Propellant mass gauging under micro-gravity conditions is a challenging task due to the unpredictable position and shape of the fuel body inside the tank. Micro-gravity conditions are common for orbiting satellites and rockets that operate on limited fuel supplies. Capacitance sensors have been investigated for this task in recent years; however, the effect of various positions and shapes of the fuel body is not analyzed in detail. In this paper, we investigate this with various fill types, such as annular, core-annular, and stratified fills at different positions. We compare the performance among several curve-fitting-based approaches and a machine-learning-based approach, the latter of which offers superior performance in estimating the fuel content.

The use of capacitance sensors for fuel mass gauging has been in consideration since the early days of manned space flight. However, certain difficulties arise when considering tanks in microgravity environments. Surface tension effects lead to fluid wetting of the interior surface of the tank, leaving large interior voids, while thrust/settling effects can lead to dispersed two-phase mixtures. With the exception of capacitance-based sensing, few sensing technologies are well suited for measuring annular, stratified, and dispersed fluid configurations. Two modalities of capacitance measurement are compared – Electrical Capacitance Volume Tomography (ECVT) and Electrical Capacitance Volume Sensing (ECVS). ECVT is a non-invasive imaging modality first introduced in 2006. ECVS is a measurement modality introduced in this paper that is derived from ECVT technology but does not reconstruct an image as part of the mass measurement. To optimize the design of future capacitance-based spherical tank mass gauging sensors, different electrode plate layouts are evaluated in a mass gauging context. Prototype sensors are constructed, and experiments are conducted with fluid fills in various orientations. The plate layouts and their effect on the performance of the sensor as a fuel mass gauge are analyzed through the use of imaging and averaging techniques.

Microgravity mass gauging has gained increasing importance in recent years due to the acceleration in planning for long-term space missions as well as in-space refueling and transfer operations. It is of particular importance with cryogenic propellants where periodic tank venting maneuvers and leak detection place a special emphasis on accurate mass gauging. Several competing technologies have arisen, but capacitance mass gauging has several distinct advantages due to its low mass, non-intrusiveness, and whole volume interrogation technique. Capacitance based measurement has also seen recent success in measuring cryogenic liquid nitrogen and hydrogen volume fraction and flow rate, showing its compatibility with cryogenic propellants. However, the effects of gravity on fluid behavior make the calibration and testing of these sensors difficult on the ground. In this paper a prototype sensor is constructed that can emulate fluid positions in microgravity and earth gravity configurations. Experimental propellant fills and drains are conducted using a simulant fluid with similar electrical properties to cryogenic propellants. This expanded dataset is compared with previous simulation results and used to construct a machine learning model capable of calculating the fluid mass in tanks both with and without propellant management devices.