August 22, 2014 – GO FET Internal Environment Data Analysis

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This week I have been busy processing data from the successful first flight of the Generation Orbit Flight Experiment Test Bed (GO FET). In addition to extracting and processing data from the experiments, we have been processing data from environmental sensors to aid in the preparation of our soon to be released GO FET payload users guide. Prior to the flight we based our hardware design on rough estimates of the expected internal environment, and applied large factors of safety to all our designs.

Two graphs generated while post processing our data are shown below. They show the measured temperature of an experiment inside the pod and the measured pressure, as well as external static and stagnation conditions estimated based on the international standard atmosphere and gps data. Prior to the flight we had predicted that conditions inside the pod would be very similar to those at stagnation, however at first glance this does not appear to be the case: although the pressure measured inside the pod shows the same trends as the stagnation pressure, they show significant differences in value. This is because the ambient conditions on a hot day in Florida are different from those assumed by reference atmospheres, as is apparent in the temperatures measured inside the pod while sitting on the runway while the aircraft was fueled prior to takeoff. We were able to confirm that the internal temperature and pressure are indeed the very close to the stagnation temperature and pressure by using looking at the difference in pressure between pressure sensors located at different points in the pod were the cross section the airflow was forced through had different diameters. Knowing this allows us to predict the expected temperature and pressure environment based on location, flight profile, and time of year for future flights.

Prior to the flight we also tried using published techniques for estimating vibration environments in external stores which take into many parameters including: store location on wing, aircraft engine locations and thrust, and more. In addition to using the vibration data we gathered for the payload user guide, we are also using it to check the predictions of these models and help inform future engineering decisions for store integraiton.