Modeling a class of thermal ice probes for accessing the solar system’s ocean worlds

Durka, Michael J.; Smith, Miles W.E.; Ullman, Michael; Cassler, Bailey; Otis, Richard; Cwik, T.; Hockman, Benjamin; Barry, Matthew

doi:10.1016/j.actaastro.2021.12.018

Cited by 2 publications

(8 citation statements)

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“…In the column of ice directly below the cryobot, the length scale for diffusion, α 0 /v, is much smaller than the system radius, R, suppressing radial diffusion. These suppressed terms are neglected in the derivation of the analytical Aamot models, as discussed in Durka et al (2022). Figure 13 explores the agreement between the Aamot models with the numerical iceonly model as a function of this factor.…”

Section: Discussionmentioning

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%

“…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 model is considered analytical, although it includes a closed-form integral that requires numerical evaluation. A full derivation is given in Durka et al (2022) and the approximating assumptions are enumerated, including that (a) the thermophysical properties of ice are treated as temperature independent, (b) the cryobot has a narrow aspect ratio and is moving relatively quickly-allowing end effects in the heat equation to be neglected-and (c) the presence of the melt jacket around the probe is neglected, along with its modification to the system heat balance. Approximations (a) and (b) enable an analytic estimation of η 0 , while approximation (c) neglects the liquid phase, so that ò = 1.…”

Section: Modeling Backgroundmentioning

confidence: 99%

“…Under the Aamot approximation of temperature-independent material properties, the latter is a linear function of far-field temperature, u c = ρc P (T 0 − T ∞ ), where ρ is the ice density and c P the heat capacity. The function 1 ( ) t  is the Jaeger integral of the first kind, as described in Durka et al (2022), and the parameter τ is a dimensionless inverse speed, first introduced in Aamot (1967b) and defined by…”

Section: Modeling Backgroundmentioning

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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Section: Discussionmentioning

confidence: 99%

Section: Modeling Backgroundmentioning

confidence: 99%