPERBEE max[0,min( r,1),min( r, )] Roe (1985) Sweby max[0, min( βr,1), min( r, β)] Sweby (1984) QUICK max[0,min( r,(3 + r) / 4, )] Leonard (1988) UMIST max[0,min( r,(1+ 3 r) / 4,(3 + r) / 4, )] Lien and Leschziner (1993) 0 β β = 1 Min-Mod Limiter β = SUPERBEE Limiter of Roe ME555 : Computational Fluid Dynamics 76 38
39 Flux Limiter Functions MIN MOD MIN MOD All Limiter functions in a r ψ diagram All limiter functions are symmetric except QUICK limiter. UMIST limiter function is a symmetric version of the QUICK limiter. ME555 : Computational Fluid Dynamics 77 Implementation of TVD Schemes For the one dimensional convection diffusion equation d d dφ ( ρuφ) = Γ dx dx dx (5.81) The coefficients of the discretized equation are written in the deferred correction approach. In this approach, the a E, a W, a P coefficients i are the same as of UD scheme. The extra terms resulting from the application of a limiter function is added to the source term S dc. The face values are: 1 ( + e P re )( E P ) + φp φ W For u > 0 φ = φ + ψ φ φ re = φe φp 1 (5.8) + + φw φ WW φ ( )( ) w = φw + ψ rw φp φ r = w W φp φw 1 ( )( ) φee φe For u < 0 φe = φe + ψ re φp φe re φe φp 1 φw φp ψ ( φ = + rw )( φw φp ) E φp rw φp φw ME555 : Computational Fluid Dynamics 78 = = (5.83) 39
40 where Implementation of TVD Schemes The discretisation equation takes the form a φ = a φ + a φ + S (5.84) P P W W E E dc The central coefficient is a = a + a + ( F F ) P W E e w a a S w e dc D + max( F,0) D + max(0, F ) w w e e (5.85) max[ Fw,0]( φw φw) max[ Fw,0]( φw φp) + max[ F,0]( φ φ ) max[ F,0]( φ φ ) e e E e e P (5.86) φ e and φ w are as defined in Eqs. (5.8) and (5.83) Note that S dc is the same as defined in Eq. (5.65). Note also that a w and a e are the same as that of the upwind method. The advantage of this approach is that the coefficients are always positive and now satisfy the requirements for conservativeness, boundedness and transportiveness ME555 : Computational Fluid Dynamics 79 Evaluation of TVD Schemes Comparison of TVD schemes for pure convection flowing 45 o to the grid direction. TVD schemes does not give unphysical overshoots or undershoots. However, TVD schemes require about 15% more CPU time. ME555 : Computational Fluid Dynamics 80 40
Solution Methods. Steady convection-diffusion equation. Lecture 05
Solution Methods Steady convection-diffusion equation Lecture 05 1 Navier-Stokes equation Suggested reading: Gauss divergence theorem Integral form The key step of the finite volume method is to integrate
More informationDiscretization of Convection Diffusion type equation
Discretization of Convection Diffusion type equation 10 th Indo German Winter Academy 2011 By, Rajesh Sridhar, Indian Institute of Technology Madras Guides: Prof. Vivek V. Buwa Prof. Suman Chakraborty
More information5. FVM discretization and Solution Procedure
5. FVM discretization and Solution Procedure 1. The fluid domain is divided into a finite number of control volumes (cells of a computational grid). 2. Integral form of the conservation equations are discretized
More informationOne Dimensional Convection: Interpolation Models for CFD
One Dimensional Convection: Interpolation Models for CFD ME 448/548 Notes Gerald Recktenwald Portland State University Department of Mechanical Engineering gerry@pdx.edu ME 448/548: 1D Convection-Diffusion
More informationFinite volume method for CFD
Finite volume method for CFD Indo-German Winter Academy-2007 Ankit Khandelwal B-tech III year, Civil Engineering IIT Roorkee Course #2 (Numerical methods and simulation of engineering Problems) Mentor:
More informationSolution Methods. Steady State Diffusion Equation. Lecture 04
Solution Methods Steady State Diffusion Equation Lecture 04 1 Solution methods Focus on finite volume method. Background of finite volume method. Discretization example. General solution method. Convergence.
