course sd 2225 heat transfer by conduction in a 2d metallic plate
DESCRIPTION
Course SD 2225 Heat transfer by conduction in a 2D metallic plate. Pau Mallol, Georgios Spanopoulos, Alan Vargas KTH, April 2008. Physical Background. Heat transfer: thermal energy in transit due to a spatial temperature difference within/between media. Modes of heat transfer:. - PowerPoint PPT PresentationTRANSCRIPT
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Course SD 2225
Heat transfer by conductionin a 2D metallic plate
Pau Mallol, Georgios Spanopoulos, Alan Vargas KTH, April 2008
2
Physical Background• Heat transfer: thermal energy in transit due to a
spatial temperature difference within/between media.
• Modes of heat transfer:
3
Differential Equation• The equation that governs the process is:
Heat sources
Convection with air
Radiation
• Assumptions: - no heat sources in plate - no convection - no radiation - constant conduction thermal conductivity k
02
2
2
2
y
T
x
TT
Poisson’s Equation
4
Boundary Conditions• One side is thermally insulated, whereas the
rest kept at a certain constant temperature
5
Meshing• 3 meshes with both COMSOL and MATLAB
a) 19 x 13 b) 49 x 31 c) 124 x 76
6
COMSOL: Resolution & Results
7
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
10
20
30
40
50
60
70
80
90
100LENGTHWISE TEMPERATURE Y=0.1 COMSOL
X [m]
T [º
C]
Coarse 13x19
Medium 31x49Fine 76x124
COMSOL: Resolution & Results
8
COARSE19 X 13
MEDIUM49 X 31
FINE124 X 76
Number of elements
247 1519 9424
ComputingTime [s]
0.094 0.157 0.939
BenchmarkTemperature [oC]Point (0.2,0.6)m
67.347493 67.346702 67.346667
COMSOL: Resolution & Results
9
MATLAB: Discretization• DG: using same stepsize h in both directions
• DD: 2nd order Finite Difference Method
11
h
Nh
M6.0
2
11
2
2
2
11
2
2
2),(
2),(
h
TTTyx
y
T
h
TTTyx
x
T
ijijijii
jiijjiii
02
2
2
2
y
T
x
TT
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• DD (cont.): discretized DE
04 1111 ijijjijiij TTTTT
• DB: 1st and 2nd order Finite Difference Method
0),( 1
h
TTyx
y
T ijijii
02
),( 11
h
TTyx
y
T ijijii
1st order
2nd order
ijij TT 1
11 ijij TT
MATLAB: Discretization
11
MATLAB: Linear Sytems of Eq.
• Analytical 2D problem results to be 1D problem after discretization.
12
• Elliptic DE has been reduced to a linear system of MxN EQUATIONS to be solved.
• There are MxN UNKNOWNS, the discretized temperatures in all points of the grid.
• STIFFNESS & STABILITY ?
MATLAB: Linear Sytems of Eq.
• System is of very SPARSE nature -> treat it this way to save computational effort.
ACOARSE19 X 13
MEDIUM49 X 31
FINE124 X 76
λMAX -0.0381 -0.0064 -0.0016
λMIN -7.9215 -7.9861 -7.9964
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MATLAB: Resolution & ResultsTEMPERATURE DISTRIBUTION
20 40 60 80 100 120
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
80
90
100
14
MATLAB: Resolution & Results
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
10
20
30
40
50
60
70
80
90
100LENGTHWISE TEMPERATURE Y=0.1 MATLAB
X [m]
T [
ºC]
Coarse 13x19
Medium 31x49Fine 76x124
15
COMSOL & MATLAB: comparison
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
10
20
30
40
50
60
70
80
90
100LENGTHWISE TEMPERATURE Y=0.1 COARSE
X [m]
T [
ºC]
COMSOL
MATLAB
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
10
20
30
40
50
60
70
80
90
100LENGTHWISE TEMPERATURE Y=0.1 MEDIUM
X [m]
T [
ºC]
COMSOL
MATLAB
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
10
20
30
40
50
60
70
80
90
100LENGTHWISE TEMPERATURE Y=0.1 FINE
X [m]
T [
ºC]
COMSOL
MATLAB • COMSOL insensitive to mesh fineness.
• MATLAB depends strongly upon mesh fineness -> ACCURACY
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COMSOL & MATLAB: comparison
• COMSOL is more efficient with big systems.
COARSE19 X 13
MEDIUM49 X 31
FINE124 X 76
Number of elements
247 1519 9424
COMSOLTime [s] 0.094 0.157 0.939
MATLABTime [s]
0.041 0.082 1.826
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Conclusions
• Max/Min temperatures not consistent in COMSOL (depend on mesh); MATLAB is OK.
• COMSOL: easier, faster, more accurate and efficient than MATLAB.
• But COMSOL is particular use and MATLAB offers infinite possibilities (general).
• STABILITY: numerical systems to these PDE’s are always stable, no matter what h.
• ACCURACY: in COMSOL does not depend on h, in MATLAB strongly depends on h -> limitation: backward slash operator A\b size of A limited to about 10000.