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  1. .gitattributes +5 -0
  2. CylinderKnMa/Kn0p26/simulation0524_Kn0p26Ma4p3/output/grid.300000.dat +3 -0
  3. CylinderKnMa/Kn0p26/simulation0525_Kn0p26Ma4p4/output/grid.300000.dat +3 -0
  4. CylinderKnMa/Kn0p26/simulation0526_Kn0p26Ma4p5/output/grid.300000.dat +3 -0
  5. CylinderKnMa/Kn0p26/simulation0527_Kn0p26Ma4p6/output/grid.300000.dat +3 -0
  6. CylinderKnMa/Kn0p26/simulation0528_Kn0p26Ma4p7/output/grid.300000.dat +3 -0
  7. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/Lambda/Lambda_0_300000.vtu +0 -0
  8. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/Lambda/Lambda_1_300000.vtu +0 -0
  9. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/Lambda/Lambda_2_300000.vtu +0 -0
  10. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/Lambda/Lambda_300000.pvtu +21 -0
  11. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/Lambda/Lambda_3_300000.vtu +27 -0
  12. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/Surf.pvd +9 -0
  13. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_0_300000.vtu +0 -0
  14. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_1_300000.vtu +0 -0
  15. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_2_300000.vtu +0 -0
  16. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_300000.pvtu +29 -0
  17. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_3_300000.vtu +0 -0
  18. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/final-grid.dat +0 -0
  19. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/log.sparta +319 -0
  20. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/mir.txt +3 -0
  21. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/output/lambdakn.300000.dat +0 -0
  22. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/output/surf.300000.dat +209 -0
  23. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/runmpirun128.sh +18 -0
  24. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/runmpirun64.sh +18 -0
  25. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/slurm-28429213.out +205 -0
  26. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/surf/Surf_300000.vtu +38 -0
  27. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/surf/geom.surf +610 -0
  28. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Grid.pvd +9 -0
  29. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Lambda.pvd +9 -0
  30. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Lambda/Lambda_0_300000.vtu +0 -0
  31. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Lambda/Lambda_1_300000.vtu +0 -0
  32. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Lambda/Lambda_2_300000.vtu +0 -0
  33. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Lambda/Lambda_300000.pvtu +21 -0
  34. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Lambda/Lambda_3_300000.vtu +27 -0
  35. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Surf.pvd +9 -0
  36. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/config.txt +78 -0
  37. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/cylinder.surf +410 -0
  38. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/gas.species +14 -0
  39. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/gas.vhs +9 -0
  40. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/grid/Grid_0_300000.vtu +0 -0
  41. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/grid/Grid_1_300000.vtu +0 -0
  42. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/grid/Grid_2_300000.vtu +0 -0
  43. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/grid/Grid_300000.pvtu +29 -0
  44. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/grid/Grid_3_300000.vtu +0 -0
  45. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/grid/final-grid.dat +0 -0
  46. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/log.sparta +319 -0
  47. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/mir.txt +3 -0
  48. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/output/lambdakn.300000.dat +0 -0
  49. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/output/surf.300000.dat +209 -0
  50. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/runmpirun128.sh +18 -0
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AQAAAACAAAAoUwAAWwIAAA==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AgAAAACAAACMAgAAZRsAABIBAAA=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AQAAAACAAAAoUwAAzQ0AAA==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AQAAAACAAABlCgAAGgAAAA==eJztwSEBAAAAwyC9/oVf4hIoAAA4G4r2XY4=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/Surf.pvd ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="Collection" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Surf/Surf_300000.vtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_0_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_1_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_2_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_300000.pvtu ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="PUnstructuredGrid" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <PUnstructuredGrid GhostLevel="0">
6
+ <PCellData>
7
+ <PDataArray type="Float64" Name="id"/>
8
+ <PDataArray type="Float64" Name="xc"/>
9
+ <PDataArray type="Float64" Name="yc"/>
10
+ <PDataArray type="Float64" Name="f_1[1]"/>
11
+ <PDataArray type="Float64" Name="f_1[2]"/>
12
+ <PDataArray type="Float64" Name="f_1[3]"/>
13
+ <PDataArray type="Float64" Name="f_1[4]"/>
14
+ <PDataArray type="Float64" Name="f_1[5]"/>
15
+ <PDataArray type="Float64" Name="f_1[6]"/>
16
+ <PDataArray type="Float64" Name="f_1[7]"/>
17
+ <PDataArray type="Float64" Name="f_1[8]"/>
18
+ <PDataArray type="Float64" Name="f_1[9]"/>
19
+ <PDataArray type="Float64" Name="f_1[10]"/>
20
+ </PCellData>
21
+ <PPoints>
22
+ <PDataArray type="Float32" Name="Points" NumberOfComponents="3"/>
23
+ </PPoints>
24
+ <Piece Source="Grid_0_300000.vtu"/>
25
+ <Piece Source="Grid_1_300000.vtu"/>
26
+ <Piece Source="Grid_2_300000.vtu"/>
27
+ <Piece Source="Grid_3_300000.vtu"/>
28
+ </PUnstructuredGrid>
29
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/Grid_3_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/grid/final-grid.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/log.sparta ADDED
@@ -0,0 +1,319 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ variable BZ equal 1.380649E-23
4
+ variable PI equal 3.1415926536
5
+ variable molecular_mass equal 6.63E-26
6
+ variable molecular_diameter equal 4.17E-10
7
+ variable gamma equal 1.666666667
8
+ variable R equal ${BZ}/${molecular_mass}
9
+ variable R equal 1.380649e-23/${molecular_mass}
10
+ variable R equal 1.380649e-23/6.63e-26
11
+ variable L equal 1.0
12
+ variable xmin equal -5.0
13
+ variable xmax equal 6.0
14
+ variable ymin equal -5.0
15
+ variable ymax equal 5.0
16
+ variable Kn equal 0.28
17
+ variable Ma equal 3.2
18
+ variable To equal 273.0
19
+ variable Tb equal 273.0
20
+ variable theta equal 0.0
21
+ variable mfp equal ${Kn}*${L}
22
+ variable mfp equal 0.28*${L}
23
+ variable mfp equal 0.28*1
24
+ variable cpmfp equal 4
25
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
26
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
27
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
28
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(${mfp}/${cpmfp}*${Kn})
29
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/${cpmfp}*${Kn})
30
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*${Kn})
31
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*0.28)
32
+ variable sos equal sqrt(${gamma}*${R}*${To})
33
+ variable sos equal sqrt(1.666666667*${R}*${To})
34
+ variable sos equal sqrt(1.666666667*208.242684766214*${To})
35
+ variable sos equal sqrt(1.666666667*208.242684766214*273)
36
+ variable vo equal ${Ma}*${sos}
37
+ variable vo equal 3.2*${sos}
38
+ variable vo equal 3.2*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 985.009805563779*cos(${theta}*2*PI/360)
41
+ variable vx equal 985.009805563779*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 985.009805563779*sin(${theta}*2*PI/360)
44
+ variable vy equal 985.009805563779*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
47
+ variable xncells equal (6-${xmin})/${mfp}*${cpmfp}
48
+ variable xncells equal (6--5)/${mfp}*${cpmfp}
