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DDS Data Analysis, II

DDS Data Analysis, II. Alberto Lobo ICE-CSIC & IEEC. Approach : 1) Apply controlled perturbation a to the system 2) Measure “ feed-through ” coefficient between force and perturbation:. 3) Measure actual a with suitable sensors

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DDS Data Analysis, II

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  1. DDS Data Analysis, II Alberto Lobo ICE-CSIC & IEEC DDS Data Analysis

  2. Approach: 1) Apply controlled perturbation a to the system 2) Measure “feed-through” coefficient between force and perturbation: 3) Measure actuala with suitable sensors 4) Estimate contribution of a by linear interpolation: 5) Substract out from total detected noise: 6) Iterate process for all identified perturbations Noise reduction philosophy Problem: to assess the contribution of a given perturbation to the total noise forcefint. DDS Data Analysis

  3. Various diagnostics items • Temperature and temperature gradients: • Sensors:thermometers at suitable locations • Control:heaters at suitable locations • Magnetic fields and magnetic field gradients: • Sensors:magnetometers at suitable locations • Control:induction coils at suitable locations • Charged particle showers (mostly protons): • Sensors: Radiation Monitor • Control: non-existent DDS Data Analysis

  4. General scheme for DDS DA (S2-IEC-TN-3031) • For each diagnostic: • Measurement runs • Controlled disturbance ON (if applicable) • Controlled disturbance OFF • Available data (in each case) • LTP-wide reference model • Data Analysis Procedures DDS Data Analysis

  5. Thermal 22 NTC temperature sensors 16 heaters DDS Data Analysis

  6. Thermal DDS Data Analysis

  7. Thermal Optical Window DDS Data Analysis

  8. Heater Thermal Optical Window Heaters DDS Data Analysis

  9. Thermal Optical Bench Temperature Sensors DDS Data Analysis

  10. Thermal Suspension Struts: Heaters and Sensors DDS Data Analysis

  11. The Problem • COMPLETED WORK: • DDS Heaters sizing (P, SNR, dT …) • OW heaters comparison with experiment • Modelling, analytic or SW tool • Others • Progress towards: – Quantify thermal gradients between any two points, given the DDS sensors measurements. DDS Data Analysis

  12. t = 1000 sec Heaters signal T1 T2 H1 H2 T3 T4 T1 T2 H1 H2 T3 T4 P Heater set 1 Heater set 2 t t EH heaters: activation scheme Sensors response (CGS SW tool) DDS Data Analysis

  13. Heaters ON: EH Measurements: Temperatures T1, T2, T3, T4 per IS Accelerations a1, a2 per IS Laser Metrology x1, D Main thermal signal: DT=(T1+T3) - (T2+T4)per IS Data Analysis: fit data to Transfer function temperature-acceleration ensues DDS Data Analysis

  14. Heaters ON: OW Measurements: Temperatures T5, T6 in IS1, T11, T12 in IS2 Laser Metrology x1 for IS1, x2= x1+ D for IS2 Thermal signals: temperature closest to activated heater Data Analysis: fit data to ARMA(2,1): • Should be OK in MBW –even beyond!–, and for each OW • Can easily be improved, if necessary, at lower frequencies DDS Data Analysis

  15. LCA Thermal Model, v8 S2-CGS-TN-3031 DDS Data Analysis

  16. Proposal: Frequency Sweep • WHAT: Evaluate transfer functions points at a fixed frequency y(n) = ystd (n) + ytrs (n) = = A exp (in+) H() + ytrs (n) x(n) = A exp (in ) n>0 y(n) = h(z) x(n) • HOW: Fitting exponential decay 2) Apply sinusoidal input, TJ = 1 · sin (0 t) 1) Select input node: node = J 3) Fit results on different node (K) peak values: yK, max (n) = A exp (- n / T) + C 4) Get transfer function node K wrt node J GKJ (0) = C DDS Data Analysis

  17. Hacking • FITTING: Simulation results sampled at the input frequency from the maximum in the last period to some selected point after transient. Substract initial value afterwards. • [a,b,c,da,db,dc] = F(y,Tin,node) 5 mHz 1 mHz 50 mHz DDS Data Analysis

  18. All heaters OFF • Temperature measurements to be translated into LTP • signals (TM accelerations and/or laser metrology phase shifts) • by transfer function scaling. Has ambiguities unless a suitable • model of the physical processes is available. • Cross correlations between different channels: • Some can be (safely) discarded, e.g. OW-EH, etc. • Others cannot, e.g., among different struts • Global LTP system identification • Some sensor readings used as housekeeping data, e.g., OB • and redundant OW sensors • Improved experimental characterisation needed and underway DDS Data Analysis

  19. Application: OB Sensors Location Input Sensors Sensors Sensors DDS Data Analysis

  20. Test masses are a AuPt alloy 0.7 Au + 0.3 Pt of low susceptibility a = 46 mm m = 1,96 kg and low remnant magnetic moment: Magnetic disturbances in the LTP • Magnetic noise is due to various causes: • Random fluctuations of magnetic field and its gradient • DC values of magnetic field and its gradient • Remnant magnetic moment of TM and its fluctuations • Residual high frequency magnetic fields DDS Data Analysis

  21. LCA DDS Data Analysis

  22. Magnetometer available areas DDS Data Analysis

  23. Magnetometers’ accommodation DDS Data Analysis

  24. Coil Accommodation DDS Data Analysis

  25. Philosophy: apply controlled periodic magnetic fields: Force comes then a two frequencies: Magnetic diagnostics: coils ON • B0 is calculated rather than measured with magnetometers • Bbg is LTP background magnetic field DDS Data Analysis

