Scaling sediment mobilization beneath rotorcraft for Titan and Mars

Rabinovitch, Jason; Lorenz, R. D.; Slimko, Eric; Wang, Kon-Sheng C.

doi:10.1016/j.aeolia.2020.100653

Cited by 8 publications

(16 citation statements)

References 21 publications

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“…A notable feature of the dust lifting shown in Figure 8 is that it did not turn sharply up as the helicopter approached the touchdown. In the Rabinovitch et al (2021) model, u* scales with ∼h −0.8 where h is the rotor height, and the drag force on particles scales as u* 2 (Greeley & Iversen, 1985).…”

Section: Dust Lifting During Landingmentioning

confidence: 99%

“…To assess the winds associated with the observed dust lifting, we relied on the helicopter brownout model of Rabinovitch et al (2021), which was developed early in the Ingenuity development cycle to estimate the expected severity of sediment mobilization during landing with a constant descent velocity. The brownout model of Rabinovitch et al (2021) uses several simplifying assumptions that allow the friction velocity on the Martian surface (generated by the high-speed helicopter rotor wash flow interacting with the surface) to be analytically predicted as a function of the helicopter altitude, helicopter operating parameters, and Martian environmental conditions. The brownout model includes the bulk rotor-driven wind-field and the advection of tip-vortices shed by the rotors.…”

Section: Dust Lifting During Landingmentioning

confidence: 99%

“…While a computational fluid dynamics (CFD) model would allow a fuller investigation that was out of the scope of this effort. Figure 7 shows the predicted friction velocity immediately under the helicopter (at one rotor radius) using the Rabinovitch et al (2021) analytical framework with input parameters of atmospheric density = 0.020 kg/m 3 , spin rate = 2,800 rpm, and T:W ∼1 conditions. We did not model takeoff due to expected large departures from equilibrium, high T:W, and the difficulty in determining when dust lifting ceased.…”

Section: Dust Lifting During Landingmentioning

confidence: 99%

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Lifting and Transport of Martian Dust by the Ingenuity Helicopter Rotor Downwash as Observed by High‐Speed Imaging From the Perseverance Rover

et al. 2022

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Understanding dust mobilization on Mars is essential for understanding and predicting the background atmospheric dust distribution, the onset and evolution of dust storms (the largest of which can cover the entire planet in a veil of dust for weeks on end, e.g., Guzewich et al., 2019), and the effects of lifted dust on Martian atmospheric dynamics. Due to Mars' low atmospheric density, which absorbs and scatters relatively little solar or thermal radiation, the presence of dust in the atmosphere has a major impact on its radiative balance, which in turn strongly affects thermal gradients and winds (Kahre et al., 2017;Wolff et al., 2017). The global distribution of atmospheric dust depends on atmospheric transport, interactions with ice particles (microphysics), fallout rate, and dust mobilization from the surface. The last process is least understood and in greatest need of investigation (Newman, Bertrand, et al., 2022). Dust lifting has been observed in both straight-line winds and vortices (dust-devils), which may be roughly equally important both outside dust storms (Newman, Hueso, et al., 2022) and during the onset of regional storms (Lemmon et al., 2022).

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Section: Dust Lifting During Landingmentioning

confidence: 99%

Section: Dust Lifting During Landingmentioning

confidence: 99%

Section: Dust Lifting During Landingmentioning

confidence: 99%

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Lifting and Transport of Martian Dust by the Ingenuity Helicopter Rotor Downwash as Observed by High‐Speed Imaging From the Perseverance Rover

et al. 2022

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“…Additionally, it has cameras with macro lenses able to make images that should resolve individual sand grains and constrain their shape and size. Although Dragonfly does not have a robot arm, it can interrogate the surface by applying controlled levels of aerodynamic shear stress via downwash from its rotors (Rabinovitch et al., 2021), and its Dragonfly Geophysics and Meteorology (DraGMet) instrument has photodiode sensors to detect the shadows of blowing sand and dust, and electric field sensors that might indicate grain charging, as well as sensors to measure the wind. Qualification tests of this instrumentation in a new Titan atmosphere chamber at the Johns Hopkins Applied Physics Laboratory may afford an opportunity to study saltation at pressure and temperature conditions somewhat faithful to Titan (albeit with Earth gravity).…”