49
+ variable xncells equal (6--5)/0.28*${cpmfp}
50
+ variable xncells equal (6--5)/0.28*4
51
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
52
+ variable yncells equal (5-${ymin})/${mfp}*${cpmfp}
53
+ variable yncells equal (5--5)/${mfp}*${cpmfp}
54
+ variable yncells equal (5--5)/0.28*${cpmfp}
55
+ variable yncells equal (5--5)/0.28*4
56
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
57
+ variable Fnum equal 6.60399822965135e+19*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
58
+ variable Fnum equal 6.60399822965135e+19*(6-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
59
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
60
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5-${ymin})/${ppc}/${xncells}/${yncells}
61
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/${ppc}/${xncells}/${yncells}
62
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/${xncells}/${yncells}
63
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/${yncells}
64
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/142.857142857143
65
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
66
+ variable tstep equal (--5+${xmax})/${vx}/${xncells}/10/4
67
+ variable tstep equal (--5+6)/${vx}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/985.009805563779/${xncells}/10/4
69
+ variable tstep equal (--5+6)/985.009805563779/157.142857142857/10/4
70
+ variable nave equal 10000
71
+ variable noutput equal 300000
72
+ clear
73
+ Running on 128 MPI task(s)
74
+ shell mkdir output
75
+ shell mkdir grid
76
+ shell mkdir surf
77
+ seed 7
78
+ dimension 2
79
+ global nrho ${nden}
80
+ global nrho 6.60399822965135e+19
81
+ global fnum ${Fnum}
82
+ global fnum 1.07865304417639e+16
83
+ timestep ${tstep}
84
+ timestep 1.77663206002135e-06
85
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
86
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
87
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
88
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
89
+ boundary o o p
90
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
91
+ create_box -5 ${xmax} ${ymin} ${ymax} -0.5 0.5
92
+ create_box -5 6 ${ymin} ${ymax} -0.5 0.5
93
+ create_box -5 6 -5 ${ymax} -0.5 0.5
94
+ create_box -5 6 -5 5 -0.5 0.5
95
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
96
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
97
+ create_grid 157.142857142857 ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
98
+ create_grid 157.142857142857 142.857142857143 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
99
+ Created 97600 child grid cells
100
+ CPU time = 0.146769 secs
101
+ create/ghost percent = 0.855346 99.1447
102
+ balance_grid rcb cell
103
+ Balance grid migrated 97380 cells
104
+ CPU time = 0.108424 secs
105
+ reassign/sort/migrate/ghost percent = 51.4419 0.0368276 7.9095 40.6118
106
+ species gas.species Ar
107
+ mixture air Ar frac 1.0
108
+ mixture air Ar vstream ${vx} ${vy} 0.0 temp ${To}
109
+ mixture air Ar vstream 985.009805563779 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 985.009805563779 0 0.0 temp ${To}
111
+ mixture air Ar vstream 985.009805563779 0 0.0 temp 273
112
+ collide vss air gas.vhs
113
+ collide_modify vremax 100 yes remain no nearcp yes 10
114
+ read_surf cylinder.surf
115
+ 200 lines
116
+ -0.5 0.5 xlo xhi
117
+ -0.5 0.5 ylo yhi
118
+ 0 0 zlo zhi
119
+ 0.0157073 min line length
120
+ 456 0 = cells overlapping surfs, overlap cells with unmarked corner pts
121
+ 87188 9956 456 = cells outside/inside/overlapping surfs
122
+ 456 = surf cells with 1,2,etc splits
123
+ 109.215 109.215 = cell-wise and global flow volume
124
+ CPU time = 0.116235 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 21.1817 18.9324 2.27225 20.5552 37.0585 5.55688 0.162444
126
+ surf2grid time = 0.0238924 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 15.1969 23.8279 5.76592 1.98564 11.5168 6.36
128
+ surf_collide 1 diffuse 273.0 1.0
129
+ surf_modify all collide 1
130
+ fix in emit/face air xlo ylo yhi
131
+ balance_grid rcb cell
132
+ Balance grid migrated 1938 cells
133
+ CPU time = 0.0521031 secs
134
+ reassign/sort/migrate/ghost percent = 6.23771 0.0433563 16.842 76.877
135
+ create_particles air n 0
136
+ WARNING: Created unexpected # of particles: 668660 versus 668661 (../create_particles.cpp:346)
137
+ Created 668660 particles
138
+ CPU time = 0.0163372 secs
139
+ stats 10000
140
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
141
+ run 200000
142
+ Memory usage per proc in Mbytes:
143
+ particles (ave,min,max) = 1.79297 0 3.375
144
+ grid (ave,min,max) = 4.297 1.51379 7.13879
145
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
146
+ total (ave,min,max) = 6.11057 3.22189 8.84689
147
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
148
+ 0 0 668660 0 0 0 0 0
149
+ 10000 17.20261 728763 19746 16663 20 693215.09 14.1672
150
+ 20000 39.991353 736277 20088 17114 15 713722.39 14.2538
151
+ 30000 62.543203 737819 20123 17069 19 721624.93 14.3215
152
+ 40000 84.73362 737091 20180 17112 16 725650.09 14.3401
153
+ 50000 107.36274 737323 20195 17077 9 727959.11 14.34102
154
+ 60000 129.92068 737956 20286 17181 12 729558.29 14.352533
155
+ 70000 152.51195 736419 20196 17162 11 730584.69 14.347729
156
+ 80000 175.05543 738056 20221 17147 16 731417.34 14.3606
157
+ 90000 197.16839 737292 20101 17049 14 732142.05 14.3627
158
+ 100000 220.10091 737513 20309 17214 13 732640.69 14.37531
159
+ 110000 242.11581 737187 20132 17074 22 733083.04 14.374618
160
+ 120000 264.72695 737485 20198 17080 15 733439.05 14.379642
161
+ 130000 287.40826 738310 20131 17029 13 733760.02 14.377446
162
+ 140000 310.05309 737277 20201 17137 16 734016.21 14.3831
163
+ 150000 332.30554 737567 20091 17033 8 734263.91 14.376833
164
+ 160000 355.33278 737104 20117 17110 15 734457.36 14.377769
165
+ 170000 376.81193 736913 20171 17059 12 734637.36 14.381012
166
+ 180000 399.85391 737158 20176 17016 14 734753.37 14.380911
167
+ 190000 422.15563 737171 20087 16994 13 734889.77 14.38
168
+ 200000 446.08469 737849 20183 17104 11 735017.13 14.38184
169
+ Loop time of 446.086 on 128 procs for 200000 steps with 737849 particles
170
+
171
+ MPI task timing breakdown:
172
+ Section | min time | avg time | max time |%varavg| %total
173
+ ---------------------------------------------------------------
174
+ Move | 0.022441 | 18.588 | 107.18 | 561.7 | 4.17
175
+ Coll | 0.48952 | 22.606 | 187.5 | 687.9 | 5.07
176
+ Sort | 0.16592 | 3.6205 | 30.868 | 278.5 | 0.81
177
+ Comm | 69.666 | 104.53 | 117.18 | 98.5 | 23.43
178
+ Modify | 0.020423 | 0.16085 | 2.8217 | 105.9 | 0.04
179
+ Output | 0.0057149 | 0.021778 | 0.024437 | 2.0 | 0.00
180
+ Other | | 296.6 | | | 66.48
181
+
182
+ Particle moves = 147003425989 (147B)
183
+ Cells touched = 153947814990 (154B)
184
+ Particle comms = 258004940 (258M)
185
+ Boundary collides = 0 (0K)
186
+ Boundary exits = 25909800 (25.9M)
187
+ SurfColl checks = 129606988 (130M)
188
+ SurfColl occurs = 2876368 (2.88M)
189
+ Surf reactions = 0 (0K)
190
+ Collide attempts = 4496940536 (4.5B)
191
+ Collide occurs = 3461152209 (3.46B)
192
+ Reactions = 0 (0K)
193
+ Particles stuck = 0
194
+ Axisymm bad moves = 0
195
+
196
+ Particle-moves/CPUsec/proc: 2.57453e+06
197
+ Particle-moves/step: 735017
198
+ Cell-touches/particle/step: 1.04724
199
+ Particle comm iterations/step: 1
200
+ Particle fraction communicated: 0.00175509
201
+ Particle fraction colliding with boundary: 0
202
+ Particle fraction exiting boundary: 0.000176253
203
+ Surface-checks/particle/step: 0.00088166
204
+ Surface-collisions/particle/step: 1.95667e-05
205
+ Surf-reactions/particle/step: 0
206
+ Collision-attempts/particle/step: 0.0305907
207
+ Collisions/particle/step: 0.0235447
208
+ Reactions/particle/step: 0
209
+
210
+ Particles: 5764.45 ave 36057 max 0 min
211
+ Histogram: 82 15 5 8 6 6 2 0 0 4
212
+ Cells: 762.5 ave 763 max 762 min
213
+ Histogram: 64 0 0 0 0 0 0 0 0 64
214
+ GhostCell: 19721.7 ave 36096 max 1099 min
215
+ Histogram: 20 10 12 10 18 2 6 4 16 30
216
+ EmptyCell: 0 ave 0 max 0 min
217
+ Histogram: 128 0 0 0 0 0 0 0 0 0
218
+ Surfs: 200 ave 200 max 200 min
219
+ Histogram: 128 0 0 0 0 0 0 0 0 0
220
+ GhostSurf: 0 ave 0 max 0 min
221
+ Histogram: 128 0 0 0 0 0 0 0 0 0
222
