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Beck, Roger

Publications and source records attributed to Beck, Roger.

SLS Trade Study 0058: Day of Launch (DOL) Wind Biasing

SLS heritage hardware and legacy designs have shown load exceedances at several locations during Design Analysis Cycles (DAC): MPCV Z bending moments; ICPS Electro-Mechanical Actuator (EMA) loads; Core Stage loads just downstream of Booster forward interface. SLS Buffet Loads Mitigation Task Team (BLMTT) tasked to study issue. Identified low frequency buffet load responses are a function of the vehicle's total angle of attack (AlphaTotal). SLS DOL Wind Biasing Trade team to analyze DOL wind biasing methods to limit maximum AlphaTotal in the M0.8 - 2.0 altitude region for EM-1 and EM-2 missions through investigating: Trajectory design process; Wind wavelength filtering options; Launch availability; DOL process to achieve shorter processing/uplink timeline. Trade Team consisted of personnel supporting SLS, MPCV, GSDO programs.

Decker, Ryan K.↗

IRVE-3 Post-Flight Reconstruction

The Inflatable Re-entry Vehicle Experiment 3 (IRVE-3) was conducted from the NASA Wallops Flight Facility on July 23, 2012. Launched on a Black Brant XI sounding rocket, the IRVE-3 research vehicle achieved an apogee of 469 km, deployed and inflated a Hypersonic Inflatable Aerodynamic Decelerator (HIAD), re-entered the Earth's atmosphere at Mach 10 and achieved a peak deceleration of 20 g's before descending to splashdown roughly 20 minutes after launch. This paper presents the filtering methodology and results associated with the development of the Best Estimated Trajectory of the IRVE-3 flight test. The reconstructed trajectory is compared against project requirements and pre-flight predictions of entry state, aerodynamics, HIAD flexibility, and attitude control system performance.

Olds, Aaron D.↗

The Best Estimated Trajectory Analysis for Pad Abort One

I. Best Estimated Trajectory (BET) objective: a) Produce reconstructed trajectory of the PA-1 flight to understand vehicle dynamics and aid other post flight analyses. b) Leverage all measurement sources taken of vehicle during flight to produce the most accurate estimate of vehicle trajectory. c) Generate trajectory reconstructions of the Crew Module (CM), Launch Abort System (LAS), and Forward Bay Cover (FBC). II. BET analysis was started immediately following the PA-1 mission and was completed in September, 2010 a) Quick look version of BET released 5/25/2010: initial repackaging of SIGI data. b) Preliminary version of BET released 7/6/2010: first blended solution using available sources of external measurements. c) Final version of BET released 9/1/2010: final blended solution using all available sources of data.

Kutty, Prasad↗

Tests and Techniques for Characterizing and Modeling X-43A Electromechanical Actuators

A series of tests were conducted on the electromechanical actuators of the X-43A research vehicle in preparation for the Mach 7 and 10 hypersonic flights. The tests were required to help validate the actuator models in the simulation and acquire a better understanding of the installed system characteristics. Static and dynamic threshold, multichannel crosstalk, command-to-surface timing, free play, voltage regeneration, calibration, frequency response, compliance, hysteretic damping, and aircraft-in-the-loop tests were performed as part of this effort. This report describes the objectives, configurations, and methods for those tests, as well as the techniques used for developing second-order actuator models from the test results. When the first flight attempt failed because of actuator problems with the launch vehicle, further analysis and model enhancements were performed as part of the return-to-flight activities. High-fidelity models are described, along with the modifications that were required to match measurements taken from the research vehicle. Problems involving the implementation of these models into the X-43A simulation are also discussed. This report emphasizes lessons learned from the actuator testing, simulation modeling, and integration efforts for the X-43A hypersonic research vehicle.

Lin, Yohan↗

The X-43A Six Degree of Freedom Monte Carlo Analysis

This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A inflight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.

Baumann, Ethan↗

The X-43A Six Degree of Freedom Monte Carlo Analysis

This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A in-flight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.

Baumann, Ethan↗