The document summarizes an edge-based finite-volume scheme for solving partial differential equations on triangular grids that achieves third-order accuracy without using curved elements. It presents a new boundary closure formula that allows the scheme to maintain third-order accuracy at boundary nodes. Numerical results are shown for the Burgers' equation and advection-diffusion equation on irregular triangular grids, demonstrating third-order convergence of errors at both interior and boundary nodes using the new boundary formula.
I am Anthony F. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, University of Cambridge, UK. I have been helping students with their homework for the past 8 years. I solve assignments related to Digital Signal Processing.
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On approximations of trigonometric Fourier series as transformed to alternati...IJERA Editor
Trigonometric Fourier series are transformed into: (1) alternating series with terms of finite sums or
(2) finite sums of alternating series. Alternating series may be approximated by finite sums more accurately as
compared to their simple truncation. The accuracy of these approximations is discussed.
I am Leonard K. I am a Stochastic Processes Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, University of Arkansas, USA.
I have been helping students with their homework for the past 6 years. I solve assignments related to Stochastic Processes.
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Regularized Compression of A Noisy Blurred Image ijcsa
Both regularization and compression are important issues in image processing and have been widely
approached in the literature. The usual procedure to obtain the compression of an image given through a
noisy blur requires two steps: first a deblurring step of the image and then a factorization step of the
regularized image to get an approximation in terms of low rank nonnegative factors. We examine here the
possibility of swapping the two steps by deblurring directly the noisy factors or partially denoised factors.
The experimentation shows that in this way images with comparable regularized compression can be
obtained with a lower computational cost.
I am Anthony F. I am a Digital Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, University of Cambridge, UK. I have been helping students with their homework for the past 8 years. I solve assignments related to Digital Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Digital Signal Processing Assignments.
On approximations of trigonometric Fourier series as transformed to alternati...IJERA Editor
Trigonometric Fourier series are transformed into: (1) alternating series with terms of finite sums or
(2) finite sums of alternating series. Alternating series may be approximated by finite sums more accurately as
compared to their simple truncation. The accuracy of these approximations is discussed.
I am Leonard K. I am a Stochastic Processes Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, University of Arkansas, USA.
I have been helping students with their homework for the past 6 years. I solve assignments related to Stochastic Processes.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Stochastic Processes Assignments.
Regularized Compression of A Noisy Blurred Image ijcsa
Both regularization and compression are important issues in image processing and have been widely
approached in the literature. The usual procedure to obtain the compression of an image given through a
noisy blur requires two steps: first a deblurring step of the image and then a factorization step of the
regularized image to get an approximation in terms of low rank nonnegative factors. We examine here the
possibility of swapping the two steps by deblurring directly the noisy factors or partially denoised factors.
The experimentation shows that in this way images with comparable regularized compression can be
obtained with a lower computational cost.
I am Keziah D. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of North Carolina, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Mechanical Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Mechanical Engineering Assignments.
On approximations of trigonometric Fourier series as transformed to alternati...IJERA Editor
Trigonometric Fourier series are transformed into: (1) alternating series with terms of finite sums or
(2) finite sums of alternating series. Alternating series may be approximated by finite sums more accurately as
compared to their simple truncation. The accuracy of these approximations is discussed.
I am Craig D. I am a Physical Chemistry Assignment Expert at eduassignmenthelp.com. I hold a Ph.D. in Physical Chemistry, from The University of Queensland. I have been helping students with their homework for the past 9 years. I solve assignments related to Physical Chemistry.
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We cover the inverses to the trigonometric functions sine, cosine, tangent, cotangent, secant, cosecant, and their derivatives. The remarkable fact is that although these functions and their inverses are transcendental (complicated) functions, the derivatives are algebraic functions. Also, we meet my all-time favorite function: arctan.