+ compute 1 grid all air n nrho massrho u v w
223
+ compute 2 grid all air trot
224
+ compute 3 tvib/grid all air
225
+ compute 4 thermal/grid all air press temp
226
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
227
+ fix 1 ave/grid all 1 10000 ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
228
+ fix 1 ave/grid all 1 10000 10000 c_1[*] c_4[*] c_2[*] c_3[*] ave running
229
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
230
+ dump 1 grid all 300000 output/grid.*.dat id xc yc f_1[*]
231
+ dump_modify 1 format float %12.5g
232
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
233
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
234
+ fix 2 ave/surf all 1 10000 ${nave} c_5[*] ave running
235
+ fix 2 ave/surf all 1 10000 10000 c_5[*] ave running
236
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
237
+ dump 2 surf all 300000 output/surf.*.dat id v1x v1y v2x v2y f_2[*]
238
+ dump_modify 2 format float %12.5g
239
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
240
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
241
+ dump 3 grid all 300000 output/lambdakn.*.dat id xc yc c_6[*]
242
+ dump_modify 3 format float %12.5g
243
+ run 100000
244
+ Memory usage per proc in Mbytes:
245
+ particles (ave,min,max) = 1.87207 0 5.0625
246
+ grid (ave,min,max) = 4.297 1.51379 7.13879
247
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
248
+ total (ave,min,max) = 6.4459 3.47786 9.10336
249
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
250
+ 200000 0 737849 20183 17104 0 0 0
251
+ 210000 38.956919 737217 20137 17071 13 737312.57 14.4271
252
+ 220000 78.802454 737820 20170 17039 11 737373.24 14.4
253
+ 230000 117.99125 737969 20241 17150 10 737499.14 14.4091
254
+ 240000 157.45034 737397 20186 17121 8 737626.65 14.40545
255
+ 250000 196.93135 737388 20254 17218 15 737600.33 14.39976
256
+ 260000 236.54049 736969 20159 17056 14 737554.62 14.4056
257
+ 270000 275.56824 737233 20171 17067 19 737547.14 14.4073
258
+ 280000 314.89555 737610 20270 17195 17 737474.98 14.411388
259
+ 290000 354.43646 737496 20000 16998 8 737435.85 14.406822
260
+ 300000 394.0034 736136 20169 17072 17 737426.59 14.41018
261
+ Loop time of 394.004 on 128 procs for 100000 steps with 736136 particles
262
+
263
+ MPI task timing breakdown:
264
+ Section | min time | avg time | max time |%varavg| %total
265
+ ---------------------------------------------------------------
266
+ Move | 0.011689 | 9.3818 | 54.165 | 397.0 | 2.38
267
+ Coll | 0.24711 | 11.534 | 100.13 | 499.7 | 2.93
268
+ Sort | 0.091394 | 1.8563 | 13.48 | 193.6 | 0.47
269
+ Comm | 49.467 | 74.132 | 85.553 | 98.0 | 18.82
270
+ Modify | 3.4777 | 25.535 | 140.25 | 601.1 | 6.48
271
+ Output | 0.053525 | 0.055061 | 0.058189 | 0.5 | 0.01
272
+ Other | | 271.5 | | | 68.91
273
+
274
+ Particle moves = 73742658917 (73.7B)
275
+ Cells touched = 77222968182 (77.2B)
276
+ Particle comms = 129299707 (129M)
277
+ Boundary collides = 0 (0K)
278
+ Boundary exits = 12993019 (13M)
279
+ SurfColl checks = 64866329 (64.9M)
280
+ SurfColl occurs = 1441018 (1.44M)
281
+ Surf reactions = 0 (0K)
282
+ Collide attempts = 2267436495 (2.27B)
283
+ Collide occurs = 1744985015 (1.74B)
284
+ Reactions = 0 (0K)
285
+ Particles stuck = 0
286
+ Axisymm bad moves = 0
287
+
288
+ Particle-moves/CPUsec/proc: 1.46221e+06
289
+ Particle-moves/step: 737427
290
+ Cell-touches/particle/step: 1.0472
291
+ Particle comm iterations/step: 1
292
+ Particle fraction communicated: 0.00175339
293
+ Particle fraction colliding with boundary: 0
294
+ Particle fraction exiting boundary: 0.000176194
295
+ Surface-checks/particle/step: 0.000879631
296
+ Surface-collisions/particle/step: 1.95412e-05
297
+ Surf-reactions/particle/step: 0
298
+ Collision-attempts/particle/step: 0.030748
299
+ Collisions/particle/step: 0.0236632
300
+ Reactions/particle/step: 0
301
+
302
+ Particles: 5751.06 ave 35971 max 0 min
303
+ Histogram: 83 15 4 8 6 6 2 0 0 4
304
+ Cells: 762.5 ave 763 max 762 min
305
+ Histogram: 64 0 0 0 0 0 0 0 0 64
306
+ GhostCell: 19721.7 ave 36096 max 1099 min
307
+ Histogram: 20 10 12 10 18 2 6 4 16 30
308
+ EmptyCell: 0 ave 0 max 0 min
309
+ Histogram: 128 0 0 0 0 0 0 0 0 0
310
+ Surfs: 200 ave 200 max 200 min
311
+ Histogram: 128 0 0 0 0 0 0 0 0 0
312
+ GhostSurf: 0 ave 0 max 0 min
313
+ Histogram: 128 0 0 0 0 0 0 0 0 0
314
+ write_grid grid/final-grid.dat
315
+ grid cells = 97600
316
+ CPU time = 0.0144484 secs
317
+ write_surf surf/geom.surf
318
+ surf elements = 200
319
+ CPU time = 0.0016271 secs
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/mir.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ dimension 2
2
+ create_box -5.0 6.0 -5.0 5.0 -0.5 0.5
3
+ read_grid grid/final-grid.dat
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/output/surf.300000.dat ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ITEM: TIMESTEP
2
+ 300000
3
+ ITEM: NUMBER OF SURFS
4
+ 200
5
+ ITEM: BOX BOUNDS oo oo pp
6
+ -5 6
7
+ -5 5
8
+ -0.5 0.5
9
+ ITEM: SURFS id v1x v1y v2x v2y f_2[1] f_2[2] f_2[3] f_2[4] f_2[5] f_2[6] f_2[7] f_2[8] f_2[9] f_2[10] f_2[11]
10
+ 1 0.5 0 0.49975 -0.015705 0.00461 0.076604 -0.076595 0.0012032 -4.9748e-06 -0.00031668 -0.0012032 1.3925e-05 6.0549 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.1204 2.0944 1.3095 -1.6345 0.28965 0.23205 0.025118 -0.022029 249.88 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.00486 0.081166 -0.081076 0.0038235 -1.937e-06 -4.1075e-05 -0.0012735 5.9411e-05 7.3594 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.1121 1.9533 1.1728 -1.562 0.27987 0.21013 0.022817 -0.021234 234.86 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00455 0.074292 -0.074063 0.0058289 -8.3673e-05 -0.0010632 -0.0011646 7.4857e-05 6.705 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.10775 1.867 1.0736 -1.5275 0.28235 0.19844 0.021298 -0.020876 218.26 0 0
16
+ 4 0.49778 -0.047054 0.49606 -0.062667 0.00505 0.085863 -0.085345 0.0094221 -0.00059967 -0.0054318 -0.00135 6.2678e-05 8.0459 0 0
17
+ 132 -0.28104 0.41354 -0.26791 0.42216 0.10195 1.7709 0.97227 -1.4801 0.28932 0.19005 0.019816 -0.020264 211.21 0 0
18
+ 5 0.49606 -0.062667 0.49384 -0.078217 0.00437 0.073175 -0.072445 0.01031 9.0067e-05 0.00063284 -0.0011365 0.00017189 5.3583 0 0
19
+ 133 -0.26791 0.42216 -0.25452 0.43037 0.09821 1.7071 0.89197 -1.4556 0.29488 0.1807 0.018642 -0.020025 208.31 0 0
20
+ 6 0.49384 -0.078217 0.49114 -0.093691 0.00414 0.0659 -0.064918 0.01133 3.7581e-05 0.00021533 -0.0010191 0.00018135 5.0924 0 0
21
+ 134 -0.25452 0.43037 -0.24088 0.43815 0.09219 1.5953 0.79042 -1.3858 0.28947 0.16511 0.016962 -0.019173 191.46 0 0
22
+ 7 0.49114 -0.093691 0.48796 -0.10907 0.00466 0.076298 -0.074713 0.015472 0.00029769 0.0014375 -0.0011689 0.00026561 5.3816 0 0
23
+ 135 -0.24088 0.43815 -0.227 0.4455 0.08839 1.53 0.71595 -1.3522 0.30329 0.16058 0.016009 -0.018717 185.74 0 0
24
+ 8 0.48796 -0.10907 0.48429 -0.12434 0.00423 0.071809 -0.069825 0.016763 -0.00023422 -0.00097558 -0.0011004 0.00024798 6.0016 0 0
25
+ 136 -0.227 0.4455 -0.21289 0.45241 0.08335 1.4479 0.63697 -1.3002 0.28938 0.14176 0.01455 -0.018196 178.67 0 0
26
+ 9 0.48429 -0.12434 0.48015 -0.1395 0.00409 0.064835 -0.062537 0.017108 0.00045508 0.0016635 -0.00097514 0.00029485 4.5284 0 0
27
+ 137 -0.21289 0.45241 -0.19857 0.45888 0.07992 1.3765 0.56644 -1.2545 0.28642 0.12932 0.013396 -0.017674 162.48 0 0
28
+ 10 0.48015 -0.1395 0.47553 -0.15451 0.00405 0.063171 -0.060379 0.018575 0.0009711 0.0031566 -0.00093314 0.00034134 5.9553 0 0
29
+ 138 -0.19857 0.45888 -0.18406 0.46489 0.07455 1.2729 0.48712 -1.176 0.29229 0.12107 0.012242 -0.01657 157.27 0 0
30
+ 11 0.47553 -0.15451 0.47044 -0.16937 0.00362 0.058251 -0.05511 0.018869 2.6064e-05 7.6127e-05 -0.00086523 0.00029757 5.0715 0 0
31
+ 139 -0.18406 0.46489 -0.16937 0.47044 0.07013 1.2046 0.42581 -1.1269 0.27817 0.10511 0.011058 -0.016049 144.51 0 0
32
+ 12 0.47044 -0.16937 0.46489 -0.18406 0.00366 0.058233 -0.054474 0.020584 0.0014982 0.0039648 -0.00083211 0.0003856 6.621 0 0
33
+ 140 -0.16937 0.47044 -0.15451 0.47553 0.06571 1.1296 0.3659 -1.0687 0.28644 0.098072 0.010247 -0.015246 140.13 0 0
34
+ 13 0.46489 -0.18406 0.45888 -0.19857 0.00364 0.056165 -0.051889 0.021493 0.0016937 0.004089 -0.00078844 0.00040183 5.1659 0 0
35
