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Mike Fritzinger

Publications and source records attributed to Mike Fritzinger.

Post-Flight Reconstruction Approach for Space Launch System Artemis I Mission

Upon completion of the first Space Launch System flight, NASA personnel will begin post-flight analyses. Telemetry from across the vehicle will be combined with external radar tracking and environmental observation data in order to close validation criteria and generate a best estimated trajectory (BET). This paper will describe the approach taken by the SLS team to integrate flight data from multiple flights sources into BET, pre-flight simulation and testing results, primary sources of uncertainty, and path towards processing flight results. This paper also includes a brief description of algorithms and approaches to estimate as-flown vehicle parameters such as booster specific impulse, booster (and core) thrust multipliers and dry mass.

Evan John Anzalone

Post-flight Reconstruction for a Mars Ascent Vehicle

As the design of a Mars Ascent Vehicle as part of the Mars Sample Return Campaign continues to mature, new focus is placed on the ability to reconstruct and validate the as-flown performance in post-flight analysis. This is increasingly important as the vehicle considers alternate design concepts that offer significant mass reductions at a cost of reduced onboard sensing capabilities. To understand architectural impacts to validate as-inserted vehicle conditions, the MAV team has assessed several options for capturing this data either on the vehicle or using external tracking options. This paper summarizes potential architectures, their associated performance, and integration challenges.

Evan Anzalone

Streamlining GNC Architecture Development and FSW Integration for the Mars Ascent Vehicle

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls (GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.

Engineering