Fractional Newton-Raphson Method and Some Variants for the Solution of Nonlin...mathsjournal
The following document presents some novel numerical methods valid for one and several variables, which
using the fractional derivative, allow us to find solutions for some nonlinear systems in the complex space using
real initial conditions. The origin of these methods is the fractional Newton-Raphson method, but unlike the
latter, the orders proposed here for the fractional derivatives are functions. In the first method, a function is
used to guarantee an order of convergence (at least) quadratic, and in the other, a function is used to avoid the
discontinuity that is generated when the fractional derivative of the constants is used, and with this, it is possible
that the method has at most an order of convergence (at least) linear.
EM 알고리즘을 jensen's inequality부터 천천히 잘 설명되어있다
이것을 보면, LDA의 Variational method로 학습하는 방식이 어느정도 이해가 갈 것이다.
옛날 Andrew Ng 선생님의 강의노트에서 발췌한 건데 5년전에 본 것을
아직도 찾아가면서 참고하면서 해야 된다는 게 그 강의가 얼마나 명강의였는지 새삼 느끼게 된다.
I am Keziah D. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of North Carolina, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Mechanical Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Mechanical Engineering Assignments.
On approximations of trigonometric Fourier series as transformed to alternati...IJERA Editor
Trigonometric Fourier series are transformed into: (1) alternating series with terms of finite sums or
(2) finite sums of alternating series. Alternating series may be approximated by finite sums more accurately as
compared to their simple truncation. The accuracy of these approximations is discussed.
I am Craig D. I am a Physical Chemistry Assignment Expert at eduassignmenthelp.com. I hold a Ph.D. in Physical Chemistry, from The University of Queensland. I have been helping students with their homework for the past 9 years. I solve assignments related to Physical Chemistry.
Visit eduassignmenthelp.com or email info@eduassignmenthelp.com. You can also call on +1 678 648 4277 for any assistance with Physical Chemistry Assignments.
We cover the inverses to the trigonometric functions sine, cosine, tangent, cotangent, secant, cosecant, and their derivatives. The remarkable fact is that although these functions and their inverses are transcendental (complicated) functions, the derivatives are algebraic functions. Also, we meet my all-time favorite function: arctan.
Fractional Newton-Raphson Method and Some Variants for the Solution of Nonlin...mathsjournal
The following document presents some novel numerical methods valid for one and several variables, which
using the fractional derivative, allow us to find solutions for some nonlinear systems in the complex space using
real initial conditions. The origin of these methods is the fractional Newton-Raphson method, but unlike the
latter, the orders proposed here for the fractional derivatives are functions. In the first method, a function is
used to guarantee an order of convergence (at least) quadratic, and in the other, a function is used to avoid the
discontinuity that is generated when the fractional derivative of the constants is used, and with this, it is possible
that the method has at most an order of convergence (at least) linear.
EM 알고리즘을 jensen's inequality부터 천천히 잘 설명되어있다
이것을 보면, LDA의 Variational method로 학습하는 방식이 어느정도 이해가 갈 것이다.
옛날 Andrew Ng 선생님의 강의노트에서 발췌한 건데 5년전에 본 것을
아직도 찾아가면서 참고하면서 해야 된다는 게 그 강의가 얼마나 명강의였는지 새삼 느끼게 된다.
Numerical Solution of Diffusion Equation by Finite Difference Methodiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Andrey V. Savchenko - Sequential Hierarchical Image Recognition based on the ...AIST
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AIST Conference 2015 http://aistconf.org
Optimum Solution of Quadratic Programming Problem: By Wolfe’s Modified Simple...IJLT EMAS
In this paper, an alternative approach to the Wolfe’s
method for Quadratic Programming is suggested. Here we
proposed a new approach based on the iterative procedure for
the solution of a Quadratic Programming Problem by Wolfe’s
modified simplex method. The method sometimes involves less or
at the most an equal number of iteration as compared to
computational procedure for solving NLPP. We observed that
the rule of selecting pivot vector at initial stage and thereby for
some NLPP it takes more number of iteration to achieve
optimality. Here at the initial step we choose the pivot vector on
the basis of new rules described below. This powerful technique
is better understood by resolving a cycling problem.
