A Real-Time Fuel Thermal Capacity and Prognostics Algorithm

Advanced tactical aircraft and their propulsion systems produce an order of magnitude more heat than legacy designs and offer fewer viable heat rejection opportunities. The current approach uses aircraft fuel as a primary heat sink which is either cooled by ram air and returned to the aircraft, or rejected off the aircraft when burned by the engine. Traditionally, aircraft have been limited in mission capability by the design performance and the available fuel quantity; however, potential thermal limitations have presented a new mission challenge. Joker and bingo range notifications based on fuel quantity remaining are common on modern fighters to ensure the pilot has the foresight to complete a mission segment and return to base before running out of fuel. Now, pilots may need to consider the possibility of a similar thermal joker/bingo concept until alternative LO heat rejection methods are discovered that remove limitations. Currently, no such parallel advance warning exists for a thermal limitation. As an air vehicle consumes fuel for sustained flight to complete a mission, the amount of thermal mass available for heat rejection, is also consumed. Therefore, an active (pilot in the loop) fuel management capability for range and thermal heat sink is necessary. To enable prediction of remaining thermal capacity requires detailed modeling of the performance of aircraft systems projected over the anticipated remaining mission segments. To this end, a prognostics modeling capability has been created to provide real-time updates to the pilot regarding the state of thermal capacity (similar to fuel quantity and range). The information is relayed to the pilot in the form of a thermal capacity “gauge”, providing the necessary foresight to allow a pilot to successfully and safely complete a mission segment and return to base. The algorithm is designed to capture deviations from the predefined missions to accurately assess impacts to thermal capacity and provide the pilot with a limitation assessment. In addition, the algorithm has the capability to provide the pilot with optimal thermal cooling operation in the condition where an in-flight thermal limitation is predicted. The foresight provided by this prognostics capability will improve operational performance of the aircraft and provide advanced mission planning ability to avoid thermal limitations in the future.