+ 141 -0.15451 0.47553 -0.1395 0.48015 0.06143 1.0437 0.30688 -0.99754 0.26798 0.082443 0.0090296 -0.014374 134.97 0 0
36
+ 14 0.45888 -0.19857 0.45241 -0.21289 0.00347 0.052658 -0.047993 0.02167 0.0019635 0.0043488 -0.000723 0.00040868 4.2706 0 0
37
+ 142 -0.1395 0.48015 -0.12434 0.48429 0.05901 0.99895 0.26359 -0.96354 0.27932 0.076413 0.0085277 -0.013934 125.26 0 0
38
+ 15 0.45241 -0.21289 0.4455 -0.227 0.00355 0.05758 -0.051709 0.025332 0.0017547 0.0035818 -0.00078464 0.00045415 4.7499 0 0
39
+ 143 -0.12434 0.48429 -0.10907 0.48796 0.0551 0.9319 0.21755 -0.90616 0.27556 0.066155 0.0077453 -0.013194 119.86 0 0
40
+ 16 0.4455 -0.227 0.43815 -0.24088 0.00343 0.05085 -0.04494 0.023794 0.0032918 0.0062172 -0.00065418 0.0004714 5.154 0 0
41
+ 144 -0.10907 0.48796 -0.093691 0.49114 0.0512 0.87405 0.17725 -0.85589 0.25945 0.053729 0.0068593 -0.0126 114.25 0 0
42
+ 17 0.43815 -0.24088 0.43037 -0.25452 0.0031 0.047971 -0.041669 0.023767 0.0029236 0.0051255 -0.00060858 0.00045383 4.4609 0 0
43
+ 145 -0.093691 0.49114 -0.078217 0.49384 0.04721 0.8035 0.13815 -0.79154 0.24523 0.0428 0.0060218 -0.011761 102.75 0 0
44
+ 18 0.43037 -0.25452 0.42216 -0.26791 0.00333 0.049761 -0.042428 0.026 0.0059153 0.0096529 -0.00057351 0.00056001 5.039 0 0
45
+ 146 -0.078217 0.49384 -0.062667 0.49606 0.04477 0.75465 0.10633 -0.74712 0.25043 0.035641 0.0056037 -0.011175 94.087 0 0
46
+ 19 0.42216 -0.26791 0.41354 -0.28104 0.00333 0.049031 -0.040981 0.026919 0.0057161 0.0087019 -0.00055391 0.00055951 4.6389 0 0
47
+ 147 -0.062667 0.49606 -0.047054 0.49778 0.04125 0.69675 0.076457 -0.69254 0.23732 0.0262 0.0049286 -0.010466 88.801 0 0
48
+ 20 0.41354 -0.28104 0.40451 -0.29389 0.00303 0.045744 -0.037426 0.026303 0.0079517 0.011314 -0.00046296 0.00059087 4.8342 0 0
49
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+ 89 -0.46489 -0.18406 -0.47044 -0.16937 0.2006 3.5326 3.3046 1.2487 0.074082 -0.19605 0.05307 0.016534 394.43 0 0
171
+ 90 -0.47044 -0.16937 -0.47553 -0.15451 0.20826 3.6373 3.4412 1.1782 0.071241 -0.20808 0.055171 0.015238 404.7 0 0
172
+ 91 -0.47553 -0.15451 -0.48015 -0.1395 0.20711 3.6425 3.4814 1.071 0.054211 -0.17621 0.055536 0.014055 411.03 0 0
173
+ 92 -0.48015 -0.1395 -0.48429 -0.12434 0.21256 3.724 3.592 0.98265 0.044697 -0.16339 0.057122 0.012869 418.62 0 0
174
+ 93 -0.48429 -0.12434 -0.48796 -0.10907 0.2153 3.764 3.66 0.87868 0.029406 -0.12248 0.05795 0.011878 411.04 0 0
175
+ 94 -0.48796 -0.10907 -0.49114 -0.093691 0.21673 3.7856 3.7069 0.76767 0.025395 -0.12263 0.058625 0.010132 429.3 0 0
176
+ 95 -0.49114 -0.093691 -0.49384 -0.078217 0.21868 3.8202 3.7633 0.6568 0.023025 -0.13193 0.059473 0.0082444 436.34 0 0
177
+ 96 -0.49384 -0.078217 -0.49606 -0.062667 0.22133 3.8785 3.8398 0.54649 0.012738 -0.089504 0.060514 0.007178 425.78 0 0
178
+ 97 -0.49606 -0.062667 -0.49778 -0.047054 0.22239 3.8904 3.8669 0.42692 0.0088067 -0.07977 0.060878 0.0054527 437.7 0 0
179
+ 98 -0.49778 -0.047054 -0.49901 -0.031395 0.22107 3.8813 3.8693 0.30452 0.0048646 -0.061811 0.060853 0.0038124 443.27 0 0
180
+ 99 -0.49901 -0.031395 -0.49975 -0.015705 0.22397 3.9057 3.9014 0.18398 0.0012139 -0.02574 0.061299 0.0024856 433.81 0 0
181
+ 100 -0.49975 -0.015705 -0.5 0 0.22694 3.9466 3.9461 0.061991 0.00019462 -0.012389 0.061986 0.00077911 430.14 0 0
182
+ 101 -0.5 0 -0.49975 0.015705 0.22524 3.9371 3.9366 -0.061841 0.00019485 0.012404 0.061837 -0.00077653 432.58 0 0
183
+ 102 -0.49975 0.015705 -0.49901 0.031395 0.22448 3.9334 3.929 -0.18529 0.0017644 0.037415 0.061742 -0.0023227 438.88 0 0
184
+ 103 -0.49901 0.031395 -0.49778 0.047054 0.22345 3.8896 3.8776 -0.30517 0.005042 0.064064 0.060985 -0.0037871 434.79 0 0
185
+ 104 -0.49778 0.047054 -0.49606 0.062667 0.22474 3.9078 3.8842 -0.42882 0.00981 0.088858 0.061165 -0.0053399 422.51 0 0
186
+ 105 -0.49606 0.062667 -0.49384 0.078217 0.22362 3.8962 3.8573 -0.54897 0.0082476 0.057951 0.060717 -0.0077126 415.29 0 0
187
+ 106 -0.49384 0.078217 -0.49114 0.093691 0.22065 3.8505 3.7932 -0.66202 0.022926 0.13136 0.059941 -0.0083352 433.17 0 0
188
+ 107 -0.49114 0.093691 -0.48796 0.10907 0.21751 3.8059 3.7269 -0.77179 0.027117 0.13094 0.058965 -0.010066 415.96 0 0
189
+ 108 -0.48796 0.10907 -0.48429 0.12434 0.21641 3.7666 3.6626 -0.8793 0.032214 0.13418 0.058035 -0.011704 405.1 0 0
190
+ 109 -0.48429 0.12434 -0.48015 0.1395 0.21303 3.7245 3.5925 -0.98279 0.050369 0.18412 0.057219 -0.012545 412.22 0 0
191
+ 110 -0.48015 0.1395 -0.47553 0.15451 0.21087 3.6833 3.5205 -1.083 0.05503 0.17888 0.056162 -0.014202 420.69 0 0
192
+ 111 -0.47553 0.15451 -0.47044 0.16937 0.20542 3.6139 3.419 -1.1706 0.06193 0.18088 0.054676 -0.015546 407.2 0 0
193
+ 112 -0.47044 0.16937 -0.46489 0.18406 0.20256 3.5544 3.3249 -1.2564 0.078585 0.20797 0.05346 -0.016468 394.57 0 0
194
+ 113 -0.46489 0.18406 -0.45888 0.19857 0.20042 3.5116 3.2443 -1.3438 0.08532 0.20598 0.0523 -0.017873 396.97 0 0
195
+ 114 -0.45888 0.19857 -0.45241 0.21289 0.19483 3.4042 3.1026 -1.4009 0.09971 0.22083 0.0503 -0.018536 382.41 0 0
196
+ 115 -0.45241 0.21289 -0.4455 0.227 0.19247 3.35 3.0084 -1.4738 0.13498 0.27554 0.049374 -0.018822 378.02 0 0
197
+ 116 -0.4455 0.227 -0.43815 0.24088 0.18902 3.2916 2.909 -1.5402 0.12879 0.24325 0.047716 -0.020372 367.83 0 0
198
+ 117 -0.43815 0.24088 -0.43037 0.25452 0.18188 3.1879 2.7691 -1.5795 0.14169 0.24842 0.045721 -0.020907 355.49 0 0
199
+ 118 -0.43037 0.25452 -0.42216 0.26791 0.17648 3.08 2.6262 -1.6093 0.16335 0.26657 0.043816 -0.021091 350.8 0 0
200
+ 119 -0.42216 0.26791 -0.41354 0.28104 0.17215 2.9986 2.5063 -1.6463 0.16668 0.25375 0.041985 -0.021874 336.03 0 0
201
+ 120 -0.41354 0.28104 -0.40451 0.29389 0.16671 2.8902 2.3646 -1.6619 0.18187 0.25878 0.039999 -0.022039 320.39 0 0
202
+ 121 -0.40451 0.29389 -0.39508 0.30645 0.16002 2.8013 2.2402 -1.682 0.19713 0.26256 0.038283 -0.022295 322.11 0 0
203
+ 122 -0.39508 0.30645 -0.38526 0.31871 0.15556 2.7181 2.1213 -1.6995 0.20632 0.25753 0.036561 -0.022649 311.19 0 0
204
+ 123 -0.38526 0.31871 -0.37506 0.33066 0.14999 2.6149 1.9884 -1.6982 0.20539 0.24048 0.034458 -0.022897 295.3 0 0
205
+ 124 -0.37506 0.33066 -0.36448 0.34227 0.14403 2.5199 1.8638 -1.6959 0.23029 0.25309 0.032892 -0.022663 291.35 0 0
206
+ 125 -0.36448 0.34227 -0.35355 0.35355 0.13971 2.4449 1.7557 -1.7014 0.23717 0.24474 0.031303 -0.022881 292.73 0 0
207
+ 126 -0.35355 0.35355 -0.34227 0.36448 0.13539 2.3722 1.6509 -1.7036 0.25783 0.24986 0.029981 -0.022834 277.85 0 0
208
+ 127 -0.34227 0.36448 -0.33066 0.37506 0.12833 2.225 1.4975 -1.6457 0.26194 0.23835 0.027636 -0.022106 258.86 0 0
209
+ 128 -0.33066 0.37506 -0.31871 0.38526 0.12394 2.1571 1.4009 -1.6403 0.25779 0.22017 0.026054 -0.022306 249.02 0 0
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/runmpirun128.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 2
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 128 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/runmpirun64.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 1
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 64 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/slurm-28429213.out ADDED
@@ -0,0 +1,205 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ Running on 128 MPI task(s)
4
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
5
+ Created 97600 child grid cells
6
+ CPU time = 0.146769 secs
7
+ create/ghost percent = 0.855346 99.1447
8
+ Balance grid migrated 97380 cells
9
+ CPU time = 0.108424 secs
10
+ reassign/sort/migrate/ghost percent = 51.4419 0.0368276 7.9095 40.6118
11
+ Reading surface file ...
12
+ 200 lines
13
+ -0.5 0.5 xlo xhi
14
+ -0.5 0.5 ylo yhi
15
+ 0 0 zlo zhi
16
+ 0.0157073 min line length
17
+ 456 0 = cells overlapping surfs, overlap cells with unmarked corner pts
18
+ 87188 9956 456 = cells outside/inside/overlapping surfs
19
+ 456 = surf cells with 1,2,etc splits
20
+ 109.214731023047 109.214731023047 = cell-wise and global flow volume
21
+ CPU time = 0.116235 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 21.1817 18.9324 2.27225 20.5552 37.0585 5.55688 0.162444
23
+ surf2grid time = 0.0238924 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 15.1969 23.8279 5.76592 1.98564 11.5168 6.36
25
+ Balance grid migrated 1938 cells
26
+ CPU time = 0.0521031 secs
27
+ reassign/sort/migrate/ghost percent = 6.23771 0.0433563 16.842 76.877
28
+ Creating particles ...