More informationComparison of some approximation schemes for convective terms for solving gas flow past a square in a micorchannel
Comparison of some approximation schemes for convective terms for solving gas flow past a square in a micorchannel Kiril S. Shterev and Sofiya Ivanovska Institute of Mechanics, Bulgarian Academy of Sciences,
More informationOn A Comparison of Numerical Solution Methods for General Transport Equation on Cylindrical Coordinates
Appl. Math. Inf. Sci. 11 No. 2 433-439 (2017) 433 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.18576/amis/110211 On A Comparison of Numerical Solution Methods
More information3.4. Monotonicity of Advection Schemes
3.4. Monotonicity of Advection Schemes 3.4.1. Concept of Monotonicity When numerical schemes are used to advect a monotonic function, e.g., a monotonically decreasing function of x, the numerical solutions
More informationNumerical Solutions for Hyperbolic Systems of Conservation Laws: from Godunov Method to Adaptive Mesh Refinement
Numerical Solutions for Hyperbolic Systems of Conservation Laws: from Godunov Method to Adaptive Mesh Refinement Romain Teyssier CEA Saclay Romain Teyssier 1 Outline - Euler equations, MHD, waves, hyperbolic
More informationNumerical Methods for Problems with Moving Fronts Orthogonal Collocation on Finite Elements
Electronic Text Provided with the Book Numerical Methods for Problems with Moving Fronts by Bruce A. Finlayson Ravenna Park Publishing, Inc., 635 22nd Ave. N. E., Seattle, WA 985-699 26-524-3375; ravenna@halcyon.com;www.halcyon.com/ravenna
More informationIntroduction to numerical simulation of fluid flows
Introduction to numerical simulation of fluid flows Mónica de Mier Torrecilla Technical University of Munich Winterschool April 2004, St. Petersburg (Russia) 1 Introduction The central task in natural
More informationVALIDATION OF ACCURACY AND STABILITY OF NUMERICAL SIMULATION FOR 2-D HEAT TRANSFER SYSTEM BY AN ENTROPY PRODUCTION APPROACH
Brohi, A. A., et al.: Validation of Accuracy and Stability of Numerical Simulation for... THERMAL SCIENCE: Year 017, Vol. 1, Suppl. 1, pp. S97-S104 S97 VALIDATION OF ACCURACY AND STABILITY OF NUMERICAL
More information2.2. Methods for Obtaining FD Expressions. There are several methods, and we will look at a few:
.. Methods for Obtaining FD Expressions There are several methods, and we will look at a few: ) Taylor series expansion the most common, but purely mathematical. ) Polynomial fitting or interpolation the
More informationIn Proc. of the V European Conf. on Computational Fluid Dynamics (ECFD), Preprint
V European Conference on Computational Fluid Dynamics ECCOMAS CFD 2010 J. C. F. Pereira and A. Sequeira (Eds) Lisbon, Portugal, 14 17 June 2010 THE HIGH ORDER FINITE ELEMENT METHOD FOR STEADY CONVECTION-DIFFUSION-REACTION
More informationAN UNCERTAINTY ESTIMATION EXAMPLE FOR BACKWARD FACING STEP CFD SIMULATION. Abstract
nd Workshop on CFD Uncertainty Analysis - Lisbon, 19th and 0th October 006 AN UNCERTAINTY ESTIMATION EXAMPLE FOR BACKWARD FACING STEP CFD SIMULATION Alfredo Iranzo 1, Jesús Valle, Ignacio Trejo 3, Jerónimo
More informationAnswers to Exercises Computational Fluid Dynamics
Answers to Exercises Computational Fluid Dynamics Exercise - Artificial diffusion upwind computations.9 k=. exact.8 k=..7 k=..6 k=..5.4.3.2...2.3.4.5.6.7.8.9 x For k =.,. and., and for N = 2, the discrete
More informationEffects of the Jacobian Evaluation on Direct Solutions of the Euler Equations
Middle East Technical University Aerospace Engineering Department Effects of the Jacobian Evaluation on Direct Solutions of the Euler Equations by Ömer Onur Supervisor: Assoc. Prof. Dr. Sinan Eyi Outline
More informationComputation of Incompressible Flows: SIMPLE and related Algorithms
Computation of Incompressible Flows: SIMPLE and related Algorithms Milovan Perić CoMeT Continuum Mechanics Technologies GmbH milovan@continuummechanicstechnologies.de SIMPLE-Algorithm I - - - Consider
More informationFINITE-VOLUME SOLUTION OF DIFFUSION EQUATION AND APPLICATION TO MODEL PROBLEMS
IJRET: International Journal of Research in Engineering and Technology eissn: 39-63 pissn: 3-738 FINITE-VOLUME SOLUTION OF DIFFUSION EQUATION AND APPLICATION TO MODEL PROBLEMS Asish Mitra Reviewer: Heat
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS NUMERICAL FLUID MECHANICS FALL 2011
MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS 02139 2.29 NUMERICAL FLUID MECHANICS FALL 2011 QUIZ 2 The goals of this quiz 2 are to: (i) ask some general
More informationSolving the Euler Equations!