  26. Analysis: from above data we can obtain are measured with good SNR (~ 100 max) are measured with poorer SNR Magnetic diagnostics: coils ON • Data: • Laser Metrology x1 and x2= x1+ D for each VE being affected • a1 (a2) from IS1 (IS2) if possible • Coil feed intensity and frequency DDS Data Analysis

  27. Magnetic diagnostics: coils ON From Fx,2w we can estimate c to ~1% From Fy,2w and Fz,2w we get error correction and cross check • Fw can be useful to estimate remnant magnetisation M • This is more complicated, though: • Fx,w has (max) SNR ~ 100, but Fy,w and Fz,w quite less • Yet all three components are needed, as M is a vector • In addition, Mneeds to be disentangled from Bbg DDS Data Analysis

  28. Continuous magnetic field monitor Data: • 4 3-axis magnetometers at fixed positions in LCA • 12 sampled magnetic field channels • Magnetic field and gradient must be known at TM locations: • Magnetometer data streams are fed to suitable extrapolation algorithms • These algorithms are (so far) computationally demanding • To be run offline • They produce a magnetic field + gradient map around TMs • Magnetic map error estimates will be delivered by the algorithm, too • Processed data directly yield magnetic transfer function. • Extrapolation operation errors need tight control. DDS Data Analysis

  29. LCA and Magnetic sources • Magnetic sources: • Around 50 identified • Distributed outside LCA • Their positions are known • They behave mostly as • magnetic dipoles • Dipole moments are • unknown • Solar panel is no such DDS Data Analysis

  30. LCA and Magnetic sources DDS Data Analysis

  31. Magnetic field map DDS Data Analysis

  32. Magnetic field reconstruction • Exact reconstruction not possible with 4 magnetometers and • around 50 dipole sources (+solar panel) • Tentative approaches attempted so far: • Linear interpolation • Weighted interpolation –various schemes • Statistical simulation (“equivalent sources”) • Latest idea: Multipole field structure estimation: • Fully possible up to quadrupole approximation • Partly up to octupole –and tricky, but not impossible... DDS Data Analysis

  33. Multipole reconstruction theory In vacuum, A multipole expansion of B follows that corresponding to y(x): The coefficients alm(r) depend on the magnetisation M(x). In an obvious notation, structure is: DDS Data Analysis

  34. Somewhat legthy calculations lead to: with Multipole reconstruction theory • Evaluation of multipole terms is based on some assumptions: • Magnetisation is due to magnetic dipoles only • Such dipoles are outside the LCA. DDS Data Analysis

  35. Fit criterion is to minimise squared error: Multipole reconstruction theory Idea is now to fit measured field values to a limited multipole expansion model. Arithmetic sets such limit to quadrupole: DDS Data Analysis

  36. these 3 are uniform field components 3+5 = 8 < 12 => some redundancy, OK 5+7 = 12 exactly!! Multipole reconstruction theory Arithmetics of reconstruction algorithm: Data channels: 12 Mlm dipole: 3 Mlm quadrupole: 5 Mlm octupole: 7 • Summing up: • Full quadrupole structure up to quadrupole level –even redundant • Fully possible up to octupole if constant dipole can be determined • Errors in the order of 10% (TBC, could be better!) DDS Data Analysis

  37. Ideal error map L %TM1 %TM2 132.0 47.8 2 3.1 12.3 3 1.9 0.6 DDS Data Analysis

  38. Radiation Monitor From S2-IEC-TN-3031: ...The radiation monitor is primarily designed to help understand and quantify these variable processes [modulations of CGR and fluxes of SEP] by monitoring the external particle fluxes and allowing these to be correlated with the test-mass charge measurements. DDS Data Analysis

  39. Radiation Monitor DDS Data Analysis

  40. Radiation Monitor DDS Data Analysis

  41. Radiation Monitor DDS Data Analysis

  42. Radiation Monitor • Establish the charging-rate in the TMs due to cosmic-ray interactions. Compare with Monte Carlo simulations. Requires a long run with no UV lamps operating. • Establish the cosmic-ray transfer function from the radiation monitor to the test-mass charge. • Establish or limit the level of power spectral density of cosmic-ray modulations caused by solar activity. Provided by continuous operation of RM and other monitors available. • Establish the solar-energetic particle (SEP) flux enhancement distributions (temporal and fluence) seen by the radiation monitor. • Establish the solar-energetic particle transfer function from the radiation monitor to the test-mass charge. Done by cross-correlation of TM charge control data with RM (and other monitors) SEP data. • Estimate the solar-energetic particle induced charging rate and compare with simulations. • Demonstrate the closed loop charge control process and estimate its gain factor. DDS Data Analysis

  43. Radiation Monitor Radiation Monitor data are formatted in a histogram-like form. A histogram is generated and sent (to OBC) every 614.4 sec. DDS Data Analysis

  44. Radiation Monitor • Additional data required: • Test mass charges, Q1 and Q2 every 1000-10,000 seconds to an accuracy to 104 elementary charges with sign. • ULU time status including lamps on/off and commanded UV levels • Inertial sensor noise power spectra • RM calibration data – channel to energy conversion • RM calibration data – efficiency factors for each spectral channel • RM calibration data – spectral resolution as function of energy • Updated satellite geometry model • Solar activity indicators DDS Data Analysis

  45. End of Presentation DDS Data Analysis

  46. Radiation Monitor GCR SEP DDS Data Analysis

  47. Radiation Monitor DDS Data Analysis

  48. Radiation Monitor • Data handling issues: • Front detector hits sent as flags • Coincident events sent as energy deposed • Electronics is able to cope with up to 5000 c/s, • so data compression will be eventually needed. • Testing issues: • Artificially generated pulses • Muon test • Proton source exposition: PSI, end of October DDS Data Analysis

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