LISA Pathfinder is a technology demonstration mission aimed at testing the free-fall purity level of a reference mass (or test mass, TM). Such an object can be used as a geodesic tracker for the detection of gravitational waves and is the core of the Gravitational Reference Sensor (GRS). The low acceleration noise level pursued for the tracker sets tight design constraints on the GRS. Any non-gravitational force has to be reduced to the level of 10 fN in the measurement bandwidth (1-30 mHz). The TM is firmly constrained by the Caging and Venting Mechanism (CVM) during the spacecraft launch and is then handed over to the Grabbing Positioning and Release Mechanism (GPRM). This mechanism is designed to handle the TM during in-orbit operations and inject it into the geodesic trajectory (or free-fall condition) by means of a quick retraction of two release tips on two opposed sides of the TM. After these operations, the TM residual velocity must be below 5 μm/s, otherwise the GRS capacitive position control is not able to capture and centre the TM in its electrode housing. Due to this criticality, the requirement on the maximum release velocity is verified on-ground both by means of an experimental apparatus (the Transferred Momentum Measurement Facility, TMMF) and by means of simulations. The TMMF is specifically designed to assess the contribution of adhesion between TM and release tips to the total TM momentum. On top of this contribution, any deviation from a symmetrical action of the two opposed release mechanisms determines an extra transferred momentum. Great care must be taken to correlate the experimental results with the in-flight conditions, where a different actuator is used to perform the TM injection. The extrapolation of the experimental results to in-flight conditions and the Montecarlo simulation of the GPRM-TM combined dynamics show the presence of a 2.4 margin factor with respect to the 5 μm/s requirement. ∗carlo.zanoni@unitn.it The on-ground measured and simulated performance of the injection phase, its challenges and critical points are here presented and discussed. For the first time, in this paper, the simulations take advantage of the large amount of data available from the testing campaigns.
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