http://www.nd.edu/~gtryggva/cfd-course/! Solving the Euler Equations! Grétar Tryggvason! Spring 0! The Euler equations for D flow:! where! Define! Ideal Gas:! ρ ρu ρu + ρu + p = 0 t x ( / ) ρe ρu E + p
More informationCHAPTER 7 NUMERICAL MODELLING OF A SPIRAL HEAT EXCHANGER USING CFD TECHNIQUE
CHAPTER 7 NUMERICAL MODELLING OF A SPIRAL HEAT EXCHANGER USING CFD TECHNIQUE In this chapter, the governing equations for the proposed numerical model with discretisation methods are presented. Spiral
More informationCOMPUTATIONAL FLUID DYNAMICS
COMPUTATIONAL FLUID DYNAMICS A.E.P. Veldman Lecture Notes in Applied Mathematics JMBC PhD Course January 2014 COMPUTATIONAL FLUID DYNAMICS A.E.P. Veldman Institute for Mathematics and Computer Science
More informationNumerical Heat and Mass Transfer
Master Degree in Mechanical Engineering Numerical Heat and Mass Transfer 15-Convective Heat Transfer Fausto Arpino f.arpino@unicas.it Introduction In conduction problems the convection entered the analysis
More informationA Multi-Dimensional Limiter for Hybrid Grid
APCOM & ISCM 11-14 th December, 2013, Singapore A Multi-Dimensional Limiter for Hybrid Grid * H. W. Zheng ¹ 1 State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy
More informationEnhancement of the momentum interpolation method on non-staggered grids
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS Int. J. Numer. Meth. Fluids 2000; 33: 1 22 Enhancement of the momentum interpolation method on non-staggered grids J. Papageorgakopoulos, G. Arampatzis,
More informationProject 4: Navier-Stokes Solution to Driven Cavity and Channel Flow Conditions
Project 4: Navier-Stokes Solution to Driven Cavity and Channel Flow Conditions R. S. Sellers MAE 5440, Computational Fluid Dynamics Utah State University, Department of Mechanical and Aerospace Engineering
More informationBasic Aspects of Discretization
Basic Aspects of Discretization Solution Methods Singularity Methods Panel method and VLM Simple, very powerful, can be used on PC Nonlinear flow effects were excluded Direct numerical Methods (Field Methods)
More informationA finite-volume algorithm for all speed flows
A finite-volume algorithm for all speed flows F. Moukalled and M. Darwish American University of Beirut, Faculty of Engineering & Architecture, Mechanical Engineering Department, P.O.Box 11-0236, Beirut,
More informationThe Finite Difference Method
Chapter 5. The Finite Difference Method This chapter derives the finite difference equations that are used in the conduction analyses in the next chapter and the techniques that are used to overcome computational
More informationIntroduction to CFD modelling of source terms and local-scale atmospheric dispersion (Part 1 of 2)
1 Introduction to CFD modelling of source terms and local-scale atmospheric dispersion (Part 1 of 2) Atmospheric Dispersion Modelling Liaison Committee (ADMLC) meeting 15 February 2018 Simon Gant, Fluid
More informationRANS Equations in Curvilinear Coordinates
Appendix C RANS Equations in Curvilinear Coordinates To begin with, the Reynolds-averaged Navier-Stokes RANS equations are presented in the familiar vector and Cartesian tensor forms. Each term in the
More informationEINDHOVEN UNIVERSITY OF TECHNOLOGY Department of Mathematics and Computer Science. CASA-Report March2008
EINDHOVEN UNIVERSITY OF TECHNOLOGY Department of Mathematics and Computer Science CASA-Report 08-08 March2008 The complexe flux scheme for spherically symmetrie conservation laws by J.H.M. ten Thije Boonkkamp,
