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John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 1 Estimation of the LISA TM-to-release tip adhesion force during dynamic separation.

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Presentation on theme: "John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 1 Estimation of the LISA TM-to-release tip adhesion force during dynamic separation."— Presentation transcript:

1 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 1 Estimation of the LISA TM-to-release tip adhesion force during dynamic separation John W. Conklin Stanford University Matteo Benedetti, Daniele Bortoluzzi, Carlo Zanoni University of Trento

2 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 2 Test Mass Caging & Release LISA GRS Impact factor: 2 kg TM  4 mm gap = 810 –3 kg m  Caging required GRS electrostatic force (5 μN) << Au adhesion force Solution: quick retraction, relying on the TM inertia *Bortoluzzi et al (2010)

3 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 3 LISA Test Mass Release Phase TM residual velocity must be < 5 μm/s Caging & Vent Mechanism final stage designed to minimize the residual velocity and consists of two opposing tips Test Mass LISA Caging System Grabbing Positioning and Release Mechanism (GPRM)

4 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 4 Testing Release Phase in the Lab Goal: Determine impulse imparted to TM during dynamic rupture of adhesive bond in representative conditions of the in-orbit environment On-orbit no contributions of shear (pre-)stress at the contact patch that may promote the adhesion rupture adhesion Release tip Quick retraction of the release tip Dynamic failure of adhesion

5 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 5 Transferred Momentum Measurement Facility On-orbit release (double-sided) Lab simulation (single-sided)

6 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 6 Transferred Momentum Measurement Facility On-orbit release (double-sided) Lab simulation (single-sided)

7 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 7 Adhesion Force Data Reduction Force-vs-elongation, F ad (e), function models adhesion phenomenon Can be transformed to on-orbit conditions (mass, release profile, …) Experimental results show that systematics dominate Statistical approach adopted to bound in-flight release Interferometer measures TM (insert) position, x I Release tip motion, x S, measured separately

8 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 8 Adhesion Force Model Adhesion force modeled as non-linear spring F ad = k ad  x where  x = x P – x I Initial model was empirical: Current model is more general: Consistent with single-contact Johnson Kendall Roberts theory extended to multi-contact (rough) surfaces by Fuller & Tabor

9 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 9 TM Release Data (medium 100 g TM) Unexpected oscillations

10 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 10 Parameter Estimation Model: Estimation algorithm: Levenberg-Marquardt A priori used for initial velocity and preload Measurement noise includes: Interferometer noise:  = 0.9-1.2 nm Uncertainty in measured positioner motion:  = 5.8 nm Unmodeled non-Gaussian behavior of residuals x I = h(t, p, x S ) + w x I = measured TM insert motion h = nonlinear model p = 7 parameters to be estimated x S = measured stage motion w = measurement noise

11 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 11 Fit and Residuals Example best-fit Post-fit residuals

12 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 12 Adhesion Force Estimates

13 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 13 In-flight Monte Carlo Simulations Due to nonlinearities, Monte Carlo method adopted to estimate confidence interval for in-flight release velocity GPRM release dynamics Measured by RUAG Schweiz No adhesion present Mathematical model of GPRM fit to measurements Parameter estimates & covariances feed Monte Carlo simulation of in-flight scenario GPRM electro-mechanical model

14 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 14 Data Set 1Data Set 2Data Set 3 Estimated max (3) velocity (μm/sec) 1.91.11.6 Margin of safety w.r.t 5 μm/sec requirement 2.64.73.1 See Poster by Carlo Zanoni et al Number of trials Results

15 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 15 Backup slides …

16 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 16 Parameters Estimation Adhesion force parameters: A, B, p Time lead/lag between measured insert motion and measured translation stage motion At time t i, x I = x I (i) and x P = x P (i + ∆t f s ) Initial velocity of TM, insert, plunger: v 0 TM/insert transition from stick to slip: x I = x s l i c k Plunger preload (defines, x T0, x I0, x P0 ): F p r e a priori = 0.5 mN  0.1 mN

17 John W. Conklin, 9 th International LISA Symposium, Paris, 23 May 2012 17


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