Local Model Checking Algorithm Based on Mu-calculus with Partial OrdersTELKOMNIKA JOURNAL
The propositionalμ-calculus can be divided into two categories, global model checking algorithm
and local model checking algorithm. Both of them aim at reducing time complexity and space complexity
effectively. This paper analyzes the computing process of alternating fixpoint nested in detail and designs
an efficient local model checking algorithm based on the propositional μ-calculus by a group of partial
ordered relation, and its time complexity is O(d2(dn)d/2+2) (d is the depth of fixpoint nesting, n is the
maximum of number of nodes), space complexity is O(d(dn)d/2). As far as we know, up till now, the best
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complexity of this algorithm is reduced from d to d/2. It is more efficient than algorithms of previous
research.
This first lecture describes what EMT is. Its history of evolution. Main personalities how discovered theories relating to this theory. Applications of EMT . Scalars and vectors and there algebra. Coordinate systems. Field, Coulombs law and electric field intensity.volume charge distribution, electric flux density, gauss's law and divergence
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The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Similar to NIACFDS2014-12-16_Nishikawa_BoundaryFormula (20)
The International Journal of Engineering and Science (The IJES)
NIACFDS2014-12-16_Nishikawa_BoundaryFormula
1. Accuracy-Preserving Boundary Quadrature
for Edge-Based Finite-Volume Scheme:
Third-order accuracy without curved elements
Hiroaki Nishikawa
National Institute of Aerospace
!
!
56th NIA CFD Seminar
December 16, 2014
JCP2015, v281, pp518-555
2. “You Already Have It.”
You already have what you want:
happiness, jobs, money, or anything.
The issue is always just how to
manifest what you already have.
The first step is to believe it.
4. Edge-Based Finite-Volume Method
Edge-based finite-volume scheme:
with the upwind flux at edge midpoint:
NASA’s FUN3D; Software Cradle’s SC/Tetra; DLR Tau code, etc.
k
j
nr
jk
nℓ
jk
- 1st-order with nodal values
- 2nd-order with linear extrapolation, linear LSQ
- 3rd-order with linear extrapolation, quadratic LSQ Katz&Sankaran(JCP2011)
Accuracy with left/right states:
Efficient 3rd-order scheme: edge-loop with a flux per edge
5. Why Third-Order? Part I
Zero dissipation for quadratic solution
Linear extrapolation with quadratic LSQ gradients:
For a quadratic solution, it gives
and therefore,
The same left and right states, but not exact.
k
j
nr
jk
nℓ
jk
Averaged flux is the source of error.
JCP2015, v281, pp518-555
6. Exact for quadratic fluxes
Linear flux extrapolation with quadratic LSQ gradients:
For a quadratic flux, it gives
and the edge-based discretization becomes
The same left and right fluxes, but not exact.
k
j
nr
jk
nℓ
jk
Exact for quadratic fluxes, and thus third-order accurate
True for arbitrary triangles/tetrahedra
Why Third-Order? Part II
JCP2015, v281, pp518-555
7. Lost with Exact Flux
If the flux is exact for quadratic fluxes (e.g., quadratic
extrapolation or kappa=0.5 with UMUSCL), we have
and the edge-based discretization becomes
k
j
nr
jk
nℓ
jk
JCP2015, v281, pp518-555
TE=O(h), and so DE=O(h^2)
3rd-order is lost if the flux is exact for quadratic fluxes.
DO NOT use quadratic extrapolation nor kappa=0.5 for fluxes.
9. “You Already Have It.”
EB Scheme for Diffusion, Source, Unsteady
3rd-order EB scheme for applies to various equations.
