IceNode: a Buoyant Vehicle for Acquiring Well-Distributed, Long-Duration Melt Rate Measurements under Ice Shelves
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Zapien, Xavier↗
Engineering topics
Zapien, Xavier
Publications and source records attributed to Zapien, Xavier.
IceNode: A Buoyant Vehicle for Acquiring Well-Distributed, Long-Duration Melt Rate Measurements Under Ice Shelves
Antarctic ice shelves buttress the Antarctic Ice Sheet from sliding into the ocean and significantly raising global sea level. However, the accelerating dynamics of ice shelf melt in a warming environment are poorly understood, and the collapse of Antarctic ice shelves remains one of the largest sources of uncertainty in global sea level rise projections. The cavities below Antarctic ice shelves are notoriously difficult to access, making model-based hypotheses about the relationship between ocean warming and greater ice shelf melting difficult to verify because of a lack of in-situ data to constrain model parameters and examine key assumptions. We present early progress on IceNode, a novel vehicle under development at the NASA Jet Propulsion Laboratory designed to acquire well-distributed, concurrent, long-duration melt rate measurements under ice shelves. IceNodes are deployed as an array from a ship at the shelf edge, and use variable buoyancy to ride melt-driven exchange currents far into the cavity. Once underneath their target, they release a ballast weight to become highly positively buoyant and attach to the underside of the ice shelf, where they acquire in-situ measurements of basal melt rate directly at the ice-ocean interface for a year or more. Finally, IceNodes detach from their landing structure and use variable buoyancy to ride melt-driven exchange currents back to open water, where they surface and transmit their mission data home. IceNodes are designed to be relatively low-cost, expendable, and have simple logistics, enabling scientists to deploy scalable arrays that simultaneously measure co-varying ice shelf melt and ocean conditions over large spatial areas, thereby providing an unprecedented view of ice shelf melt rate variability and its drivers.
Zapien, Xavier↗
A GaN-based Four-Switch Buck-Boost Converter using Ripple Correlation Control for Maximum Power Point Tracking in Dynamic Deep Space Environments
As the demand for high-performance power conversion in spacecraft continues to grow and spacecraft mass and volume budgets become increasingly tight, it is essential to design DC-DC converters with higher efficiency and power density. Although photovoltaic (PV) efficiency has increased over time, solar irradiance and temperatures can fluctuate dramatically in deep space. This causes significant variations in the maximum power point (MPP) of the PV array, which can decrease the overall system efficiency unless accounted for. Thus, it is imperative to track the MPP of the PV panels to maintain optimal efficiency. This paper presents the experimental development of a four-switch, GaN-based buck-boost converter with an implementation of the Ripple Correlation Control (RCC) MPPT algorithm for dynamic deep space environments. Due to the use of GaN HEMTs, the experimental system achieves better efficiency and power density compared to the previous state of the art implementations. A simulation of the prototype buck-boost converter was implemented in SaberRD (Synopsis), and a digital design of the RCC-based MPPT controller utilizing the StateAMS tool is presented. The simulation results show that this controller swiftly and precisely converged to the MPP of the source PV panels in a dynamic solar irradiance condition.
Mahmud, Sadab↗