29
+ WARNING: Created unexpected # of particles: 668660 versus 668661 (../create_particles.cpp:346)
30
+ Created 668660 particles
31
+ CPU time = 0.0163372 secs
32
+ Memory usage per proc in Mbytes:
33
+ particles (ave,min,max) = 1.79297 0 3.375
34
+ grid (ave,min,max) = 4.297 1.51379 7.13879
35
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
36
+ total (ave,min,max) = 6.11057 3.22189 8.84689
37
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
38
+ 0 0 668660 0 0 0 0 0
39
+ 10000 17.20261 728763 19746 16663 20 693215.09 14.1672
40
+ 20000 39.991353 736277 20088 17114 15 713722.39 14.2538
41
+ 30000 62.543203 737819 20123 17069 19 721624.93 14.3215
42
+ 40000 84.73362 737091 20180 17112 16 725650.09 14.3401
43
+ 50000 107.36274 737323 20195 17077 9 727959.11 14.34102
44
+ 60000 129.92068 737956 20286 17181 12 729558.29 14.352533
45
+ 70000 152.51195 736419 20196 17162 11 730584.69 14.347729
46
+ 80000 175.05543 738056 20221 17147 16 731417.34 14.3606
47
+ 90000 197.16839 737292 20101 17049 14 732142.05 14.3627
48
+ 100000 220.10091 737513 20309 17214 13 732640.69 14.37531
49
+ 110000 242.11581 737187 20132 17074 22 733083.04 14.374618
50
+ 120000 264.72695 737485 20198 17080 15 733439.05 14.379642
51
+ 130000 287.40826 738310 20131 17029 13 733760.02 14.377446
52
+ 140000 310.05309 737277 20201 17137 16 734016.21 14.3831
53
+ 150000 332.30554 737567 20091 17033 8 734263.91 14.376833
54
+ 160000 355.33278 737104 20117 17110 15 734457.36 14.377769
55
+ 170000 376.81193 736913 20171 17059 12 734637.36 14.381012
56
+ 180000 399.85391 737158 20176 17016 14 734753.37 14.380911
57
+ 190000 422.15563 737171 20087 16994 13 734889.77 14.38
58
+ 200000 446.08469 737849 20183 17104 11 735017.13 14.38184
59
+ Loop time of 446.086 on 128 procs for 200000 steps with 737849 particles
60
+
61
+ MPI task timing breakdown:
62
+ Section | min time | avg time | max time |%varavg| %total
63
+ ---------------------------------------------------------------
64
+ Move | 0.022441 | 18.588 | 107.18 | 561.7 | 4.17
65
+ Coll | 0.48952 | 22.606 | 187.5 | 687.9 | 5.07
66
+ Sort | 0.16592 | 3.6205 | 30.868 | 278.5 | 0.81
67
+ Comm | 69.666 | 104.53 | 117.18 | 98.5 | 23.43
68
+ Modify | 0.020423 | 0.16085 | 2.8217 | 105.9 | 0.04
69
+ Output | 0.0057149 | 0.021778 | 0.024437 | 2.0 | 0.00
70
+ Other | | 296.6 | | | 66.48
71
+
72
+ Particle moves = 147003425989 (147B)
73
+ Cells touched = 153947814990 (154B)
74
+ Particle comms = 258004940 (258M)
75
+ Boundary collides = 0 (0K)
76
+ Boundary exits = 25909800 (25.9M)
77
+ SurfColl checks = 129606988 (130M)
78
+ SurfColl occurs = 2876368 (2.88M)
79
+ Surf reactions = 0 (0K)
80
+ Collide attempts = 4496940536 (4.5B)
81
+ Collide occurs = 3461152209 (3.46B)
82
+ Gas reactions = 0 (0K)
83
+ Particles stuck = 0
84
+ Axisymm bad moves = 0
85
+
86
+ Particle-moves/CPUsec/proc: 2.57453e+06
87
+ Particle-moves/step: 735017
88
+ Cell-touches/particle/step: 1.04724
89
+ Particle comm iterations/step: 1
90
+ Particle fraction communicated: 0.00175509
91
+ Particle fraction colliding with boundary: 0
92
+ Particle fraction exiting boundary: 0.000176253
93
+ Surface-checks/particle/step: 0.00088166
94
+ Surface-collisions/particle/step: 1.95667e-05
95
+ Surface-reactions/particle/step: 0
96
+ Collision-attempts/particle/step: 0.0305907
97
+ Collisions/particle/step: 0.0235447
98
+ Gas-reactions/particle/step: 0
99
+
100
+ Particles: 5764.45 ave 36057 max 0 min
101
+ Histogram: 82 15 5 8 6 6 2 0 0 4
102
+ Cells: 762.5 ave 763 max 762 min
103
+ Histogram: 64 0 0 0 0 0 0 0 0 64
104
+ GhostCell: 19721.7 ave 36096 max 1099 min
105
+ Histogram: 20 10 12 10 18 2 6 4 16 30
106
+ EmptyCell: 0 ave 0 max 0 min
107
+ Histogram: 128 0 0 0 0 0 0 0 0 0
108
+ Surfs: 200 ave 200 max 200 min
109
+ Histogram: 128 0 0 0 0 0 0 0 0 0
110
+ GhostSurf: 0 ave 0 max 0 min
111
+ Histogram: 128 0 0 0 0 0 0 0 0 0
112
+ Memory usage per proc in Mbytes:
113
+ particles (ave,min,max) = 1.87207 0 5.0625
114
+ grid (ave,min,max) = 4.297 1.51379 7.13879
115
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
116
+ total (ave,min,max) = 6.4459 3.47786 9.10336
117
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
118
+ 200000 0 737849 20183 17104 0 0 0
119
+ 210000 38.956919 737217 20137 17071 13 737312.57 14.4271
120
+ 220000 78.802454 737820 20170 17039 11 737373.24 14.4
121
+ 230000 117.99125 737969 20241 17150 10 737499.14 14.4091
122
+ 240000 157.45034 737397 20186 17121 8 737626.65 14.40545
123
+ 250000 196.93135 737388 20254 17218 15 737600.33 14.39976
124
+ 260000 236.54049 736969 20159 17056 14 737554.62 14.4056
125
+ 270000 275.56824 737233 20171 17067 19 737547.14 14.4073
126
+ 280000 314.89555 737610 20270 17195 17 737474.98 14.411388
127
+ 290000 354.43646 737496 20000 16998 8 737435.85 14.406822
128
+ 300000 394.0034 736136 20169 17072 17 737426.59 14.41018
129
+ Loop time of 394.004 on 128 procs for 100000 steps with 736136 particles
130
+
131
+ MPI task timing breakdown:
132
+ Section | min time | avg time | max time |%varavg| %total
133
+ ---------------------------------------------------------------
134
+ Move | 0.011689 | 9.3818 | 54.165 | 397.0 | 2.38
135
+ Coll | 0.24711 | 11.534 | 100.13 | 499.7 | 2.93
136
+ Sort | 0.091394 | 1.8563 | 13.48 | 193.6 | 0.47
137
+ Comm | 49.467 | 74.132 | 85.553 | 98.0 | 18.82
138
+ Modify | 3.4777 | 25.535 | 140.25 | 601.1 | 6.48
139
+ Output | 0.053525 | 0.055061 | 0.058189 | 0.5 | 0.01
140
+ Other | | 271.5 | | | 68.91
141
+
142
+ Particle moves = 73742658917 (73.7B)
143
+ Cells touched = 77222968182 (77.2B)
144
+ Particle comms = 129299707 (129M)
145
+ Boundary collides = 0 (0K)
146
+ Boundary exits = 12993019 (13M)
147
+ SurfColl checks = 64866329 (64.9M)
148
+ SurfColl occurs = 1441018 (1.44M)
149
+ Surf reactions = 0 (0K)
150
+ Collide attempts = 2267436495 (2.27B)
151
+ Collide occurs = 1744985015 (1.74B)
152
+ Gas reactions = 0 (0K)
153
+ Particles stuck = 0
154
+ Axisymm bad moves = 0
155
+
156
+ Particle-moves/CPUsec/proc: 1.46221e+06
157
+ Particle-moves/step: 737427
158
+ Cell-touches/particle/step: 1.0472
159
+ Particle comm iterations/step: 1
160
+ Particle fraction communicated: 0.00175339
161
+ Particle fraction colliding with boundary: 0
162
+ Particle fraction exiting boundary: 0.000176194
163
+ Surface-checks/particle/step: 0.000879631
164
+ Surface-collisions/particle/step: 1.95412e-05
165
+ Surface-reactions/particle/step: 0
166
+ Collision-attempts/particle/step: 0.030748
167
+ Collisions/particle/step: 0.0236632
168
+ Gas-reactions/particle/step: 0
169
+
170
+ Particles: 5751.06 ave 35971 max 0 min
171
+ Histogram: 83 15 4 8 6 6 2 0 0 4
172
+ Cells: 762.5 ave 763 max 762 min
173
+ Histogram: 64 0 0 0 0 0 0 0 0 64
174
+ GhostCell: 19721.7 ave 36096 max 1099 min
175
+ Histogram: 20 10 12 10 18 2 6 4 16 30
176
+ EmptyCell: 0 ave 0 max 0 min
177
+ Histogram: 128 0 0 0 0 0 0 0 0 0
178
+ Surfs: 200 ave 200 max 200 min
179
+ Histogram: 128 0 0 0 0 0 0 0 0 0
180
+ GhostSurf: 0 ave 0 max 0 min
181
+ Histogram: 128 0 0 0 0 0 0 0 0 0
182
+ Writing grid file ...
183
+ grid cells = 97600
184
+ CPU time = 0.0144484 secs
185
+ Writing surface file ...
186
+ surf elements = 200
187
+ CPU time = 0.0016271 secs
188
+ Processing 4 grid chunk(s).
189
+ Completed grid chunk number: 3
190
+ Completed grid chunk number: 2
191
+ Completed grid chunk number: 1
192
+ Completed grid chunk number: 0
193
+
194
+ Done in 0:00:13 (wall clock time)
195
+
196
+ Processing 4 grid chunk(s).