More informationPressure-velocity correction method Finite Volume solution of Navier-Stokes equations Exercise: Finish solving the Navier Stokes equations
Today's Lecture 2D grid colocated arrangement staggered arrangement Exercise: Make a Fortran program which solves a system of linear equations using an iterative method SIMPLE algorithm Pressure-velocity
More informationOne Dimensional Convection: Interpolation Models for CFD
One Dimensional Convection: Interpolation Moels for CFD ME 448/548 Notes Geral Recktenwal Portlan State University Department of Mechanical Engineering gerry@p.eu ME 448/548: D Convection-Di usion Equation
More informationBasic Fluid Mechanics
Basic Fluid Mechanics Chapter 3B: Conservation of Mass C3B: Conservation of Mass 1 3.2 Governing Equations There are two basic types of governing equations that we will encounter in this course Differential
More informationCOMPUTATIONAL FLUID DYNAMICS
COMPUTATIONAL FLUID DYNAMICS A.E.P. Veldman Lecture Notes in Applied Mathematics Academic year 2010 2011 COMPUTATIONAL FLUID DYNAMICS Code: WICFD-03 MSc Applied Mathematics MSc Applied Physics MSc Mathematics
More informationCOMPUTATIONAL FLUID DYNAMICS
COMPUTATIONAL FLUID DYNAMICS A.E.P. Veldman Lecture Notes in Applied Mathematics JMBC PhD Course January 2010 COMPUTATIONAL FLUID DYNAMICS A.E.P. Veldman University of Groningen Institute of Mathematics
More informationAdvection / Hyperbolic PDEs. PHY 604: Computational Methods in Physics and Astrophysics II
Advection / Hyperbolic PDEs Notes In addition to the slides and code examples, my notes on PDEs with the finite-volume method are up online: https://github.com/open-astrophysics-bookshelf/numerical_exercises
More informationThis section develops numerically and analytically the geometric optimisation of
7 CHAPTER 7: MATHEMATICAL OPTIMISATION OF LAMINAR-FORCED CONVECTION HEAT TRANSFER THROUGH A VASCULARISED SOLID WITH COOLING CHANNELS 5 7.1. INTRODUCTION This section develops numerically and analytically
More informationSung-Ik Sohn and Jun Yong Shin
Commun. Korean Math. Soc. 17 (2002), No. 1, pp. 103 120 A SECOND ORDER UPWIND METHOD FOR LINEAR HYPERBOLIC SYSTEMS Sung-Ik Sohn and Jun Yong Shin Abstract. A second order upwind method for linear hyperbolic
More informationFluid Flow Modelling with Modelica
NE T E C H N I C A L N O T E Marco Bonvini 1*, Mirza Popovac 2 1 Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italia; 1 * bonvini@elet.polimi.it 2 Austrian
More informationLimitations of Richardson Extrapolation and Some Possible Remedies
Ismail Celik 1 Jun Li Gusheng Hu Christian Shaffer Mechanical and Aerospace Engineering Department, West Virginia University, Morgantown, WV 26506-6106 Limitations of Richardson Extrapolation and Some
More informationDue Tuesday, November 23 nd, 12:00 midnight
Due Tuesday, November 23 nd, 12:00 midnight This challenging but very rewarding homework is considering the finite element analysis of advection-diffusion and incompressible fluid flow problems. Problem
More informationDraft Notes ME 608 Numerical Methods in Heat, Mass, and Momentum Transfer
Draft Notes ME 608 Numerical Methods in Heat, Mass, and Momentum Transfer Instructor: Jayathi Y. Murthy School of Mechanical Engineering Purdue University Spring 00 c 1998 J.Y. Murthy and S.R. Mathur.
