Determined effect of vent velocity ratio, r vel, on grain jet width (Cases 2, 3, 4 and 5) Jet width measured using Full Width at Half Maximum (FWHM) Jet width increases as r vel decreases; smaller r vel (larger velocity slip) results in greater drag, thus grains are more entrained and spread out by gas flow. Simulated E2 flyby to compare with E2 CDA data [7] Found no signal in all cases, suggesting a greater jet width (smaller r vel ); simulated flyby misses plume in all cases. Ran cases with imposed spreading half- angle θ sp at the vent (Cases 6 and 7) Minimum spreading half-angle to obtain a signal: 15  < θ sp < 30  Through extrapolation (see Figure 4), even with r vel = 0, the FWHM is still smaller than that of Case 6, implying no signal for this case as well. 1. Density contribution from each individual source, n, along the trajectory is determined via simulation. 2. The total density, n total, along the trajectory is obtained as follows: 3. L-S fitting of n total to INMS data is performed to obtain optimized set of s i. FM model uses the DSMC velocity distributions to assign its particles velocities at each point source. Velocities of gas molecules and grains are sampled to form velocity distributions. Modeling the Gas-Grain Plume of Enceladus S. K. Yeoh, T. A. Chapman, D. B. Goldstein, P. L. Varghese, L. M. Trafton The University of Texas at Austin; Parametric Study Using Model Conclusions A low mass flow rate of grains, compared to that of gas, and a small velocity difference at the vent barely affect the gas flow for micron-sized grains. Plumes are variable over period between flybys (months), varying by nearly 4× between E2 and E7; plumes may be variable on even shorter time scales. Constraint on grain jet width suggests other mechanisms may be responsible for grain formation, perhaps condensation above as opposed to below the vent [8]. It is also possible that the grains may not be coming out radially but may already have a spreading angle at the vent. Introduction In 2005, Cassini first detected a gas-grain plume over Enceladus’ south pole originating from the tiger-stripe fractures. The discovery not only helped unlock some mysteries, such as the source of Saturn’s E-ring grains [1] and the origin of the very bright expanses in Enceladus’ south polar region [2], but also opened doors to new possibilities, including the existence of extra-terrestrial life [3]. Consequently, it has been a very active area of research. Here, we model both the gas and the grain components of Enceladus’ plume to constrain the conditions at the sources. The Model We simulate the different regimes of the plume using models of different scales that are linked together to obtain the entire plume. Then, simulated flybys are performed and the results are compared with available in-situ data. Modeled channel as converging-diverging nozzle Assumed isentropic water vapor expansion from its triple point in the reservoir to the vent Subsurface Model Direct Simulation Monte Carlo (DSMC) Model for Collisional Near-Field Uses a representative set of computational particles to statistically approximate the motions of real gas molecules and grains Implements two-way coupling between gas and grains Free-molecular (FM) Model for Collisionless Far-Field Simulates ballistic particle motion under the influence of gravity Places 8 point sources on the planet surface, according to locations and jet orientations determined by Spitale and Porco [4] Includes analytic global and background sources Reservoir Throat Vent PropertyValue Diameter3.0 m Mach number5 Temperature50 K Density kg/m 3 Pressure0.9 Pa Speed900 m/s Vent-to-throat area ratio36 Table 1. Vent Conditions (Gas-only) Triple point of Water: Temperature = K Pressure = 612 Pa Flow becomes collisionless. Collisional flow in the near-field DSMC domain Vent conditions are used as input to DSMC model for gas; grains are initialized independently. Acknowledgements: Work is supported by NASA Cassini Data Analysis Program (CDAP) grants NNX08AP77G and NNH09ZDA001N-CDAP. Computations were performed at the Texas Advanced Computing Center (TACC). References: [1] Baum, W.A., et al., Saturn’s E Ring: I. CCD Observations of March Icarus 47, 84–96. [2] Porco, C.C., et al., Cassini Observes the Active South Pole of Enceladus. Science 311, 1393–1401. [3] McKay, C.P., et al., The Possible Origin and Persistence of Life on Enceladus and Detection of Biomarkers in the Plume. Astrobiology 8, 909–919. [4] Spitale, J.N., Porco, C.C., Association of the jets of Enceladus with the warmest regions on its south-polar fractures. Nature 449, 695–697. [5] Smith, H.T., et al., Enceladus plume variability and the neutral gas densities in Saturn’s magnetosphere. J. Geophys. Res. 115, A [6] Dong, Y., et al., The water vapor plumes of Enceladus. J. Geophys. Res. 116, A [7] Waite, J.H., et al., Cassini Ion and Neutral Mass Spectrometer: Enceladus Plume Composition and Structure. Science 311, 1419–1422. [8] Schmidt, J., et al., Slow dust in Enceladus’ plume from condensation and wall collisions in tiger stripe fractures. Nature 451, 685–688. Caser mass r vel θ sp (  ) Table 2. Parameter Values Source Tiger Stripe Strengths (kg/s) E3E5E7 IBaghdad IIDamascus IIIDamascus000 IVAlexandria VCairo VIBaghdad VIIBaghdad000 VIIICairo Total Strength We vary the parameters one at a time and study their effects on the plume near-field and far-field. Grains are 1-µm in size. Gas-only Case Case 1Case 2 Case 3 Near-Field Gas Number Density Contours Gas contours are hardly affected by grains in Case 1 (r mass = 0.1, r vel = 1.0). Grains change the gas contours in Cases 2 (r mass = 1.0, r vel = 1.0) and 3 (r mass = 1.0, r vel = 0.5), especially near the plume center. Grain columns are straight in Cases 1 and 2 and spreads out slightly in Case 3. Credit: NASA/JPL Figure 1. Gas number density contours. Black lines are outlines of grain columns. Far-field Results vs. Cassini INMS Data Definitions of Parameters: r mass Vent mass flow rate ratio of grains to gas r vel Vent velocity ratio of grains to gas θ sp Spreading half-angle of gas/grain jet imposed at the vent (see figure) Constraining Width of Grain Jets Figure 2. Least-Squares-Fitted Simulated Water Number Density Distributions along the Cassini E3, E5 and E7 trajectories compared to INMS data [5] [6]. Table 3. Optimized Source Strengths (pure gas, θ sp = 0  ) Simulated Cassini flyby water density distributions Performed least squares (L-S) fitting to INMS results to analyze the temporal variability of the plume s i : Strength of source i n i : Density contribution of source i along trajectory L-S Fitting Procedure: Figure 4. FWHM of the grain jets, normalized by the DSMC domain height (10 km), vs. velocity ratio, r vel. Vent θ sp Case 5 Case 4 Case 3 Case 2 Case 7 Case 6 Non-zero spreading angle CaseSignal? 6 (θ sp = 15  ) No 7 (θ sp = 30  ) Yes Credit: NASA/JPL