Engineering topics

Kurklu, Elif

Publications and source records attributed to Kurklu, Elif.

4 records

NASA NTRS2020

A Flight Rule Checker for the LADEE Lunar Spacecraft

As part of the design of a space mission, an important part is the design of so-called flight rules. Flight rules express constraints on various parts and processes of the mission, that if followed, will reduce the risk of failure. One such set of flight rules constrain the format of command sequences regularly (e.g. daily) sent to the spacecraft to con- trol its next near term behavior. We present a high-level view of the automated flight rule checker Frc for checking command sequences sent to NASA’s LADEE Lunar mission spacecraft, used throughout its entire mission. A command sequence is in this case essentially a program (a sequence of commands) with no loops or conditionals, and it can there- fore be verified with a trace analysis tool. Frc is implemented using the TraceContract runtime verification tool, an internal Scala DSL for checking event sequences against “formal specifications”. The paper illustrates this untraditional use of runtime verification in a real con- text, with strong demands on the expressiveness and flexibility of the specification language, illustrating the advantages of an internal DSL.

Kurklu, Elif↗

NASA NTRS2008

Automated Data Assimilation and Flight Planning for Multi-Platform Observation Missions

This is a progress report on an effort in which our goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advanced techniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. We describe the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

Oza, Nikunj↗

NASA NTRS2005

High and Dry: Trading Water Vapor, Fuel and Observing Time for SOFIA

The Stratospheric Observatory for Infrared Astronomy (SOFIA) is NASA's next generation airborne astronomical observatory. The facility consists of a 747-SP modified to accommodate a 2.5 meter telescope. SOFIA is expected to fly an average of 140 science flights per year over it's 20 year lifetime, and will commence operations in early 2005. The SOFIA telescope is mounted aft of the wings on the port side of the aircraft and is articulated through a range of 20 deg to 60 deg of elevation. A significant problem in future SOFIA operations is that of scheduling Facility Instrument (E) flights in support of the SOFIA General Investigator (GI) program. GIs are expected to propose small numbers of observations, and many observations must be grouped together to make up single flights. Approximately 70 GI flight per year are expected, with 5-15 observations per flight.

Frank, Jeremy↗

NASA NTRS2002

SOFIA's Choice: Scheduling Observations for an Airborne Observatory

We describe the problem of scheduling observations for an airborne observatory. The problem is more complex than traditional scheduling problems in that it incorporates complex constraints relating the feasibility of an astronomical observation to the position and time of a mobile observatory, as well as traditional temporal constraints and optimization criteria. We describe the problem, its proposed solution and the empirical validation of that solution.

Frank, Jeremy↗