More informationMATHEMATICAL RELATIONSHIP BETWEEN GRID AND LOW PECLET NUMBERS FOR THE SOLUTION OF CONVECTION-DIFFUSION EQUATION
2006-2018 Asian Research Publishing Network (ARPN) All rights reserved wwwarpnjournalscom MATHEMATICAL RELATIONSHIP BETWEEN GRID AND LOW PECLET NUMBERS FOR THE SOLUTION OF CONVECTION-DIFFUSION EQUATION
More informationNotation Nodes are data points at which functional values are available or at which you wish to compute functional values At the nodes fx i
LECTURE 6 NUMERICAL DIFFERENTIATION To find discrete approximations to differentiation (since computers can only deal with functional values at discrete points) Uses of numerical differentiation To represent
More informationNumerical Oscillations and how to avoid them
Numerical Oscillations and how to avoid them Willem Hundsdorfer Talk for CWI Scientific Meeting, based on work with Anna Mozartova (CWI, RBS) & Marc Spijker (Leiden Univ.) For details: see thesis of A.
More informationENGR Heat Transfer II
ENGR 7901 - Heat Transfer II External Flows 1 Introduction In this chapter we will consider several fundamental flows, namely: the flat plate, the cylinder, the sphere, several other body shapes, and banks
More informationNumerical Solution Techniques in Mechanical and Aerospace Engineering
Numerical Solution Techniques in Mechanical and Aerospace Engineering Chunlei Liang LECTURE 9 Finite Volume method II 9.1. Outline of Lecture Conservation property of Finite Volume method Apply FVM to
More informationOn the spectral and conservation properties of nonlinear discretization operators.
On the spectral and conservation properties of nonlinear discretization operators. D. Fauconnier, E. Dick Department of Flow, Heat and Combustion Mechanics, Ghent University, St. Pietersnieuwstraat 41,
More informationNon-linear least squares
Non-linear least squares Concept of non-linear least squares We have extensively studied linear least squares or linear regression. We see that there is a unique regression line that can be determined
More informationBasics on Numerical Methods for Hyperbolic Equations
Basics on Numerical Methods for Hyperbolic Equations Professor Dr. E F Toro Laboratory of Applied Mathematics University of Trento, Italy eleuterio.toro@unitn.it http://www.ing.unitn.it/toro October 8,
More information3.3. Phase and Amplitude Errors of 1-D Advection Equation
3.3. Phase and Amplitude Errors of 1-D Advection Equation Reading: Duran section 2.4.2. Tannehill et al section 4.1.2. The following example F.D. solutions of a 1D advection equation show errors in both
More information2.29 Numerical Fluid Mechanics Fall 2011 Lecture 20
2.29 Numerical Fluid Mechanics Fall 2011 Lecture 20 REVIEW Lecture 19: Finite Volume Methods Review: Basic elements of a FV scheme and steps to step-up a FV scheme One Dimensional examples d x j x j 1/2
More informationComparative Study of Flux-limiters Based on MUST Differencing Scheme
International Journal of Computational Fluid Dynamics, 2003 Vol. not known (not known), pp. 1 8 Comparative Study of Flux-limiters Based on MUST Differencing Scheme V. JUNTASARO a, * and A.J. MARQUIS b,
More informationarxiv: v1 [physics.comp-ph] 10 Aug 2015
Numerical experiments on the efficiency of local grid refinement based on truncation error estimates Alexandros Syrakos a,, Georgios Efthimiou a, John G. Bartzis a, Apostolos Goulas b arxiv:1508.02345v1
More information2.29 Numerical Fluid Mechanics Spring 2015 Lecture 13
REVIEW Lecture 12: Spring 2015 Lecture 13 Grid-Refinement and Error estimation Estimation of the order of convergence and of the discretization error Richardson s extrapolation and Iterative improvements
More informationStabilization Techniques for Finite Element Analysis of Convection-Diffusion Problems
