Under contract to SAIC, AI is providing engineering support to characterize pre-defined flight control systems and hardware in order to design, build, and integrate all harnessing for Avionics data and power systems as well as Engine power systems. AI has also characterized control surface actuators in order to design, built, and integrate harnessing for all control surface actuator power and data systems. All as-build harnessing documentation was created by AI using OrCAD 10.
In addition, AI has developed the Skybus30K Ballonet Pressurization system currently used to maintain proper envelope pressure during both ground and flight operations. The Pressure Monitor System is a dedicated microcontroller based system designed to maintain air and helium pressure within specified limits.
Finally, AI developed a dedicated microcontroller based Flight Termination System designed as an independent system to terminate the Skybus balloon flight. The unit controls an FTS Helium Valve and the engine kill/ignition via relays.
Sensor Test for Orion RPOD (Renezvous Proximity Operations and Docking) Risk Mitigation (STORRM) was a joint JSC/LaRC project. It was a quick (two -year from initiation to launch) Development Test Objective (DTO) payload that flew on STS-134 in May 2011, to test the Orion docking camera and Vision Navigation Sensor on a realistic approach to the ISS. Retroreflectors were installed on the docking hatch of the ISS on a previous mission. AI was instrumental in the development of the avionics which controlled and recorded all data for the Ball Vision Navigation Sensor Lidar and Lockheed Martin provided High-resolution (4K) Docking Camera. The mission was a great success, providing 3D images of the ISS during the Orbiters approach. Tom Johnson was awarded a ‘Silver Snoopy’ for his part in the mission. Details of the mission can be found here: https://www.eoportal.org/other-space-activities/iss-storrm#vns-vision-navigation-sensor .
AI was instrumental in developing the test setup, running the tests, and reducing the data for deployables testing. This testing was used to verify impact forces and adjust damper rates over the expected range of flight temperatures for the solar arrays and antenna deployments.
Under subcontract to Orbital Sciences Corp, AI was selected to both develop and assist in running the magnetics testing for ROCSAT-3. Attention was given to four main area of magnetic concern: Hard dipoles, Soft dipoles, Current loop testing, and Spacecraft Magnetometer Calibration. AI also fabricated the non-magnetic tilt fixture for the spacecraft measurements in the Goddard Space Flight Center Spacecraft (42’) Magnetic Test Facility (SMTF). Visit the official site for more information.
AI’s initial role on Vegitation Canopy Lidar (VCL) supported the development of the Attitude Determination and Control System (ADACS). Our engineering support has been instrumental in the following areas.
AI developed the ADACS FlatSat, a test-bed used to verify the functions, interfaces, and communications with each ADACS component prior to integrating the component on the flight structure. AI developed the flight-like harness, test software, safe to mate procedures, and test procedures for the FlatSat, which also were used for the spacecraft. FlatSat is currently being upgraded to support the Command and Data Handling (CDH) computer in order to provide full simulation of the Dual cardcage. Our experience with the ADACS hardware on the FlatSat led to our involvement integrating those units on the spacecraft.
The DCC houses two redundant computers: the Command and Data Handling (CDH) computer and ADACS computer. To support the development and testing of the DCC, AI enhanced the DCC test software to verify all cardcage functions and interfaces. AI developed a test harness (thermal vacuum chamber ready) and GSE to verify these interfaces.
Because of our experience evaluating spacecraft jitter, AI was tasked to perform a jitter analysis for VCL. AI analyzed the system timing between the GPS , Star Tracker, and ADACS processor to verify the center of integration time of the star tracker data packet could be determined relative to the GPS time synchronization pulse. We assisted in investigating alternate methods to updating the spacecraft clock.
AI developed a test setup to verify the new Course Sun Sensor ( CSS ) baffle design. The concern was that the baffles might contaminate the sensor readings by reflecting light onto the sensor surface. The test also demonstrated the flight baffles did not contaminate the sensor readings.
AI’s support on VCL includes the development of ground support equipment ( GSE ). Much of the GSE consists of component simulators that provide the functionality of the flight boxes when they are not available. The GSE has been designed to interface to the flight connectors either on the FlatSat or Spacecraft. When the VCL instrument was delayed, the spacecraft bus was re-assigned as the GLORY mission.
Under subcontract to QSS, AI provided engineering test support to Goddard Space Flight Center ‘s ICESAT GPS receiver. This task was a result of AI’s work with the GPS unit on the VCL spacecraft. ICESAT’s GPS is the same unit as the GPS to be flown on the VCL spacecraft.
AI assisted in verifying the timing of the GPS 1 Hz tick and data packets. The timing of the data packets to the 1 Hz tick was critical for ICESAT. AI assisted in measuring and documenting the length of the data packets as well as their timing referenced to the 1 Hz pulse. AI developed custom circuits taking these measurements. Visit NASA’s site for more information.