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Modeling the Sublimation-driven Atmosphere of Io with DSMC Andrew Walker David Goldstein, Chris Moore, Philip Varghese, and Laurence Trafton University.

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Presentation on theme: "Modeling the Sublimation-driven Atmosphere of Io with DSMC Andrew Walker David Goldstein, Chris Moore, Philip Varghese, and Laurence Trafton University."— Presentation transcript:

1 Modeling the Sublimation-driven Atmosphere of Io with DSMC Andrew Walker David Goldstein, Chris Moore, Philip Varghese, and Laurence Trafton University of Texas at Austin Department of Aerospace Engineering 41 st LPSC Conference March 3 rd, 2010 Supported by the NASA Planetary Atmosphere Program In collaboration with Deborah Levin and Sergey Gratiy at Pennsylvania State University

2 Outline Background information on Io Overview of DSMC –The basic method –Our modifications Gas dynamic results –Circumplanetary flow –Global column densities –Translational temperature Validation – comparison to observations Conclusions

3 Io is the closest satellite of Jupiter –Io radius ~1820 km It is the most volcanically active body in the solar system Many observations have failed to determine whether Io’s atmosphere is pre-dominantly volcanically or sublimation-driven. Background Information on Io Surface Temperature ~ 90 K – 115 K Length of Ionian Day ~ 42 hours Frost patch of condensed SO 2 Volcanic plume with ring deposition

4 The Basics of DSMC A spatial domain is decomposed into cells Representative molecules move and collide in these cells. Variables (temperature, density, etc.) are sampled from molecular properties in a given cell Cells can have a variety of boundary conditions: vacuum, specular/diffuse reflection, unit sticking, or periodic.

5 Overview of our DSMC code Three-dimensional Parallel Important physical models –Dual rock/frost surface model –Temperature-dependent residence time –Rotating temperature distribution –Variable weighting functions –Quantized vibrational & continuous rotational energy states –Photo-emission –Plasma heating Time scales Vibrational Half-lifemillisecond-second Time step0.5 seconds Between Collisions0.1 seconds - hours Residence TimeSeconds - Hours Ballistic Time2-3 Minutes Flow Evolution1-2 Hours Simulation Time2 hours Eclipse2 hours Io Day42 Hours

6 T frost Boundary Conditions – Surface temperature & frost fraction T rock Dual frost/rock surface temperature: –Independent thermal inertias and albedos –Same peak temperature (115 K) Temperature Dist. validated by Rathbun et al. (2004) Galileo PPR data Surface frost fraction from Doute et al. (2001)

7 Column density predominantly (exponentially) controlled by surface frost temperature –Due to exponential dependence of SO 2 vapor pressure on surface frost temperature Frost fraction has small (proportional) effect on column –Leads to slightly irregular column densities on dayside –Large irregularities on the nightside where the surface temperature is nearly constant Winds have negligible effect on the column Vertical Column Density

8 Streamlines in white; Sonic line in dashed white; Surface temperature contours in thick black (104 K and 108 K) Dusk vs. dawn asymmetry ( Horseshoe-shaped Shock) –Due to extended dawn atmospheric enhancement which blocks west-moving flow Along the equator, Mach numbers peak at: –M=1.40 for eastward flow; M=0.84 for westward flow Mach Number at 30 km Altitude

9 Coldest (~100 K) near peak surface temperature –Plasma energy coming down column of gas is completely absorbed above this altitude Very warm (~360 K) near the M=1.4 shock at the dusk terminator –Compressive shock heating Translational Temperature at 3 km Altitude

10 Types of Available Observations Plume ImagesAuroral GlowsIR Map of Hot Spots IR Map of Passive Background Disk-Averaged Spectra Lyman-  inferred column densities

11 Comparison of band depth vs. central longitude for several atmospheric cases (Gratiy et al., 2009) –The upper curve is a cos 1/4 (  ) variation with a 90 K nightside temperature –The lower curves are the temperatures needed to create a column densities inferred by Lyman-  observations. The empirical fit is also a cos 1/4 (  ) variation but with a 0 K nightside temperature. Comparison to Observations Comparison of our atmospheric simulations with inferred column densities from Lyman-  observations 115 K cases both show reasonable agreement with the peak of Feaga’s data (Feaga et al., 2009); however, the peak in Feaga’s data may be from additional volcanic column. There are morphological differences at mid- to high latitudes between the simulations and observations

12 Conclusions Column density is predominantly controlled by the frost surface temperature –Small effects from the surface frost fraction and negligible effects from flow The pressure-driven supersonic flow diverges from near the region of peak surface frost temperature toward the nightside –The extended dawn enhancement blocks the westward flow –Supersonic to east, north, and south of peak pressure –Horseshoe-shaped shock


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