INTERNATIONAL CENTER FOR NUMERICAL METHODS IN ENGINEERING Stabilization Techniques for Finite Element Analysis of Convection-Diffusion Problems E. Oñate M. Manzan Publication CIMNE Nº-183, February 000
More informationMATLAB Solution of Flow and Heat Transfer through a Porous Cooling Channel and the Conjugate Heat Transfer in the Surrounding Wall
MATLAB Solution of Flow and Heat Transfer through a Porous Cooling Channel and the Conjugate Heat Transfer in the Surrounding Wall James Cherry, Mehmet Sözen Grand Valley State University, cherryj1@gmail.com,
More informationFinite Difference Solution of the Heat Equation
Finite Difference Solution of the Heat Equation Adam Powell 22.091 March 13 15, 2002 In example 4.3 (p. 10) of his lecture notes for March 11, Rodolfo Rosales gives the constant-density heat equation as:
More informationChapter 5. Formulation of FEM for Unsteady Problems
Chapter 5 Formulation of FEM for Unsteady Problems Two alternatives for formulating time dependent problems are called coupled space-time formulation and semi-discrete formulation. The first one treats
More informationAdvection in two dimensions
Lecture 0 Advection in two dimensions 6. Stability of multiple terms (in multiple dimensions) When we analyzed the stability of time-stepping methods we tended to consider either a single damping term
More informationNon-linear Methods for Scalar Equations
Non-linear Methods for Scalar Equations Professor Dr. E F Toro Laboratory of Applied Mathematics University of Trento, Italy eleuterio.toro@unitn.it http://www.ing.unitn.it/toro October 3, 04 / 56 Abstract
More informationComputational Fluid Dynamics Prof. Dr. SumanChakraborty Department of Mechanical Engineering Indian Institute of Technology, Kharagpur
Computational Fluid Dynamics Prof. Dr. SumanChakraborty Department of Mechanical Engineering Indian Institute of Technology, Kharagpur Lecture No. #11 Fundamentals of Discretization: Finite Difference
More informationLattice Boltzmann Method for Moving Boundaries
Lattice Boltzmann Method for Moving Boundaries Hans Groot March 18, 2009 Outline 1 Introduction 2 Moving Boundary Conditions 3 Cylinder in Transient Couette Flow 4 Collision-Advection Process for Moving
More informationModule 3: BASICS OF CFD. Part A: Finite Difference Methods
Module 3: BASICS OF CFD Part A: Finite Difference Methods THE CFD APPROACH Assembling the governing equations Identifying flow domain and boundary conditions Geometrical discretization of flow domain Discretization
More informationTHREE-DIMENSIONAL FINITE DIFFERENCE MODEL FOR TRANSPORT OF CONSERVATIVE POLLUTANTS
Pergamon Ocean Engng, Vol. 25, No. 6, pp. 425 442, 1998 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0029 8018/98 $19.00 + 0.00 PII: S0029 8018(97)00008 5 THREE-DIMENSIONAL FINITE
More informationRECENT DEVELOPMENTS IN COMPUTATIONAL REACTOR ANALYSIS
RECENT DEVELOPMENTS IN COMPUTATIONAL REACTOR ANALYSIS Dean Wang April 30, 2015 24.505 Nuclear Reactor Physics Outline 2 Introduction and Background Coupled T-H/Neutronics Safety Analysis Numerical schemes
More informationME Computational Fluid Mechanics Lecture 5
ME - 733 Computational Fluid Mechanics Lecture 5 Dr./ Ahmed Nagib Elmekawy Dec. 20, 2018 Elliptic PDEs: Finite Difference Formulation Using central difference formulation, the so called five-point formula
More informationFINITE VOLUME METHOD: BASIC PRINCIPLES AND EXAMPLES
FINITE VOLUME METHOD: BASIC PRINCIPLES AND EXAMPLES SHRUTI JAIN B.Tech III Year, Electronics and Communication IIT Roorkee Tutors: Professor G. Biswas Professor S. Chakraborty ACKNOWLEDGMENTS I would like
More informationOpen boundary conditions in numerical simulations of unsteady incompressible flow
Open boundary conditions in numerical simulations of unsteady incompressible flow M. P. Kirkpatrick S. W. Armfield Abstract In numerical simulations of unsteady incompressible flow, mass conservation can