- Diffusion (Laplace) [ JCP2014 ]
Preprint accepted for publication in Journal of Computational Physics, 2014.
where
F =
⎡
⎢
⎢
⎣
au − νp
−u/Tr − (y − yj) q/Tr
(x − xj) q/Tr
⎤
⎥
⎥
⎦ , G =
⎡
⎢
⎢
⎣
bu − νq
(y − yj) p/Tr
−u/Tr − (x − xj) p/Tr
⎤
⎥
⎥
⎦ ,
where Tr = (2
√
νπ)−2
as described in Ref.[18], and (xj, yj) denotes the location of a node at wh
is discretized. The third-order edge-based discretization is constructed based on this system a
Section 2.1. For the second-order scheme, the terms involving (x − xj) and (y − yj), which re
terms, are not used, and a point-integration has been used for the source terms as describe
Ref.[18]. Note that u satisfies the advection-diffusion equation (43), and also the gradients a
(p, q) = (∂xu, ∂yu) to the same order of accuracy as demonstrated in Ref.[18]. The nodal gradien
in the edge-based discretization, are computed in the same way as in the previous case excep
∇uj = (pj, qj) and avoid the gradient computation for u [18]. The resulting second- and third-or
finite-volume schemes are referred to as SchemeII(2nd) and SchemeII(3rd), respectively, as in
domain is a square as in the previous case. For this problem, we set (a, b) = (1.23, 0.12), and ν
Re = 10. The following exact solution [23] is specified everywhere on the boundary:
u(x, y) = cos(2πη) exp
−2π2
ν
1 +
√
1 + 4π2ν2
ξ ,
where ξ = ax + by, η = bx − ay. For the variables, (p, q), we specify the exact gradients on t
except on the bottom (y = 0), where the variable q, corresponding to the normal derivative, is co
numerical scheme solving the third equation for q; the boundary flux quadrature is then require
errors are, therefore, computed for the third equation. In this problem, we focus on the ac
boundary nodes for isotropic and anisotropic grids.
Steady Conservation Law
- Source term [ JCP2012 ]
Steady Conservation Law
- Unsteady [ CF2014 ]
Steady Conservation Law (implicit time integration)
10. Two Approaches
Pincock and Katz, JSC, v61, Issue2, pp454-476
1. Modify the target equation - extra equations
(so that, the same 3rd-order EB scheme can be applied.)
!
!
2. Modify the scheme - extra computational work
diffusion, source terms, and unsteady equations
1st/2nd/3rd-order Hyperbolic NS Schemes [AIAA2014-2091]
(4th-order viscous with cubic LSQ gradients)
3rd-order EB scheme has already been demonstrated for NS.
12. Boundary Closure
If BCs are imposed through numerical fluxes, the residual needs to be
closed by boundary contributions at boundary nodes.
2
3
5
4
j
nL
B nR
B
Boundary contributions must be defined such that
the overall discretization is exact for linear/quadratic fluxes:
It depends on the element type.
13. Second-Order Formulas
Available for triangles, quadrilaterals,
tetrahedra, hexahedra, prisms, pyramids.
See Appendix B. in AIAA2010-5093
Note: Some regularity is required for quadrilaterals, hexahedra, prisms, and pyramids.
Exact for linear fluxes
14. Second-Order for Triangles
Appendix B. in AIAA2010-5093
2
3
5
4
j
nL
B nR
B
Boundary condition is set in the right (ghost) state,
and let the numerical flux determine the boundary flux.
This formula has been known for decades (see, e.g., AIAA Paper 95-0348, 1995).
3rd-order formula is not known at this point….
Let’s see what we get with the 2nd-order boundary formula.
See, e.g., NASA-TM-2011-217181, AIAA2014-2923
15. x
y
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
Terrible Results
Advection-diffusion problem
We need a formula for third-order.
2nd-order boundary formula doesn’t seem compatible with 3rd-order EB scheme.
x 0.01
Solution
16. A Formula for Third-Order?
How can I derive such a formula? Very difficult…..
2
3
5
4
j
nL
B nR
B
The overall discretization must be exact quadratic fluxes.
17. “You Already Have It.”
Free yourself and reset your mindset:
Forget about deriving the formula.
Believe it.
Behold what you have.
27. A General Formula
3rd-order with straight boundary edges.
2
3
5
4
j
b
nL
B nR
B
Note: the second terms (j5 and j2 terms) require linear extrapolation.
It preserves 1st/2nd/3rd-order accuracy at boundary nodes.