197
+ Completed grid chunk number: 3
198
+ Completed grid chunk number: 2
199
+ Completed grid chunk number: 1
200
+ Completed grid chunk number: 0
201
+
202
+ Done in 0:00:12 (wall clock time)
203
+
204
+ Processing SPARTA surface information.
205
+ Processing dump result file: ['./output/surf.300000.dat']
CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/surf/Surf_300000.vtu ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
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+ <VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
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+ <UnstructuredGrid>
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+ <Piece NumberOfPoints="400" NumberOfCells="200" >
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+ <PointData>
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+ </PointData>
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+ <CellData>
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+ <DataArray type="Float64" Name="id" format="appended" RangeMin="1" RangeMax="200" offset="0" />
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+ <DataArray type="Float64" Name="v1x" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="432" />
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+ <DataArray type="Float64" Name="v1y" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="1468" />
11
+ <DataArray type="Float64" Name="v2x" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="2476" />
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+ <DataArray type="Float64" Name="v2y" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="3508" />
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+ <DataArray type="Float64" Name="f_2[1]" format="appended" RangeMin="0.00282" RangeMax="0.22694" offset="4512" />
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+ <DataArray type="Float64" Name="f_2[2]" format="appended" RangeMin="0.041409" RangeMax="3.9466" offset="6488" />
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+ <DataArray type="Float64" Name="f_2[3]" format="appended" RangeMin="-0.085345" RangeMax="3.9461" offset="8520" />
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+ <DataArray type="Float64" Name="f_2[4]" format="appended" RangeMin="-1.7036" RangeMax="1.7258" offset="10620" />
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+ <DataArray type="Float64" Name="f_2[5]" format="appended" RangeMin="-0.00059967" RangeMax="0.30486" offset="12692" />
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+ <DataArray type="Float64" Name="f_2[6]" format="appended" RangeMin="-0.27347" RangeMax="0.27554" offset="14808" />
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+ <DataArray type="Float64" Name="f_2[7]" format="appended" RangeMin="-0.00135" RangeMax="0.061986" offset="16908" />
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+ <DataArray type="Float64" Name="f_2[8]" format="appended" RangeMin="-0.022897" RangeMax="0.023263" offset="19052" />
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+ <DataArray type="Float64" Name="f_2[9]" format="appended" RangeMin="3.4766" RangeMax="443.27" offset="21188" />
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+ <DataArray type="Float64" Name="f_2[10]" format="appended" RangeMin="0" RangeMax="0" offset="22728" />
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+ <DataArray type="Float64" Name="f_2[11]" format="appended" RangeMin="0" RangeMax="0" offset="22780" />
24
+ </CellData>
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+ <Points>
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+ <DataArray type="Float32" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0.49999998503" RangeMax="0.50000001255" offset="22832" />
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+ </Points>
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+ <Cells>
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+ <DataArray type="Int64" Name="connectivity" format="appended" RangeMin="" RangeMax="" offset="24160" />
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+ <DataArray type="Int64" Name="offsets" format="appended" RangeMin="" RangeMax="" offset="25064" />
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+ <DataArray type="UInt8" Name="types" format="appended" RangeMin="" RangeMax="" offset="25556" />
32
+ </Cells>
33
+ </Piece>
34
+ </UnstructuredGrid>
35
+ <AppendedData encoding="base64">
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AQAAAACAAABABgAA4gUAAA==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AQAAAACAAABABgAAAAYAAA==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AQAAAACAAABABgAAFAYAAA==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AQAAAACAAABABgAANQYAAA==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AQAAAACAAABABgAALgYAAA==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AQAAAACAAABABgAAcAQAAA==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AQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAADAEgAA0gMAAA==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AQAAAACAAABABgAAXwEAAA==eJwtxVFEHAAAANBKkpzkJEmSJMlJkiRJkiRJkiQnSXKSJEmSJEmSJEmSZGYmmUmSJEmSJEkyMzOTzCRJkmT20Xs/LybqXazjHO8EB5zoJAed7BSnOs3pznCms5ztHOc6zyHnu8CFLnKxS1zqMpe7wpWucrVrXOs617vBjW5ys1vc6rDb3O4Od7rLEXe7x73uc78HPOghD3vEox7zuCc86SlPe8aznvO8F7zoJS97xate8wd/9Cd/9ro3/MVfvektb3vHu97zvg986CMf+8SnPvO5L3zpK1/7m7/7h3/6l3/7xrf+47++870f/OgnP/vFr37zP0dFvxfjWMc53gkOONFJDjrZKU51mtOd4UxnOds5znWeQ853gQtd5GKXuNRlLneFK13late41nWud4Mb3eRmt7jVYbe53R3udJcj7naPe93nfg940EMe9ohHPeZxT3jSU572jGc95/9sSVRSAQAAAACAAADIAAAADAAAAA==eJxjZh4eAADsVAJZ
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38
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AQAAAACAAAAoUwAAWwIAAA==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AQAAAACAAAAoUwAA/DUAAA==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AgAAAACAAACMAgAAZRsAABIBAAA=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AQAAAACAAAAoUwAAzQ0AAA==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AQAAAACAAABlCgAAGgAAAA==eJztwSEBAAAAwyC9/oVf4hIoAAA4G4r2XY4=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/Surf.pvd ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="Collection" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Surf/Surf_300000.vtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/config.txt ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ variable BZ equal 1.380649E-23
2
+ variable PI equal 3.1415926536
3
+ variable molecular_mass equal 6.63E-26
4
+ variable molecular_diameter equal 4.17E-10
5
+ variable gamma equal 1.666666667
6
+ variable R equal ${BZ}/${molecular_mass}
7
+ variable L equal 1.0
8
+ variable xmin equal -5.0
9
+ variable xmax equal 6.0
10
+ variable ymin equal -5.0
11
+ variable ymax equal 5.0
12
+ variable Kn equal 0.28
13
+ variable Ma equal 3.3
14
+ variable To equal 273.0
15
+ variable Tb equal 273.0
16
+ variable theta equal 0.0
17
+ variable mfp equal ${Kn}*${L}
18
+ variable cpmfp equal 4
19
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
20
+ variable sos equal sqrt(${gamma}*${R}*${To})
21
+ variable vo equal ${Ma}*${sos}
22
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
23
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
24
+ variable ppc equal 30
25
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
26
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
27
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
28
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
29
+ variable nave equal 10000
30
+ variable noutput equal 300000
31
+ clear
32
+ shell mkdir output
33
+ shell mkdir grid
34
+ shell mkdir surf
35
+ seed 7
36
+ dimension 2
37
+ global nrho ${nden}
38
+ global fnum ${Fnum}
39
+ timestep ${tstep}
40
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
41
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
42
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
43
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
44
+ boundary o o p
45
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
46
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
47
+ balance_grid rcb cell
48
+ species gas.species Ar
49
+ mixture air Ar frac 1.0
50
+ mixture air Ar vstream ${vx} ${vy} 0.0 temp ${To}
51
+ collide vss air gas.vhs
52
+ collide_modify vremax 100 yes remain no nearcp yes 10
53
+ read_surf cylinder.surf
54
+ surf_collide 1 diffuse 273.0 1.0
55
+ surf_modify all collide 1
56
+ fix in emit/face air xlo ylo yhi
57
+ balance_grid rcb cell
58
+ create_particles air n 0
59
+ stats 10000
60
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
61
+ run 200000
62
+ compute 1 grid all air n nrho massrho u v w
63
+ compute 2 grid all air trot
64
+ compute 3 tvib/grid all air
65
+ compute 4 thermal/grid all air press temp
66
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
67
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
68
+ dump_modify 1 format float %12.5g
69
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
70
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
71
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
72
+ dump_modify 2 format float %12.5g
73
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
74
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
75
+ dump_modify 3 format float %12.5g
76
+ run 100000
77
+ write_grid grid/final-grid.dat
78
+ write_surf surf/geom.surf
CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/cylinder.surf ADDED
@@ -0,0 +1,410 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ surf file from Pointwise
2
+
3
+ 200 points
4
+ 200 lines
5
+
6
+ Points
7
+
8
+ 1 0.5 0.0
9
+ 2 0.499753280182866E+000 -0.157053795390643E-001
10
+ 3 0.499013364214136E+000 -0.313952597646568E-001
11
+ 4 0.497780982301540E+000 -0.470541566592573E-001
12
