Constraints on the behavior of a descending ice probe due to force balance

Cassler, Bailey; Smith, Miles W.E.; Durka, Michael J.; Furst, Benjamin; Hockman, Benjamin; Ullman, Michael

doi:10.1016/j.actaastro.2021.09.002

Cited by 4 publications

(4 citation statements)

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“…Various authors have used the singular term efficiency to describe either the design efficiency (Aamot 1967b;Ulamec et al 2007), the operational efficiency (Cassler et al 2021;Durka et al 2022), or the product of the two (Schüller & Kowalski 2019). We have taken care to distinguish between the alternate definitions in this paper and to recognize that the total efficiency is the product of the two terms.…”

Section: Modeling Backgroundmentioning

confidence: 99%

“…The most optimal configuration (ò → 1) is achieved when the probe hull temperature approaches the melting temperature, with just enough additional warmth to enable a gap that renders viscous drag negligible (Cassler et al 2021). That leads to the minimum possible heat for a given speed, as is demonstrated by perturbing the solution, which increases the size of the 273.15 K boundary and the integral of the flux across it.…”

Section: A Closer Look At Operational Efficiencymentioning

confidence: 99%

“…As will be seen in Section 6.3, ò can be close to unity provided that the distribution of heat between the forward direction and probe sidewall is optimized. The operational efficiency cannot be strictly equal to unity because a thin layer of water is required around the probe circumference in order that its weight can overcome viscous forces (Cassler et al 2021), but this is in practice a small effect (Durka et al 2022).…”

Section: Modeling Backgroundmentioning

confidence: 99%

“…The melt jacket surrounding the probes has been modeled to be a source of operational inefficiency (Schüller & Kowalski 2019;Cassler et al 2021;Durka et al 2022), and we conjectured that the model disagreements are primarily due to this effect. For the limited cases where the high-fidelity 2D-IceOnly model shows agreement with the experiment, we further conjectured that the water jacket was much thinner, leading to better agreement with the fundamental model assumptions (ò ≈ 1).…”

Section: The Inadequacy Of Ice-only Models To Explain the Experimenta...mentioning

confidence: 99%

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Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds

Pereira

Durka

Hogan³

et al. 2023

Planet. Sci. J.

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Accessing the potentially habitable subsurface waters of Ocean Worlds requires a robotic ice probe (or “cryobot”) to traverse tens of kilometers of ice with temperatures ranging from ∼100 to 273 K. Designing and planning such a mission requires understanding ice probe behavior as a function of the local environment and design parameters. We present experimental results of two laboratory melt probes in cryogenic (79 K) and warm (253 K) ice. The melt probe tested in warm ice had multiple adjustable heaters, enabling optimization of the system's efficiency. The melt probes tested in cryogenic ice operated in vacuum and had internal tether spools, allowing for experimental confirmation of hole closure and the creation of a pressurized pocket with liquid water around the probe. These melt probes were tested at power levels ranging from 120 to 1135 W, achieving descent speeds between 5.3 and 59 cm hr−1. By analyzing the relationship between power and speed using analytical and high-fidelity numerical models, we demonstrate progress in understanding melt probe performance. We distinguish between the previously confounding terms of probe operational inefficiency and analytical model inaccuracy, allowing us to understand the range of applicability of the analytical models and demonstrate the importance of controlling heat distribution in cryobot design. The validated models show that while numerical models may be required to describe the behavior of short probes descending in limited-size laboratory test beds, the performance of efficient cryobots designed for operation on Ocean Worlds can be predicted by analytical models within 5% error.

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