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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.

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Evaluation of Surlyn 8920 as PHE Visor Material and Evaluations of New Adhesives for Improving Bonding Between Teflon and Stainless Steel at Cryogenic Temperature

Two studies are presented, and in the first study, Surlyn 8920 (an ionic and amorphous low density polyethylene made by Dupont) was evaluated as a possible replacement of Plexyglass G as PHE visor material. Four formulations of the polymer were made by adding different amounts of UV stabilizer, energy quencher, and antioxident in a Brabender Plasticorder. The formulated polymers were molded in the form of sheets in a compression molder. Cut samples from the molded sheets were exposed in a weatherometer and tested on Instron Tensile Tester for strength and elongation. Specially molded samples of the formulated polymers were subjected to Charpy Impact Tests. In the second study, preliminary evaluations of adhesives for improvement of bonding between Teflon and stainless steel (SS) were performed. Kapton, a high temperature polyimide made by Dupont, and a rubber based adhesive made by Potter Paint Co., were evaluated against industrial quality epoxy, the current material used to bond Teflon and SS. The degreased surfaces of the SS discs were etched mechanically, with a few of these etched chemically. The surfaces of the SS discs were etched mechanically, with a few of these etched chemically. Bonding strengths were evaluated using lap shear tests on the Instron Tensile Tester for the samples bonded by Kapton and industrial quality epoxy. Bond strengths were also evaluated using a pull test on the Instron for the samples bonded by Potter adhesive (CWL-152) and industrial quality epoxy. Based on limited lap shear data, Kapton gave bond strength favorable compared to that of industrial epoxy. Based on limited pull test data, Kapton bonded and CWL-152 bonded samples showed poor strength compared to epoxy bonded sample.

Ray, Asit K.↗

LIPA: Lunar Ice Perception Algorithm

The highest concentration of Lunar water-ice stores exists within the Permanently Shadowed Regions (PSRs) of the Lunar South Pole. As such, the ability to locate in situ water-ice stores in an accurate, systematic, and safe manner will prove vital for future Lunar activities which rely on hydrogen-based resources. Here we show how the strong absorptive properties of ice can be exploited (by coupling robotics, infrared imaging techniques, and machine learning) so that surface frost located in PSRs can be easily differentiated from the surrounding frozen regolith. Testbeds which simulate an icy lunar landscape were created and then imaged using a mid-wave infrared (MWIR) camera system mounted to a robotic arm (UR5e). Testbeds were imaged under two filter modes (1) wide band mode: whereby imagery captured filled a spectral range of 3.0 - 5.0 μm and (2) narrow band mode: whereby imagery captured were confined to a single central wavelength (CWL) of 3.15 ± 0.03 μm. A CWL of 3.15 μm was chosen due to the highly absorptive nature of ice at that specific wavelength. Images produced under both camera modes were processed in MATLAB. Narrow band images (NB) were subtracted from their wide band (WB) counterparts to produce differenced images (DI) which clearly demonstrated the spatial extent of ice (e.g., WB – NB = DI). Differenced images were used to train a Microsoft Azure model to discriminate between frozen regolith which did and did not contain ice. These works prove promising for future in situ resource utilization (ISRU) missions which employ robotics in combination with camera systems to advance science objectives (e.g., locate water-ice in frozen regolith) on the lunar surface.

Ane Slabic↗

LIPA: Lunar Ice Perception Algorithm

Introduction: The highest concentration of Lu-nar water-ice stores exists within the Permanently Shadowed Regions (PSRs) of the Lunar South Pole [1-3]. As such, the ability to locate in situ water-ice stores in an accurate, systematic, and safe manner will prove vital for future Lunar activities which rely on hydrogen-based resources. Here we show how the strong absorptive properties of ice can be exploited so that surface ice located in PSRs can be easily differentiated from the surrounding frozen regolith. Testbeds which simulate an icy lunar landscape were created and then systematically imaged using a mid-wave infrared (MWIR) camera system. Testbeds were imaged under two filter modes (1) high-absorption (high-abs) mode: whereby imagery captured were confined to a single central wave-length (CWL) of 3.15 ± 0.03 μm and (2) low-absorption (low-abs) mode: whereby imagery cap-tured were confined to a single CWL of 3.80 ± 0.04 μm (Figure 1). High- and low-absorption modes are related to the absorptive properties of ice at each selected wavelength, respectively. Corresponding images from each filter mode were differenced (i.e., pixels were subtracted) to enhance contrast between ice-bearing and non-ice-bearing pixels, and then fed into a semantic segmen-tation model. The model was trained to detect and differentiate between water, ice, shadows, and lunar regolith. Results: Modeling results accurately discrimi-nated ice from other materials (such as frozen lunar regolith) and were used to visually resolve the spa-tial extent of surface ice. Further, outputs produced through semantic segmentation were used to estimate water-ice contents in collected imagery [(Pixels with Class = “Water Ice”)/(Sum of Pixels)*100]. Summary: These works prove promising for future in situ resource utilization (ISRU) missions which employ robotics in combination with infrared camera systems to advance science objectives (e.g., locate water-ice in frozen regolith) on the lunar sur-face. References: [1] Cannon K. M., Deutsch A. N., Head J. W., and Britt D. T. (2020) Geophysical Re-search Letters, 46, e2020GL088920. [2] Honniball C. I. et al. (2021) Nature Astronomy 5, no. 2, 121-127. [3] Li S. et al. (2018) Proceedings of the National Academy of Sciences, 115(36), 8907-8912.

A. Slabic↗