More informationLecture 4.2 Finite Difference Approximation
Lecture 4. Finite Difference Approimation 1 Discretization As stated in Lecture 1.0, there are three steps in numerically solving the differential equations. They are: 1. Discretization of the domain by
More informationMOTIONAL MAGNETIC FINITE ELEMENT METHOD APPLIED TO HIGH SPEED ROTATING DEVICES
MOTIONAL MAGNETIC FINITE ELEMENT METHOD APPLIED TO HIGH SPEED ROTATING DEVICES Herbert De Gersem, Hans Vande Sande and Kay Hameyer Katholieke Universiteit Leuven, Dep. EE (ESAT), Div. ELEN, Kardinaal Mercierlaan
More informationAtwood number effects in buoyancy driven flows
Advanced Computational Methods in Heat Transfer IX 259 Atwood number effects in buoyancy driven flows M. J. Andrews & F. F. Jebrail Los Alamos National Laboratory, USA Abstract Consideration is given to
More informationModule 2: Introduction to Finite Volume Method Lecture 14: The Lecture deals with: The Basic Technique. Objectives_template
The Lecture deals with: The Basic Technique file:///d /chitra/nptel_phase2/mechanical/cfd/lecture14/14_1.htm[6/20/2012 4:40:30 PM] The Basic Technique We have introduced the finite difference method. In
More informationStudy of Forced and Free convection in Lid driven cavity problem
MIT Study of Forced and Free convection in Lid driven cavity problem 18.086 Project report Divya Panchanathan 5-11-2014 Aim To solve the Navier-stokes momentum equations for a lid driven cavity problem
More informationExtremum-Preserving Limiters for MUSCL and PPM
arxiv:0903.400v [physics.comp-ph] 7 Mar 009 Extremum-Preserving Limiters for MUSCL and PPM Michael Sekora Program in Applied and Computational Mathematics, Princeton University Princeton, NJ 08540, USA
More informationIntroduction to Physical Acoustics
Introduction to Physical Acoustics Class webpage CMSC 828D: Algorithms and systems for capture and playback of spatial audio. www.umiacs.umd.edu/~ramani/cmsc828d_audio Send me a test email message with
More informationComparison of (Some) Algorithms for Edge Gyrokinetics
Comparison of (Some) Algorithms for Edge Gyrokinetics Greg (G.W.) Hammett & Luc (J. L.) Peterson (PPPL) Gyrokinetic Turbulence Workshop, Wolfgang Pauli Institute, 15-19 Sep. 2008 w3.pppl.gov/~hammett Acknowledgments:
More informationCalculating equation coefficients
Fluid flow Calculating equation coefficients Construction Conservation Equation Surface Conservation Equation Fluid Conservation Equation needs flow estimation needs radiation and convection estimation
More information5. Advection and Diffusion of an Instantaneous, Point Source
1 5. Advection and Diffusion of an Instantaneous, Point Source In this chapter consider the combined transport by advection and diffusion for an instantaneous point release. We neglect source and sink
More informationFall Exam II. Wed. Nov. 9, 2005
Fall 005 10.34 Eam II. Wed. Nov. 9 005 (SOLUTION) Read through the entire eam before beginning work and budget your time. You are researching drug eluting stents and wish to understand better how the drug
More informationA Critical Investigation of High-Order Flux Limiters In Multiphase Flow Problems
A Critical Investigation o High-Order Flux Limiters In Multiphase Flow Problems Chris Guenther Fluent In., 3647 Collins Ferry Rd., Morgantown, WV 26505, USA cpg@luent.com ABSTRACT. In recent years inite
More informationBlock-Structured Adaptive Mesh Refinement
Block-Structured Adaptive Mesh Refinement Lecture 2 Incompressible Navier-Stokes Equations Fractional Step Scheme 1-D AMR for classical PDE s hyperbolic elliptic parabolic Accuracy considerations Bell
More informationA recovery-assisted DG code for the compressible Navier-Stokes equations
A recovery-assisted DG code for the compressible Navier-Stokes equations January 6 th, 217 5 th International Workshop on High-Order CFD Methods Kissimmee, Florida Philip E. Johnson & Eric Johnsen Scientific
