Jovian Meteoroid Environment Model JMEM

Transcription

1 Jovian Meteoroid Environment Model JMEM J. Schmidt, X Liu (U Oulu) M. Sachse, F. Spahn (U Potsdam) R. Soja, R. Srama (U Stuttgart) images: NASA) 1

2 Some Background 2

3 ring system and ring moons 3

4 four large moons Io Ganymede rings Europa Callisto 4

5 5

6 Dust is all over the place: images: NASA) 6

7 Dust is all over the place: * main rings, halo & gossamer ring (Hamilton&Krüger, 2008, Nature) images: NASA) 6

8 Dust is all over the place: * main rings, halo & gossamer ring * stream particles from Io's volcanoes images: NASA) 6

9 Dust is all over the place: * main rings, halo & gossamer ring * stream particles from Io's volcanoes * dust from the irregular satellites images: NASA) 6

10 Dust is all over the place: * main rings, halo & gossamer ring * stream particles from Io's volcanoes * dust from the irregular satellites images: NASA) 6

11 Dust is all over the place: * main rings, halo & gossamer ring * stream particles from Io's volcanoes * dust from the irregular satellites * external dust magnetospherically captured images: NASA) 6

12 Dust is all over the place: * main rings, halo & gossamer ring * stream particles from Io's volcanoes * dust from the irregular satellites * external dust magnetospherically captured * dust from the Galilean moons images: NASA) 6

13 Dust from the Galilean moons: -> ejecta from impacts of interplanetary dust: fill region of Galilean moons Modeling Krivov et al., 2003 Sremcevic et al.,

14 Dust from the Galilean moons: -> ejecta from impacts of interplanetary dust: fill region of Galilean moons Modeling Galileo DDS data: Krivov et al., 2003 Sremcevic et al., 2003 (Krüger et al, 1999, Nature) (Krüger et al, 2003, Icarus) 7

15 Dust from the Galilean moons: -> ejecta from impacts of interplanetary dust: fill region of Galilean moons -> most relevant for JUICE dust hazard Galileo DDS data: Modeling Krivov et al., 2003 Sremcevic et al., 2003 (Krüger et al, 1999, Nature) (Krüger et al, 2003, Icarus) 7

16 Available data: Galileo DDS FIGURE 10 from Krivov et al 2002: Galileo DDS NUMBER DENSITY [km**-3] KRIVOV FIG 10 Harald data no y_corr Harald data with y_corr RADIAL DISTANCE [RJ] /Users/jschmidt/Projects/Dyne2/GalileoData/DisplayGalileoData jschmidt Fri Mar 27 10:34:

17 Available data: Galileo DDS FIGURE 10 from Krivov et al 2002: Galileo DDS NUMBER DENSITY [km**-3] KRIVOV FIG 10 Harald data no y_corr Harald data with y_corr use these data to calibrate the model (H. Krüger, pers. comm. Hamilton&Krüger, 2008; Krüger et al, 2009) RADIAL DISTANCE [RJ] /Users/jschmidt/Projects/Dyne2/GalileoData/DisplayGalileoData jschmidt Fri Mar 27 10:34:

18 Available data: Galileo DDS FIGURE 10 from Krivov et al 2002: Galileo DDS NUMBER DENSITY [km**-3] KRIVOV FIG 10 Harald data no y_corr Harald data with y_corr use these data to calibrate the model (H. Krüger, pers. comm. Hamilton&Krüger, 2008; Krüger et al, 2009) RADIAL DISTANCE [RJ] /Users/jschmidt/Projects/Dyne2/GalileoData/DisplayGalileoData jschmidt Fri Mar 27 10:34:

19 Available data: Galileo DDS FIGURE 10 from Krivov et al 2002: Galileo DDS NUMBER DENSITY [km**-3] KRIVOV FIG 10 Harald data no y_corr Harald data with y_corr (Hamilton&Krüger, 2008, Nature) RADIAL DISTANCE [RJ] /Users/jschmidt/Projects/Dyne2/GalileoData/DisplayGalileoData jschmidt Fri Mar 27 10:34:

20 Construction of the Model 10

21 11

22 A dust creation, launch to orbit: -> want to test effect of different starting distributions? -> dynamics through Hill-sphere (nearly) generic 11

23 A dust creation, launch to orbit: B long term orbital evolution: -> CPU expensive. Want to re-use individual trajectories -> statistical weighting with probabilities from A -> want to test effect of different starting distributions? -> dynamics through Hill-sphere (nearly) generic 11