+ 5 0.496057350657239E+000 -0.626666167821523E-001
13
+ 6 0.493844170297569E+000 -0.782172325201156E-001
14
+ 7 0.491143625364344E+000 -0.936906572928624E-001
15
+ 8 0.487958380969374E+000 -0.109071620698271E+000
16
+ 9 0.484291580564315E+000 -0.124344943582427E+000
17
+ 10 0.480146842838471E+000 -0.139495553019615E+000
18
+ 11 0.475528258147577E+000 -0.154508497187474E+000
19
+ 12 0.470440384477113E+000 -0.169368960122646E+000
20
+ 13 0.464888242944126E+000 -0.184062276342339E+000
21
+ 14 0.458877312841991E+000 -0.198573945317390E+000
22
+ 15 0.452413526233010E+000 -0.212889645782536E+000
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+ 16 0.445503262094184E+000 -0.226995249869774E+000
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+ 17 0.438153340021932E+000 -0.240876837050858E+000
25
+ 18 0.430371013501972E+000 -0.254520707875186E+000
26
+ 19 0.422163962751007E+000 -0.267913397489498E+000
27
+ 20 0.413540287137281E+000 -0.281041688926065E+000
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+ 21 0.404508497187474E+000 -0.293892626146237E+000
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+ 22 0.395077506187845E+000 -0.306453526826488E+000
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+ 23 0.385256621387895E+000 -0.318711994874345E+000
31
+ 24 0.375055534815230E+000 -0.330655932661826E+000
32
+ 25 0.364484313710706E+000 -0.342273552964344E+000
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+ 26 0.353553390593274E+000 -0.353553390593274E+000
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+ 27 0.342273552964344E+000 -0.364484313710706E+000
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+ 28 0.330655932661826E+000 -0.375055534815230E+000
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+ 29 0.318711994874345E+000 -0.385256621387895E+000
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+ 30 0.306453526826488E+000 -0.395077506187845E+000
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+ 31 0.293892626146236E+000 -0.404508497187474E+000
39
+ 32 0.281041688926065E+000 -0.413540287137281E+000
40
+ 33 0.267913397489498E+000 -0.422163962751008E+000
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+ 34 0.254520707875185E+000 -0.430371013501972E+000
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+ 35 0.240876837050857E+000 -0.438153340021932E+000
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+ 36 0.226995249869773E+000 -0.445503262094184E+000
44
+ 37 0.212889645782536E+000 -0.452413526233010E+000
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+ 38 0.198573945317390E+000 -0.458877312841991E+000
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+ 39 0.184062276342339E+000 -0.464888242944126E+000
47
+ 40 0.169368960122645E+000 -0.470440384477113E+000
48
+ 41 0.154508497187474E+000 -0.475528258147577E+000
49
+ 42 0.139495553019614E+000 -0.480146842838472E+000
50
+ 43 0.124344943582427E+000 -0.484291580564316E+000
51
+ 44 0.109071620698271E+000 -0.487958380969374E+000
52
+ 45 0.936906572928621E-001 -0.491143625364344E+000
53
+ 46 0.782172325201152E-001 -0.493844170297569E+000
54
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1
+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ variable BZ equal 1.380649E-23
4
+ variable PI equal 3.1415926536
5
+ variable molecular_mass equal 6.63E-26
6
+ variable molecular_diameter equal 4.17E-10
7
+ variable gamma equal 1.666666667
8
+ variable R equal ${BZ}/${molecular_mass}
9
+ variable R equal 1.380649e-23/${molecular_mass}
10
+ variable R equal 1.380649e-23/6.63e-26
11
+ variable L equal 1.0
12
+ variable xmin equal -5.0
13
+ variable xmax equal 6.0
14
+ variable ymin equal -5.0
15
+ variable ymax equal 5.0
16
+ variable Kn equal 0.28
17
+ variable Ma equal 3.3
18
+ variable To equal 273.0
19
+ variable Tb equal 273.0
20
+ variable theta equal 0.0
21
+ variable mfp equal ${Kn}*${L}
22
+ variable mfp equal 0.28*${L}
23
+ variable mfp equal 0.28*1
24
+ variable cpmfp equal 4
25
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
26
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
27
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
28
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(${mfp}/${cpmfp}*${Kn})
29
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/${cpmfp}*${Kn})
30
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*${Kn})
31
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*0.28)
32
+ variable sos equal sqrt(${gamma}*${R}*${To})
33
+ variable sos equal sqrt(1.666666667*${R}*${To})
34
+ variable sos equal sqrt(1.666666667*208.242684766214*${To})
35
+ variable sos equal sqrt(1.666666667*208.242684766214*273)
36
+ variable vo equal ${Ma}*${sos}
37
+ variable vo equal 3.3*${sos}
38
+ variable vo equal 3.3*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 1015.79136198765*cos(${theta}*2*PI/360)
41
+ variable vx equal 1015.79136198765*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 1015.79136198765*sin(${theta}*2*PI/360)
44
+ variable vy equal 1015.79136198765*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
47
+ variable xncells equal (6-${xmin})/${mfp}*${cpmfp}
48
+ variable xncells equal (6--5)/${mfp}*${cpmfp}
49
+ variable xncells equal (6--5)/0.28*${cpmfp}
50
+ variable xncells equal (6--5)/0.28*4
51
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
52
+ variable yncells equal (5-${ymin})/${mfp}*${cpmfp}
53
+ variable yncells equal (5--5)/${mfp}*${cpmfp}
54
+ variable yncells equal (5--5)/0.28*${cpmfp}
55
+ variable yncells equal (5--5)/0.28*4
56
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
57
+ variable Fnum equal 6.60399822965135e+19*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
58
+ variable Fnum equal 6.60399822965135e+19*(6-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
59
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
60
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5-${ymin})/${ppc}/${xncells}/${yncells}
61
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/${ppc}/${xncells}/${yncells}
62
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/${xncells}/${yncells}
63
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/${yncells}
64
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/142.857142857143
65
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
66
+ variable tstep equal (--5+${xmax})/${vx}/${xncells}/10/4
67
+ variable tstep equal (--5+6)/${vx}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/1015.79136198765/${xncells}/10/4
69
+ variable tstep equal (--5+6)/1015.79136198765/157.142857142857/10/4
70
+ variable nave equal 10000
71
+ variable noutput equal 300000
72
+ clear
73
+ Running on 128 MPI task(s)
74
+ shell mkdir output
75
+ shell mkdir grid
76
+ shell mkdir surf
77
+ seed 7
78
+ dimension 2
79
+ global nrho ${nden}
80
+ global nrho 6.60399822965135e+19
81
+ global fnum ${Fnum}
82
+ global fnum 1.07865304417639e+16
83
+ timestep ${tstep}
84
+ timestep 1.72279472486918e-06
85
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
86
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
87
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
88
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
89
+ boundary o o p
90
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
91
+ create_box -5 ${xmax} ${ymin} ${ymax} -0.5 0.5
92
+ create_box -5 6 ${ymin} ${ymax} -0.5 0.5
93
+ create_box -5 6 -5 ${ymax} -0.5 0.5
94
+ create_box -5 6 -5 5 -0.5 0.5
95
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
96
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
97
+ create_grid 157.142857142857 ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
98
+ create_grid 157.142857142857 142.857142857143 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
99
+ Created 97600 child grid cells
100
+ CPU time = 0.13768 secs
101
+ create/ghost percent = 0.955671 99.0443
102
+ balance_grid rcb cell
103
+ Balance grid migrated 97380 cells
104
+ CPU time = 0.102242 secs
105
+ reassign/sort/migrate/ghost percent = 53.7683 0.0337434 8.87719 37.3208
106
+ species gas.species Ar
107
+ mixture air Ar frac 1.0
108
+ mixture air Ar vstream ${vx} ${vy} 0.0 temp ${To}
109
+ mixture air Ar vstream 1015.79136198765 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 1015.79136198765 0 0.0 temp ${To}
111
+ mixture air Ar vstream 1015.79136198765 0 0.0 temp 273
112
+ collide vss air gas.vhs
113
+ collide_modify vremax 100 yes remain no nearcp yes 10
114
+ read_surf cylinder.surf
115
+ 200 lines
116
+ -0.5 0.5 xlo xhi
117
+ -0.5 0.5 ylo yhi
118
+ 0 0 zlo zhi
119
+ 0.0157073 min line length
120
+ 456 0 = cells overlapping surfs, overlap cells with unmarked corner pts
121
+ 87188 9956 456 = cells outside/inside/overlapping surfs
122
+ 456 = surf cells with 1,2,etc splits
123
+ 109.215 109.215 = cell-wise and global flow volume
124
+ CPU time = 0.112149 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 18.894 23.9987 2.42929 22.302 32.376 6.13091 0.103611
126
+ surf2grid time = 0.0250115 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 15.1269 23.8807 5.88661 1.59925 11.1873 6.83301
128
+ surf_collide 1 diffuse 273.0 1.0
129
+ surf_modify all collide 1
130
+ fix in emit/face air xlo ylo yhi
131
+ balance_grid rcb cell
132
+ Balance grid migrated 1938 cells
133
+ CPU time = 0.180105 secs
134
+ reassign/sort/migrate/ghost percent = 38.8983 0.0156297 5.04829 56.0378
135
+ create_particles air n 0
136
+ WARNING: Created unexpected # of particles: 668660 versus 668661 (../create_particles.cpp:346)
137
+ Created 668660 particles
138
+ CPU time = 0.0171434 secs
139
+ stats 10000
140
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
141
+ run 200000
142
+ Memory usage per proc in Mbytes:
143
+ particles (ave,min,max) = 1.79297 0 3.375
144
+ grid (ave,min,max) = 4.297 1.51379 7.13879
145
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