28. Remaks
!
• A general formula derived for tetrahedra (3D).
!
• Curved elements should not be used.
The EB discretization is 3rd-order on linear triangular elements.
Immediately applicable to existing grids (high-order grids not needed).
Other 3rd-order schemes on linear elements:
See JCP2015, v281, pp518-555
[ AIAA2001-2595 ]
[ IJNMF2007 ]
- 3rd-order fluctuation-splitting scheme
- 3rd-order LSQ scheme
- 3rd-order residual-distribution scheme [Mazaheri and Nishikawa 2015]
Attractive alternatives to high-order methods in applications
for which 3rd-order accuracy is sufficient.
31. Boundary Formulas
- One-point formula (1st-order, 3rd-order for flat uniform boundary)
2
3
5
4
j
b
nL
B nR
B
- Two-point formula (2nd-order)
- General formula (3rd-order)
33. Numerical Results
!
• Third-order results (see for 2nd-order results)
• Burgers, advection-diffusion, Laplace
• All equations solved as a steady conservation law
• 10 Irregular triangular grids: 1089 to 409,600 nodes.
• Residuals reduced by 10 orders in all cases.
• Errors measured at interior and boundary nodes
JCP2015
34. Burgers’ Equation
where and
- Weak condition at top boundary (outflow); exact solution imposed elsewhere.
- Errors measured at interior nodes and boundary nodes separately.
Exact solution:
x
y
0 1
0
1
3rd-order EB scheme applied in the steady conservation form.
[ JCP2012 ]
35. Burgers’ Equation
Interior Nodes
Error doesn’t propagate back into the domain.
3 2.5 2 1.5
10
9
8
7
6
5
4
3
2
1
0
1
Log
10
(h)
Log
10
(L
1
errornormofu)
DE−I, IRRG, 3rd, Burgers
One point
Two point
General
Slope 1
Slope 2
Slope 3
3 2.5 2 1.5
10
9
8
7
6
5
4
3
2
1
0
1
Log
10
(h)
Log
10
(L
1
TEnorm)
TE−I, IRRG, 3rd, Burgers
One point
Two point
General
Slope 1
Slope 2
Slope 3
Boundary Nodes
3 2.5 2 1.5
10
9
8
7
6
5
4
3
2
1
0
1
Log
10
(h)
Log
10
(L
1
errornormofu)
DE−B, IRRG, 3rd, Burgers
One point
Two point
General
Slope 1
Slope 2
Slope 3
3
10
9
8
7
6
5
4
3
2
1
0
1
Log10
(L1
TEnorm)
TE−B
36. Advection-Diffusion
- Weak condition at bottom boundary: specify (u,p) and compute q(=uy).
the exact solution imposed elsewhere.
- Errors measured in q at interior and boundary nodes separately.
x
y
0 1
0
1
3rd-order EB scheme applied in the steady conservation form.
[ JCP2014 ]
37. Advection-Diffusion
3 2.5 2 1.5
10
9
8
7
6
5
4
3
2
1
0
1
Log
10
(h)
Log10
(L1
errornormofq)
DE−I, IRRG, 3rd, Adv−Diff
One point
Two point
General
Slope 1
Slope 2
Slope 3
3 2.5 2 1.5
10
9
8
7
6
5
4
3
2
1
0
1
Log
10
(h)
Log10
(L1
TEnormforthethirdequation)
TE−I, IRRG, 3rd, Adv−Diff
One point
Two point
General
Slope 1
Slope 2
Slope 3
Interior Nodes
Boundary formula affects both boundary and interior:
1st/2nd/3rd-order with one/two- and general formula.