24 1 st step: ~10 6 particles from surface -> tabulate phase space Hill scale -> determine abscissas and weights for Gauss integration 12

25 1 st step: ~10 6 particles from surface -> tabulate phase space Hill scale -> determine abscissas and weights for Gauss integration 12

26 -> for each Galilean moon ~ 10s of CPU hours -> repeat for various starting distributions, if wanted 1 st step: ~10 6 particles from surface -> tabulate phase space Hill scale -> determine abscissas and weights for Gauss integration 12

27 ~r = ~r Sat EM RP DRAG MOONS SOLG 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions 13

28 ~r = ~r Sat EM RP DRAG MOONS SOLG planet, inc. higher moments 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions 13

29 ~r = ~r Sat EM RP DRAG MOONS SOLG planetary magnetic field 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions 13

30 ~r = ~r Sat EM RP DRAG MOONS SOLG radiation pressure and Poynting-Robertson drag 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions 13

31 ~r = ~r Sat EM RP DRAG MOONS SOLG plasma drag 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions 13

32 ~r = ~r Sat EM RP DRAG MOONS SOLG gravity of satellites 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions 13

33 ~r = ~r Sat EM RP DRAG MOONS SOLG solar gravity 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions 13

34 ~r = ~r Sat EM RP DRAG MOONS SOLG solar gravity 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions -> do this for 10 particle sizes from 0.05 micron to 1cm -> integrate until the particle hits a sink: planet, moons, escape from the system -> repeat for each moon: 60,000 CPU hours on large cluster 13

35 ~r = ~r Sat EM RP DRAG MOONS SOLG solar gravity 2 nd step: for given moon do -> numerical integrations using the abscissa values of Gaussian weighting as starting conditions -> do this for 10 particle sizes from 0.05 micron to 1cm -> integrate until the particle hits a sink: planet, moons, escape from the system -> repeat for each moon: 60,000 CPU hours on large cluster storage: save (osculating) orbital elements ~ once per orbit 13

36 3 rd step: populate cylindrical grid from elements: Ganymede positions times J Europa 14

37 3 rd step: populate cylindrical grid from elements: Ganymede positions times J increment location in the grid Europa 15

38 3 rd step: populate cylindrical grid from elements: Ganymede positions times J increment location in the grid Europa 16

39 4 th step: co-adding and calibration number density for grains of size r in cell i,j,k of grid 17

40 4 th step: co-adding and calibration ñ moon (i, j, k; r)dr =dr Z d P moon ( ) N moon( ; i, j, k; r) V cell (i, j, k) where = {,,,, v} number density for grains of size r in cell i,j,k of grid 17

41 4 th step: co-adding and calibration ñ moon (i, j, k; r)dr =dr A) distributions of starting conditions 1 st step Z number density for grains of size r in cell i,j,k of grid d P moon ( ) N moon( ; i, j, k; r) V cell (i, j, k) where = {,,,, v} {z } pos and direction of velocity starting vector 17

42 4 th step: co-adding and calibration ñ moon (i, j, k; r)dr =dr A) distributions of starting conditions 1 st step Z number density for grains of size r in cell i,j,k of grid B) grid from 2 nd step d P moon ( ) N moon( ; i, j, k; r) V cell (i, j, k) where = {,,,, v} {z } pos and direction of velocity starting vector 17

43 4 th step: co-adding and calibration ñ moon (i, j, k; r)dr =dr A) distributions of starting conditions 1 st step Z number density for grains of size r in cell i,j,k of grid B) grid from 2 nd step d P moon ( ) N moon( ; i, j, k; r) V cell (i, j, k) where = {,,,, v} {z } pos and direction of velocity starting vector numerical integration: use Gaussian weights 17

44 4 th step: co-adding and calibration ñ moon (i, j, k; r)dr =dr A) distributions of starting conditions 1 st step Z number density for grains of size r in cell i,j,k of grid n total (i, j, k; >r)= X B) grid from 2 nd step d P moon ( ) N moon( ; i, j, k; r) V cell (i, j, k) where = {,,,, v} {z } moons pos and direction of velocity starting vector numerical integration: use Gaussian weights c moon Z 1 r dr 0 f(r 0 )ñ moon (i, j, k; r 0 ) 17