Section: Figurementioning

confidence: 99%

Sand Transport on Titan: A Sticky Problem

Lorenz

2022

Geophysical Research Letters

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Saturn's moon Titan has the only solid surface in the solar system today, apart from our own, on which it rains. As any child on a beach knows well, the presence of even small amounts of dampness can drastically change the mechanical properties of sand by providing cohesion between the grains. A penetrometer instrument on the Huygens probe in 2005 determined that the streambed sediments at the landing site were damp (Atkinson et al., 2010), data initially interpreted (in)famously as indicating mechanical properties similar to crème brûlée (e.g., Lorenz, 2020). Some new work (Comola et al., 2022) explores the implications of possibly enhanced cohesion of the carbon-rich sands elsewhere on Titan.Ironically, it was the expected presence of moisture (in frigid Titan's case, liquid methane) that might immobilize sand that led this author to speculate before Cassini (Lorenz et al., 1995) that sand dunes would not be found. That prediction was spectacularly inaccurate, since more than 10% of Titan's surface proved to be draped with giant dark sand dunes, hundreds of km long and in some places over 100 m high. This welcome surprise arose from Titan's climate, which segregates the methane moisture into polar lakes and seas, dessicating the equatorial regions.This drying of low latitudes is a feature that was quickly found in Global Circulation Models (GCMs) of the climate (and in part results from Titan's slow 16-day rotation). Much subsequent work focused on attempting to relate the dune morphology (linear, e.g., Radebaugh et al., 2010) and orientation (indicating sand migration toward the east, e.g., Lorenz and Radebaugh, 2009) to the wind patterns predicted in these models.After some initial confusion, in that the average winds at low latitudes should be toward the west, the big-picture agreement of models with the pattern has been satisfying. In fact, the dominant near-surface flows are seasonally alternating north-south winds-such bidirectional flows in abundant sands give the linear morphology. The wrinkle is that during the stormy equinox seasons (around 2009, 2024, and 2039) occasional storms pull down the prograde eastwards flow from higher altitudes, and so the winds that are strong enough to move sand are biased in that direction, even though the usual (gentler) winds are biased westwards. While the details differ between models (e.g., Charnay et al., 2015;Lucas et al., 2014;Tokano, 2010) the general picture seems secure. Since 100 m dunes take millennia to grow and change, the details of the dune pattern may also carry some memory of recent Croll-Milankovich climate cycles (e.g., Ewing et al., 2015;McDonald et al., 2016).In part stimulated by the Titan discoveries, models of dune formation, migration, and growth have been studied extensively in the last decade. In particular, it has been recognized that limited sand supply (which may just be a manifestation of higher cohesion limiting transport) can lead to a dune alignment (Courrech du Pont et al., 2014) that is quite different from the traditional one for a...

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“…New applications of Unmanned Aerial Vehicle (UAV or drones) swarms are developed nearly every day for different problems, such as crop monitoring [1,2], forestry activities [3], space exploration [4,5], or military and rescue missions [6]. The main reason for that popularity lies in the advantages offered by UAVs, such as low cost, great maneuverability, safety, and convenient size for certain kinds of maneuvers [7].…”

Section: Introductionmentioning

confidence: 99%

UAV swarm path planning with reinforcement learning for field prospecting

2022

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There has been steady growth in the adoption of Unmanned Aerial Vehicle (UAV) swarms by operators due to their time and cost benefits. However, this kind of system faces an important problem, which is the calculation of many optimal paths for each UAV. Solving this problem would allow control of many UAVs without human intervention while saving battery between recharges and performing several tasks simultaneously. The main aim is to develop a Reinforcement Learning based system capable of calculating the optimal flight path for a UAV swarm. This method stands out for its ability to learn through trial and error, allowing the model to adjust itself. The aim of these paths is to achieve full coverage of an overflight area for tasks such as field prospection, regardless of map size and the number of UAVs in the swarm. It is not necessary to establish targets or to have any previous knowledge other than the given map. Experiments have been conducted to determine whether it is optimal to establish a single control for all UAVs in the swarm or a control for each UAV. The results show that it is better to use one control for all UAVs because of the shorter flight time. In addition, the flight time is greatly affected by the size of the map. The results give starting points for future research, such as finding the optimal map size for each situation.

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Scaling sediment mobilization beneath rotorcraft for Titan and Mars

Cited by 8 publications

References 21 publications

Lifting and Transport of Martian Dust by the Ingenuity Helicopter Rotor Downwash as Observed by High‐Speed Imaging From the Perseverance Rover

Lifting and Transport of Martian Dust by the Ingenuity Helicopter Rotor Downwash as Observed by High‐Speed Imaging From the Perseverance Rover

Sand Transport on Titan: A Sticky Problem

UAV swarm path planning with reinforcement learning for field prospecting

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