3 2.5 2 1.5
10
9
8
7
6
5
4
3
2
1
0
1
Log
10
(h)
Log
10
(L
1
errornormofq)
DE−B, IRRG, 3rd, Adv−Diff
One point
Two point
General
Slope 1
Slope 2
Slope 3
3 2.5
10
9
8
7
6
5
4
3
2
1
0
1
L
Log
10
(L
1
TEnormforthethirdequation)
TE−B, IRR
Boundary Nodes
Isotropic Grids
38. Advection-Diffusion
Anisotropic Grids
x
y
0 1
0
1
x
y
0 1
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.01
Flat uniform boundary grid:
- One-point formula: 3rd-order for flat uniform boundary
- Two-point formula: 2nd-order
41. Governing Equation
Laplace equation for the stream function
Third-order EB discretization is applied in the steady
conservation form.
Extra variables, p and q, correspond to the velocity components.
Third-order EB scheme produces 3rd-order accurate (u,v).
[ JCP2014 ]
42. The last equation approximates
Boundary Condition
Strong and weak conditions
1. Outer boundary: Specify the exact solution.
2. Cylinder (slip wall)
This is where the boundary flux is required.
43. Normal and TangentVectors
1. Linear Approximation
2. Quadratic Approximation
Quadratic interpolation over 3 nodes in the parameter space of edge-length, s.
NOTE: - This is reconstruction.
No analytical geometry is required.
- Geometrical singularities (e.g., corner)
would require a special treatment.
44. Linear Approximation
1.8 1.6 1.4 1.2 1
5.5
5
4.5
4
3.5
3
2.5
2
Log10
(h)
Log
10
(L
1
errornormofq)
DE−I, IRRG, 3rd, cylinder
One point
Two point
General
Slope 1
Slope 2
Slope 3
1.8 1.6 1.4 1.2 1
3.5
3
2.5
2
1.5
1
0.5
0
Log10
(h)
Log10
(L1
TEnormforthethirdequation)
TE−I, IRRG, 3rd, cylinder
One point
Two point
General
Slope 1
Slope 2
Slope 3
Interior Nodes
No boundary formula gives 3rd-order accuracy
Boundary Nodes
1.8 1.6 1.4 1.2 1
4.5
4
3.5
3
2.5
2
1.5
1
0.5
Log10
(h)
Log10
(L1
errornormofq)
DE−B, IRRG, 3rd, cylinder
One point
Two point
General
Slope 1
Slope 2
Slope 3
1.8 1.6
2
1.5
1
0.5
0
0.5
1
1.5
Log10
(L1
TEnormforthethirdequation)
TE−B
Discretization error in the x-velocity (u)
45. 1.8 1.6 1.4 1.2 1
5.5
5
4.5
4
3.5
3
2.5
2
Log
10
(h)
Log
10
(L
1
errornormofq)
DE−I, IRRG, 3rd, cylinder
One point
Two point
General
Slope 1
Slope 2
Slope 3
1.8 1.6 1.4 1.2 1
3.5
3
2.5
2
1.5
1
0.5
0
Log
10
(h)
Log
10
(L
1
TEnormforthethirdequation)
TE−I, IRRG, 3rd, cylinder
One point
Two point
General
Slope 1
Slope 2
Slope 3
Quadratic Approximation
Interior Nodes Boundary Nodes
1.8 1.6 1.4 1.2 1
4.5
4
3.5
3
2.5
2
1.5
1
0.5
Log
10
(h)
Log
10
(L
1
errornormofq)
DE−B, IRRG, 3rd, cylinder
One point
Two point
General
Slope 1
Slope 2
Slope 3
1.8 1.6
2
1.5
1
0.5
0
0.5
1
1.5
Log
10
(L
1
TEnormforthethirdequation)
TE−B,
Only the general formula gives 3rd-order accuracy.
Discretization error in the x-velocity (u)
47. Conclusions
See JCP2015, v281, pp518-555 for details.
- Edge-based scheme is 1st/2nd/3rd-order through boundary nodes.
!
-We already had a general boundary quadrature formula.
!
- Numerical flux should NOT be exact for quadratic fluxes
!
- 3rd-order accurate without curved elements.
!
- Accurate normal vectors required for 3rd-order (quadratic interpolation)
See AIAA2014-2091 for NS results.
48. Don’t worry,
if Santa Claus doesn’t bring you a present.
You already have it!
Happy Holidays!