45 4 th step: co-adding and calibration ñ moon (i, j, k; r)dr =dr A) distributions of starting conditions 1 st step Z number density for grains of size r in cell i,j,k of grid n total (i, j, k; >r)= X B) grid from 2 nd step d P moon ( ) N moon( ; i, j, k; r) V cell (i, j, k) where = {,,,, v} {z } moons pos and direction of velocity starting vector numerical integration: use Gaussian weights c moon Z 1 r dr 0 f(r 0 )ñ moon (i, j, k; r 0 ) 17 plausible power law: e.g. r -3.7

46 4 th step: co-adding and calibration ñ moon (i, j, k; r)dr =dr A) distributions of starting conditions 1 st step Z number density for grains of size r in cell i,j,k of grid n total (i, j, k; >r)= X B) grid from 2 nd step d P moon ( ) N moon( ; i, j, k; r) V cell (i, j, k) where = {,,,, v} {z } moons pos and direction of velocity starting vector numerical integration: use Gaussian weights c moon Z 1 r dr 0 f(r 0 )ñ moon (i, j, k; r 0 ) normalization from Galileo DDS 17 plausible power law: e.g. r -3.7

47 4 th step: co-adding and calibration * do the same for the velocity in a given grid cell ~v total (i, j, k; >r)= X moons c moon Z 1 r dr 0 f(r 0 ) ~v moon (i, j, k; r 0 ) normalization from Galileo DDS 18 plausible power law: e.g. r -3.7

48 4 th step: co-adding and calibration * do the same for the velocity in a given grid cell * to reconstruct the flux on a user specified surface: also store moments for <v 2 > and <v 3 > ~v total (i, j, k; >r)= X moons c moon Z 1 r dr 0 f(r 0 ) ~v moon (i, j, k; r 0 ) normalization from Galileo DDS 18 plausible power law: e.g. r -3.7

49 4 th step: co-adding and calibration * do the same for the velocity in a given grid cell * to reconstruct the flux on a user specified surface: also store moments for <v 2 > and <v 3 > * populating the grid cells takes about 2 CPU hours for all moons, grain sizes X Z 1 ~v total (i, j, k; >r)= c moon moons r dr 0 f(r 0 ) ~v moon (i, j, k; r 0 ) normalization from Galileo DDS 18 plausible power law: e.g. r -3.7

50 5 th step: evaluation, visualization, with IDL user interface practically instantaneous reaction on user specified change of parameters uses NAIF Spice toolkit for SC trajectory 19

51 Calibration, in practice NUMBER DENSITY [km**-3] FIGURE 10 from Krivov et al 2002: Galileo DDS KRIVOV FIG 10 Harald data no y_corr Harald data with y_corr RADIAL DISTANCE [RJ] /Users/jschmidt/Projects/Dyne2/GalileoData/DisplayGalileoData jschmidt Fri Mar 27 10:34:

52 Calibration, in practice FIGURE 10 from Krivov et al 2002: Galileo DDS NUMBER DENSITY [km**-3] KRIVOV FIG 10 Harald data no y_corr Harald data with y_corr use these data to calibrate the model (H. Krüger, pers. comm. Hamilton&Krüger, 2008; Krüger et al, 2009) RADIAL DISTANCE [RJ] /Users/jschmidt/Projects/Dyne2/GalileoData/DisplayGalileoData jschmidt Fri Mar 27 10:34:

53 Calibration, in practice FIGURE 10 from Krivov et al 2002: Galileo DDS NUMBER DENSITY [km**-3] RADIAL DISTANCE [RJ] 20 KRIVOV FIG 10 Harald data no y_corr Harald data with y_corr use these data to calibrate the model (H. Krüger, pers. comm. Hamilton&Krüger, 2008; Krüger et al, 2009) more than factor of two difference between two interpretations? /Users/jschmidt/Projects/Dyne2/GalileoData/DisplayGalileoData jschmidt Fri Mar 27 10:34:

54 Calibration, in practice - here: Galileo sees r>0.3mu 21

55 Calibration, in practice - here: Galileo sees r>0.3mu - low radial resolution: can be improved but need to re-assess Galileo data (H. Krüger, pers. com.) 21

56 Calibration, in practice - here: Galileo sees r>0.3mu - low radial resolution: can be improved but need to re-assess Galileo data (H. Krüger, pers. com.) - fairly large error bars 21

57 Calibration, in practice - here: Galileo sees r>0.3mu - low radial resolution: can be improved but need to re-assess Galileo data (H. Krüger, pers. com.) - fairly large error bars => the data does not give us tight constraints 21