146
+ total (ave,min,max) = 6.11057 3.22189 8.84689
147
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
148
+ 0 0 668660 0 0 0 0 0
149
+ 10000 17.323615 729374 19329 16394 10 693764.11 14.507
150
+ 20000 39.595872 737578 19554 16619 24 714589 14.55005
151
+ 30000 62.166123 738301 19631 16726 13 722415.43 14.596667
152
+ 40000 84.185744 737771 19495 16636 13 726300.89 14.6068
153
+ 50000 106.91785 738029 19583 16537 16 728648.91 14.62676
154
+ 60000 129.6107 738879 19505 16571 13 730293.92 14.63525
155
+ 70000 152.5167 738727 19755 16708 16 731437.41 14.6428
156
+ 80000 174.0455 739435 19812 16819 11 732323.66 14.653588
157
+ 90000 196.58103 738055 19732 16818 12 733069.34 14.659744
158
+ 100000 219.09886 737894 19603 16647 14 733544.51 14.66955
159
+ 110000 241.94607 737931 19626 16670 19 733974.98 14.669873
160
+ 120000 264.10218 738588 19683 16752 15 734319.47 14.671092
161
+ 130000 286.77116 737948 19714 16673 15 734607.85 14.671408
162
+ 140000 309.44905 738910 19579 16616 9 734852.43 14.674771
163
+ 150000 332.82323 738881 19521 16599 9 735110.32 14.670207
164
+ 160000 355.76165 737917 19709 16713 19 735295.77 14.675812
165
+ 170000 379.74074 737255 19667 16686 17 735473.15 14.675282
166
+ 180000 403.30907 738493 19565 16603 12 735609.15 14.676128
167
+ 190000 427.70233 738548 19679 16665 18 735763.14 14.677753
168
+ 200000 451.41144 738663 19842 16824 15 735905.17 14.680565
169
+ Loop time of 451.412 on 128 procs for 200000 steps with 738663 particles
170
+
171
+ MPI task timing breakdown:
172
+ Section | min time | avg time | max time |%varavg| %total
173
+ ---------------------------------------------------------------
174
+ Move | 0.023876 | 18.127 | 106.22 | 551.2 | 4.02
175
+ Coll | 0.48678 | 22.511 | 184.04 | 694.8 | 4.99
176
+ Sort | 0.16698 | 3.6263 | 30.495 | 280.5 | 0.80
177
+ Comm | 75.463 | 110.23 | 122.47 | 91.4 | 24.42
178
+ Modify | 0.020277 | 0.17246 | 3.4759 | 114.1 | 0.04
179
+ Output | 0.003812 | 0.020179 | 0.022184 | 2.2 | 0.00
180
+ Other | | 296.7 | | | 65.73
181
+
182
+ Particle moves = 147181034925 (147B)
183
+ Cells touched = 154105006972 (154B)
184
+ Particle comms = 257316863 (257M)
185
+ Boundary collides = 0 (0K)
186
+ Boundary exits = 25772605 (25.8M)
187
+ SurfColl checks = 134541864 (135M)
188
+ SurfColl occurs = 2936113 (2.94M)
189
+ Surf reactions = 0 (0K)
190
+ Collide attempts = 4394384994 (4.39B)
191
+ Collide occurs = 3384357701 (3.38B)
192
+ Reactions = 0 (0K)
193
+ Particles stuck = 0
194
+ Axisymm bad moves = 0
195
+
196
+ Particle-moves/CPUsec/proc: 2.54723e+06
197
+ Particle-moves/step: 735905
198
+ Cell-touches/particle/step: 1.04704
199
+ Particle comm iterations/step: 1
200
+ Particle fraction communicated: 0.0017483
201
+ Particle fraction colliding with boundary: 0
202
+ Particle fraction exiting boundary: 0.000175108
203
+ Surface-checks/particle/step: 0.000914125
204
+ Surface-collisions/particle/step: 1.9949e-05
205
+ Surf-reactions/particle/step: 0
206
+ Collision-attempts/particle/step: 0.029857
207
+ Collisions/particle/step: 0.0229945
208
+ Reactions/particle/step: 0
209
+
210
+ Particles: 5770.8 ave 35996 max 0 min
211
+ Histogram: 84 14 4 8 6 6 2 0 0 4
212
+ Cells: 762.5 ave 763 max 762 min
213
+ Histogram: 64 0 0 0 0 0 0 0 0 64
214
+ GhostCell: 19721.7 ave 36096 max 1099 min
215
+ Histogram: 20 10 12 10 18 2 6 4 16 30
216
+ EmptyCell: 0 ave 0 max 0 min
217
+ Histogram: 128 0 0 0 0 0 0 0 0 0
218
+ Surfs: 200 ave 200 max 200 min
219
+ Histogram: 128 0 0 0 0 0 0 0 0 0
220
+ GhostSurf: 0 ave 0 max 0 min
221
+ Histogram: 128 0 0 0 0 0 0 0 0 0
222
+ compute 1 grid all air n nrho massrho u v w
223
+ compute 2 grid all air trot
224
+ compute 3 tvib/grid all air
225
+ compute 4 thermal/grid all air press temp
226
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
227
+ fix 1 ave/grid all 1 10000 ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
228
+ fix 1 ave/grid all 1 10000 10000 c_1[*] c_4[*] c_2[*] c_3[*] ave running
229
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
230
+ dump 1 grid all 300000 output/grid.*.dat id xc yc f_1[*]
231
+ dump_modify 1 format float %12.5g
232
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
233
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
234
+ fix 2 ave/surf all 1 10000 ${nave} c_5[*] ave running
235
+ fix 2 ave/surf all 1 10000 10000 c_5[*] ave running
236
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
237
+ dump 2 surf all 300000 output/surf.*.dat id v1x v1y v2x v2y f_2[*]
238
+ dump_modify 2 format float %12.5g
239
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
240
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
241
+ dump 3 grid all 300000 output/lambdakn.*.dat id xc yc c_6[*]
242
+ dump_modify 3 format float %12.5g
243
+ run 100000
244
+ Memory usage per proc in Mbytes:
245
+ particles (ave,min,max) = 1.87207 0 5.0625
246
+ grid (ave,min,max) = 4.297 1.51379 7.13879
247
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
248
+ total (ave,min,max) = 6.4459 3.47786 9.10336
249
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
250
+ 200000 0 738663 19842 16824 0 0 0
251
+ 210000 39.03671 737584 19554 16608 14 738545.01 14.6599
252
+ 220000 77.808855 738408 19633 16661 15 738523.78 14.72045
253
+ 230000 116.90592 738691 19670 16780 14 738590.14 14.714167
254
+ 240000 157.19402 736688 19409 16531 18 738512.21 14.69725
255
+ 250000 196.43484 737541 19463 16494 14 738422.57 14.70434
256
+ 260000 235.50827 738168 19773 16769 18 738372.21 14.706267
257
+ 270000 275.96046 737896 19535 16581 12 738374.44 14.710186
258
+ 280000 315.08012 738090 19665 16715 19 738343.39 14.699488
259
+ 290000 353.58657 737492 19581 16696 16 738255.74 14.699933
260
+ 300000 394.71416 737804 19571 16627 9 738227.45 14.69917
261
+ Loop time of 394.715 on 128 procs for 100000 steps with 737804 particles
262
+
263
+ MPI task timing breakdown:
264
+ Section | min time | avg time | max time |%varavg| %total
265
+ ---------------------------------------------------------------
266
+ Move | 0.013458 | 9.1654 | 53.99 | 389.9 | 2.32
267
+ Coll | 0.24309 | 11.505 | 98.554 | 508.5 | 2.91
268
+ Sort | 0.095485 | 1.8675 | 13.699 | 194.9 | 0.47
269
+ Comm | 54.665 | 75.007 | 83.084 | 71.3 | 19.00
270
+ Modify | 3.4266 | 25.232 | 139.4 | 599.8 | 6.39
271
+ Output | 0.0798 | 0.082333 | 0.086212 | 0.5 | 0.02
272
+ Other | | 271.9 | | | 68.87
273
+
274
+ Particle moves = 73822745026 (73.8B)
275
+ Cells touched = 77292150940 (77.3B)
276
+ Particle comms = 128937486 (129M)
277
+ Boundary collides = 0 (0K)
278
+ Boundary exits = 12923056 (12.9M)
279
+ SurfColl checks = 67497553 (67.5M)
280
+ SurfColl occurs = 1469917 (1.47M)
281
+ Surf reactions = 0 (0K)
282
+ Collide attempts = 2216003609 (2.22B)
283
+ Collide occurs = 1706399478 (1.71B)
284
+ Reactions = 0 (0K)
285
+ Particles stuck = 0
286
+ Axisymm bad moves = 0
287
+
288
+ Particle-moves/CPUsec/proc: 1.46116e+06
289
+ Particle-moves/step: 738227
290
+ Cell-touches/particle/step: 1.047
291
+ Particle comm iterations/step: 1
292
+ Particle fraction communicated: 0.00174658
293
+ Particle fraction colliding with boundary: 0
294
+ Particle fraction exiting boundary: 0.000175055
295
+ Surface-checks/particle/step: 0.000914319
296
+ Surface-collisions/particle/step: 1.99114e-05
297
+ Surf-reactions/particle/step: 0
298
+ Collision-attempts/particle/step: 0.0300179
299
+ Collisions/particle/step: 0.0231148
300
+ Reactions/particle/step: 0
301
+
302
+ Particles: 5764.09 ave 35994 max 0 min
303
+ Histogram: 82 16 4 8 6 6 2 0 0 4
304
+ Cells: 762.5 ave 763 max 762 min
305
+ Histogram: 64 0 0 0 0 0 0 0 0 64
306
+ GhostCell: 19721.7 ave 36096 max 1099 min
307
+ Histogram: 20 10 12 10 18 2 6 4 16 30
308
+ EmptyCell: 0 ave 0 max 0 min
309
+ Histogram: 128 0 0 0 0 0 0 0 0 0
310
+ Surfs: 200 ave 200 max 200 min
311
+ Histogram: 128 0 0 0 0 0 0 0 0 0
312
+ GhostSurf: 0 ave 0 max 0 min
313
+ Histogram: 128 0 0 0 0 0 0 0 0 0
314
+ write_grid grid/final-grid.dat
315
+ grid cells = 97600
316
+ CPU time = 0.0356052 secs
317
+ write_surf surf/geom.surf
318
+ surf elements = 200
319
+ CPU time = 0.00166842 secs
CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/mir.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ dimension 2
2
+ create_box -5.0 6.0 -5.0 5.0 -0.5 0.5
3
+ read_grid grid/final-grid.dat
CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/output/surf.300000.dat ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ITEM: TIMESTEP
2
+ 300000
3
+ ITEM: NUMBER OF SURFS
4
+ 200
5
+ ITEM: BOX BOUNDS oo oo pp
6
+ -5 6
7
+ -5 5
8
+ -0.5 0.5
9
+ ITEM: SURFS id v1x v1y v2x v2y f_2[1] f_2[2] f_2[3] f_2[4] f_2[5] f_2[6] f_2[7] f_2[8] f_2[9] f_2[10] f_2[11]
10
+ 1 0.5 0 0.49975 -0.015705 0.00504 0.088047 -0.088036 0.001383 -4.1619e-05 -0.0026493 -0.0013835 -1.9891e-05 6.5955 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.12122 2.1814 1.3639 -1.7025 0.29598 0.23713 0.026073 -0.023017 260.61 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.00432 0.073545 -0.073463 0.0034644 0.00019843 0.0042077 -0.0011508 0.00012051 4.9124 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.11423 2.0515 1.2317 -1.6405 0.30437 0.22852 0.024128 -0.022179 255.96 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00495 0.085207 -0.084945 0.0066853 4.2467e-05 0.0005396 -0.0013336 0.00011348 6.1087 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.10919 1.9819 1.1396 -1.6215 0.29878 0.20998 0.022593 -0.02217 257.21 0 0
16
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208
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CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/runmpirun128.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 2
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 128 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat