Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “regularity”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19

Formation of Large Regular Satellites of Giant Planets in an Extended Gaseous Nebula: Subnebula Model and Accretion of Satellites

We model the subnebulae of Jupiter and Saturn wherein satellite accretion took place. We expect a giant planet subnebula to be composed of an optically thick (given gaseous opacity) inner region inside of the planet's centrifugal radius (located at r(sub c, sup J) = l5R(sub J) for Jupiter and r(sub c, sup S) = 22R(sub S) for Saturn), and an optically thin, extended outer disk out to a fraction of the planet's Roche lobe, which we choose to be R(sub roche)/5 (located at approximately 150R(sub J) near the inner irregular satellites for Jupiter, and approximately 200R(sub S) near Phoebe for Saturn). This places Titan and Ganymede in the inner disk, Callisto and Iapetus in the outer disk, and Hyperion in the transition region. The inner disk is the leftover of the gas accreted by the protoplanet. The outer disk results from the solar torque on nebula gas flowing into the protoplanet during the time of giant planet gap opening. For the sake of specificity, we use a cosmic mixture 'minimum mass' model to constrain the gas densities of the inner disks of Jupiter and Saturn (and also Uranus). For the total mass of the outer disk we use the simple scaling M(sub disk) = M(sub P)tau(sub gap)/tau(sub acc), where M(sub P) is the mass of the giant planet, tau(sub gap) is the gap opening timescale, and tau(sub acc) is the giant planet accretion time. This gives a total outer disk mass of approximately 100M(sub Callisto) for Jupiter and possibly approximately 200M(sub Iapetus) for Saturn (which contain enough condensables to form Callisto and Iapetus respectively). Our model has Ganymede at a subnebula temperature of approximately 250 K and Titan at approximately 100 K. The outer disks of Jupiter and Saturn have constant temperatures of 130 K and 90 K respectively.

Mosqueira, I.↗

Formation of Large Regular Satellites of Giant Planets in an Extended Gaseous Nebula: Satellite Migration And Survival - 2

Using an optically thick inner disk and an extended, optically thin outer disk as described in Mosqueira and Estrada, we compute the torque as a function of position in the subnebula, and show that although the torque exerted on the satellite is generally negative, which leads to inward migration as expected, there are regions of the disk where the torque is positive. For our model these regions of positive torque correspond roughly to the locations of Callisto and Iapetus. Though the outer location of zero torque depends on the (unknown) size of the transition region between the inner and outer disks, the result that Saturn's is found much farther out (at approximately 3r(sub c, sup S) where r(sub c, sup S) is Saturn's centrifugal radius) than Jupiter's (at approximately 2r(sub c, sup J), where r(sub c, sup J) is Jupiter's centrifugal radius) is mostly due to Saturn's less massive outer disk, and larger Hill radius. For a satellite to survive in the disk the timescale of satellite migration must be longer than the timescale for gas dissipation. For large satellites (approximately 1000 km) migration is dominated by the gas torque. We consider the possibility that the feedback reaction of the gas disk caused by the redistribution of gas surface density around satellites with masses larger than the inertial mass causes a large drop in the drift velocity of such objects, thus improving the likelihood that they will be left stranded following gas dissipation. We adapt the inviscid inertial mass criterion to include gas drag, and m-dependent non-local deposition of angular momentum.

Mosqueira, I.↗

Gap-Opening, Disk Clearing, and the Survival of the Regular Satellites of Jupiter and Saturn

It is only recently that the theory of disk-companion interactions yields migration rates due to the gas tidal torque that are in agreement with numerical simulations and up to an order of magnitude slower than previous estimates. Also, for a weakly turbulent disk, the gap size is controlled primarily by the damping length of acoustic waves launched by the secondary at Lindblad resonances, which in turn depends on whether the waves are 2D or 3D. At least for small azimuthal wavenumbers this damping length is of the order of the radial location of the Lindblad resonance. This may have important consequences for disk dispersal in satellite systems. In the case of Jupiter, it means that the inner Galilean satellites may have jointly opened a gap. On the other hand, in Saturn's system the satellites inside of Titan are probably too small to have opened gaps in the gas disk at the time of their formation; but the possibility exists that, by effectively clearing the gas disk inside its own orbit, Titan may have allowed smaller satellites to survive, depending on whether Titan can clear the disk in a timescale comparable to the migration rates due to gas drag and gas tidal torque for these objects.

Estrada, P. R.↗

A Time-Regularized Multiple Gravity-Assist Low-Thrust Bounded-Impulse Model for Trajectory Optimization

The multiple gravity assist low-thrust (MGALT) trajectory model combines the medium-fidelity Sims-Flanagan bounded-impulse transcription with a patched-conics flyby model and is an important tool for preliminary trajectory design. While this model features fast state propagation via Kepler's equation and provides a pleasingly accurate estimation of the total mass budget for the eventual flight-suitable integrated trajectory it does suffer from one major drawback, namely its temporal spacing of the control nodes. We introduce a variant of the MGALT transcription that utilizes the generalized anomaly from the universal formulation of Kepler's equation as a decision variable in addition to the trajectory phase propagation time. This results in two improvements over the traditional model. The first is that the maneuver locations are equally spaced in generalized anomaly about the orbit rather than time. The second is that the Kepler propagator now has the generalized anomaly as its independent variable instead of time and thus becomes an iteration-free propagation method. The new algorithm is outlined, including the impact that this has on the computation of Jacobian entries for numerical optimization, and a motivating application problem is presented that illustrates the improvements that this model has over the traditional MGALT transcription.

Optimization↗

A Time-Regularized, Multiple Gravity-Assist Low-Thrust, Bounded-Impulse Model for Trajectory Optimization

The multiple gravity assist low-thrust (MGALT) trajectory model combines the medium-fidelity Sims-Flanagan bounded-impulse transcription with a patched-conics flyby model and is an important tool for preliminary trajectory design. While this model features fast state propagation via Keplers equation and provides a pleasingly accurate estimation of the total mass budget for the eventual flight suitable integrated trajectory it does suffer from one major drawback, namely its temporal spacing of the control nodes. We introduce a variant of the MGALT transcription that utilizes the generalized anomaly from the universal formulation of Keplers equation as a decision variable in addition to the trajectory phase propagation time. This results in two improvements over the traditional model. The first is that the maneuver locations are equally spaced in generalized anomaly about the orbit rather than time. The second is that the Kepler propagator now has the generalized anomaly as its independent variable instead of time and thus becomes an iteration-free propagation method. The new algorithm is outlined, including the impact that this has on the computation of Jacobian entries for numerical optimization, and a motivating application problem is presented that illustrates the improvements that this model has over the traditional MGALT transcription.

Optimization↗