58 Calibration, in practice - alternative interpretation: Galileo sees r>1 micron 22

59 Calibration, in practice - alternative interpretation: Galileo sees r>1 micron => this calibration would increase the number of large particles in the model 22

60 Calibration, in practice - alternative interpretation: Galileo sees r>1 micron => this calibration would increase the number of large particles in the model => but the 0.3 micron calibration is preferred by DDS 22

61 Calculation of the flux: Z J = d 3 vf(~v ) ~v sc ~v H (cos!) we cannot store the full velocity distribution function in each grid point! 23

62 Calculation of the flux: Z J = d 3 vf(~v ) ~v sc ~v H (cos!) we cannot store the full velocity distribution function in each grid point! and: approximation J = nv does likely NOT hold in many cases! 23

63 Calculation of the flux: Z J = d 3 vf(~v ) ~v sc ~v H (cos!) we cannot store the full velocity distribution function in each grid point! and: approximation J = nv does likely NOT hold in many cases! => we save 4 moments of the velocity components and use a known algorithm to reconstruct the distribution from the moments 23

64 Calculation of the flux: Example 24

65 Calculation of the flux: Example `true' distribution reconstruction from moments 24

66 Calculation of the flux: Example `true' distribution reconstruction from moments 24

67 Calculation of the flux: Example 25

68 Results 26

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77 Conclusions 35

78 Summary: -> numerical code to simulate dust dynamics in circumplanetary environment: - adapted to Jupiter system - tested relative to special case analytical solutions and results from literature (Krivov et al 2003 and others) 36

79 Summary: -> numerical code to simulate dust dynamics in circumplanetary environment: - adapted to Jupiter system - tested relative to special case analytical solutions and results from literature (Krivov et al 2003 and others) -> application to dust from Galilean moons: 60,000 CPU hours to cover the whole dust configuration on large computing cluster 36

80 Summary: -> numerical code to simulate dust dynamics in circumplanetary environment: - adapted to Jupiter system - tested relative to special case analytical solutions and results from literature (Krivov et al 2003 and others) -> application to dust from Galilean moons: 60,000 CPU hours to cover the whole dust configuration on large computing cluster -> calibration with Galileo DDS data and LDEX data from Luna 36

81 Summary: -> numerical code to simulate dust dynamics in circumplanetary environment: - adapted to Jupiter system - tested relative to special case analytical solutions and results from literature (Krivov et al 2003 and others) -> application to dust from Galilean moons: 60,000 CPU hours to cover the whole dust configuration on large computing cluster -> calibration with Galileo DDS data and LDEX data from Luna -> spatially resolved information on density, size distribution and velocity of dust 36

82 Summary: -> numerical code to simulate dust dynamics in circumplanetary environment: - adapted to Jupiter system - tested relative to special case analytical solutions and results from literature (Krivov et al 2003 and others) -> application to dust from Galilean moons: 60,000 CPU hours to cover the whole dust configuration on large computing cluster -> calibration with Galileo DDS data and LDEX data from Luna -> spatially resolved information on density, size distribution and velocity of dust -> IDL GUI to visualize results 36

83 New findings: -> need strong contribution by Io: high density seen by DDS between Io and Europa cannot be explained by Europa dust 37

84 New findings: -> need strong contribution by Io: high density seen by DDS between Io and Europa cannot be explained by Europa dust -> inside the Io orbit there seem to exist stable configurations where larger dust grains form fairly narrow tori 37

85 New findings: -> need strong contribution by Io: high density seen by DDS between Io and Europa cannot be explained by Europa dust -> inside the Io orbit there seem to exist stable configurations where larger dust grains form fairly narrow tori -> a fraction of grains develops retrograde orbits. This population is contained in our flux estimates. 37

86 New findings: -> need strong contribution by Io: high density seen by DDS between Io and Europa cannot be explained by Europa dust -> inside the Io orbit there seem to exist stable configurations where larger dust grains form fairly narrow tori -> a fraction of grains develops retrograde orbits. This population is contained in our flux estimates. -> initially steep size distribution at production flattens significantly 37

87 In progress: -> estimates for delivery of dust to the Galilean region from Gossamer rings and Thebe extension - same approach / code - expect little or no transport across Io's orbit -> evaluation and visualization of interplanetary and interstellar flux on the same grid 38

88 Take home for JUICE: peak flux of s>300micron ice Europa flybys (closest point to Jupiter in the tour): best guess: J= 3x10-10 m -2 s -1 worst case: J= 10-7 m -2 s -1 39