More informationVector and scalar variables laminar natural convection in 2D geometry arbitrary angle of inclination
Vector and scalar variables laminar natural convection in 2D geometry arbitrary angle of inclination Miomir Raos and Ljubiša Nešić Abstract The aim of this study 1 is to analyze vector and scalar depending
More information1.061 / 1.61 Transport Processes in the Environment
MIT OpenCourseWare http://ocw.mit.edu 1.061 / 1.61 Transport Processes in the Environment Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Solution
More informationALGEBRAIC FLUX CORRECTION FOR FINITE ELEMENT DISCRETIZATIONS OF COUPLED SYSTEMS
Int. Conf. on Computational Methods for Coupled Problems in Science and Engineering COUPLED PROBLEMS 2007 M. Papadrakakis, E. Oñate and B. Schrefler (Eds) c CIMNE, Barcelona, 2007 ALGEBRAIC FLUX CORRECTION
More informationGodunov methods in GANDALF
Godunov methods in GANDALF Stefan Heigl David Hubber Judith Ngoumou USM, LMU, München 28th October 2015 Why not just stick with SPH? SPH is perfectly adequate in many scenarios but can fail, or at least
More informationCapSel Roe Roe solver.
CapSel Roe - 01 Roe solver keppens@rijnh.nl modern high resolution, shock-capturing schemes for Euler capitalize on known solution of the Riemann problem originally developed by Godunov always use conservative
More informationCFD Analysis of Mixing in Polymerization Reactor. By Haresh Patel Supervisors: Dr. R. Dhib & Dr. F. Ein-Mozaffari IPR 2007
CFD Analysis of Mixing in Polymerization Reactor By Haresh Patel Supervisors: Dr. R. Dhib & Dr. F. Ein-Mozaffari Introduction Model development Simulation Outline Model Setup for Fluent Results and discussion
More informationq t = F q x. (1) is a flux of q due to diffusion. Although very complex parameterizations for F q
! Revised Tuesday, December 8, 015! 1 Chapter 7: Diffusion Copyright 015, David A. Randall 7.1! Introduction Diffusion is a macroscopic statistical description of microscopic advection. Here microscopic
More informationFinite Volume Schemes: an introduction
Finite Volume Schemes: an introduction First lecture Annamaria Mazzia Dipartimento di Metodi e Modelli Matematici per le Scienze Applicate Università di Padova mazzia@dmsa.unipd.it Scuola di dottorato
More informationThe RAMSES code and related techniques I. Hydro solvers
The RAMSES code and related techniques I. Hydro solvers Outline - The Euler equations - Systems of conservation laws - The Riemann problem - The Godunov Method - Riemann solvers - 2D Godunov schemes -
More informationCORBIS: Code Raréfié Bidimensionnel Implicite Stationnaire
CORBIS: Code Raréfié Bidimensionnel Implicite Stationnaire main ingredients: [LM (M3AS 00, JCP 00)] plane flow: D BGK Model conservative and entropic velocity discretization space discretization: finite
More informationNumerical Methods for Conservation Laws WPI, January 2006 C. Ringhofer C2 b 2
Numerical Methods for Conservation Laws WPI, January 2006 C. Ringhofer ringhofer@asu.edu, C2 b 2 2 h2 x u http://math.la.asu.edu/ chris Last update: Jan 24, 2006 1 LITERATURE 1. Numerical Methods for Conservation
More informationProblem Set 4 Issued: Wednesday, March 18, 2015 Due: Wednesday, April 8, 2015
MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS 0139.9 NUMERICAL FLUID MECHANICS SPRING 015 Problem Set 4 Issued: Wednesday, March 18, 015 Due: Wednesday,
More informationME 608 Numerical Methods in Heat, Mass, and Momentum Transfer. q 0 = εσ ( T 4 T 4) Figure 1: Computational Domain for Problem 1.
ME 68 Numerical Methods in Heat, Mass, and Momentum Transfer Mid-Term Examination Solution Date: March 1, 21 6: 8: PM Instructor: J. Murthy Open Book, Open Notes Total: 1 points 1. Consider steady 1D conduction
More information