© GMV, 2016 Property of GMV All rights reserved Model Validation Framework for Launchers: Post-Flight Performance Analysis 6 TH INTERNATIONAL CONFERENCE ON ASTRODYNAMICS TOOLS AND TECHNIQUES

© GMV, 2016 Property of GMV All rights reserved MVFLAU Overview 6 th ICATT – MVFLAU

© GMV, 2016 MVFLAU Overview Objective –Development and integration of a set of SW tools that implements those techniques that allow exploiting all the valuable data collected during the first flights and to characterize all those parameters that impacts on the GNC system performance Steps –Measurement Pre-processing –Trajectory Reconstruction –Parameter Estimation –Model Validation Target application: VEGA Flight Programme Software Alternative 2016/03/15Page 36 th ICATT - MVFLAU

© GMV, 2016 Property of GMV All rights reserved MVFLAU Architecture 6 th ICATT – MVFLAU

© GMV, 2016 MVFLAU Architecture: SW Elements 2016/03/15Page 56 th ICATT - MVFLAU

© GMV, 2016 MVFLAU Architecture: Data In-flight measurements: –Digital Message 22 at 25 Hz with: IMU measurements TVC deflection commands –Digital Message 1 at 25 Hz with the RCT activation commands –Tracking data. Simulated radar measurements at 1 Hz based on LV reference position. –Combustion chamber pressure measurements. Simulated measurements at 25 Hz based on reference SRM thrust profiles A-priori LV models: Aerodynamic database Environment models: Kourou Atmospheric model 2016/03/15Page 66 th ICATT - MVFLAU

© GMV, 2016 Property of GMV All rights reserved MVFLAU Results 6 th ICATT – MVFLAU

© GMV, 2016 Measurement Pre-processing MVF-MP-IMU: IMU Measurement Pre-processing Pre-processing steps: –Application of digital filter –Numerical differentiation –Reference frame transformation Results –Attitude quaternion, angular rate and angular acceleration. –Non-gravitational velocity vector in Equatorial Reference Frame –Non-gravitational acceleration vector in LV BF Reference Frame Conclusions and recommendations –The application of the digital filter minimizes the impact of the quantization step –Availability of angular rate measurements should be explored to improve the rotational state reconstruction. –Availability of acceleration measurements should be explored to improve the reconstruction of the non-gravitational acceleration 2016/03/15Page 86 th ICATT - MVFLAU

© GMV, 2016 Trajectory Reconstruction MVF-TR-EKF: Trajectory Estimation Estimation steps: –Prediction or propagation step based on pre-processed IMU velocity increments, i.e. inertial propagation –Correction step based on tracking data measurements Results –Position errors in NED reference frame smaller than (2000.0, , 750.0) m –Velocity errors in NED reference frame smaller than (2.0, 1.0, 2.0) m/s Conclusions and recommendations –Trajectory estimation performances are driven by the availability of additional measurements to be considered in the correction step –Trajectory estimation performances if no additional measurements are available depend on the IMU velocity measurements, i.e. type of IMU 2016/03/15Page 96 th ICATT - MVFLAU

© GMV, 2016 Trajectory Reconstruction MVF-TR-RTS: Trajectory Reconstruction Application of RTS smoother to the estimated trajectory Results –Improved reconstructed trajectory for the periods where tracking data are available. 2016/03/15Page 106 th ICATT - MVFLAU

© GMV, 2016 Model Characterization MVF-AVUM: AVUM Main Engine AVUM Main Engine characterization –Estimation of mass flow rate and specific impulse based on non- gravitational acceleration profile. –Rise phase: Linear fit of the mass flow rate. –Steady phase: LSQ applied to non-gravitational acceleration profile to estimate mass flow rate during this phase and specific impulse. –Decay phase: Mass flow rate reconstructed based on a-priori model for the decay phase. Conclusions –The characterisation of the AVUM main engine during the steady phase is performed by means of the estimated mass flow rate and specific impulse –The selected mass flow rate model for the complete AVUM main engine actuation provides good results for the reconstructed LV mass 2016/03/15Page 116 th ICATT - MVFLAU

© GMV, 2016 Model Characterization 2016/03/15Page 126 th ICATT - MVFLAU

© GMV, 2016 Property of GMV All rights reserved Conclusions 6 th ICATT – MVFLAU

© GMV, 2016 Conclusions Measurement Pre-processing –Availability of IMU acceleration and angular rate measurements Trajectory Reconstruction –Good performances of the proposed technique based on the availability of additional measurements to complement IMU Model Characterization –AVUM ME model based on reconstructed acceleration –Aerodynamic variables based on reconstructed trajectory and a-priori AEDB –Availability of acceleration and angular rate measurements to improve characterisation of aerodynamic model 2016/03/15Page 146 th ICATT - MVFLAU

© GMV, 2016 Property of GMV All rights reserved Thank you GMV