Haifa Students Develop Algorithm to Safely Land Aircraft with Engine Failure

December 3, 2019

3 min read

When a turbine engine unexpectedly stops producing power due to a malfunction, turbine engine failure occurs. Losing an engine in flight of a twin-engine aircraft is not usually a particularly serious problem, and the pilots are given extensive training to deal with such a situation.

Yet, although such engines are very reliable and efficient, sometimes malfunctions occur that mean an engine has to be shut down during flight. Engine cutoff, a recurring threat in aviation, can be caused by an engine malfunction, fuel leaks or improper aircraft maintenance. Such events, coupled with adverse weather and terrain-induced obstacles, may endanger passengers and crew.

According to the US Federal Aviation Administration, turbine engines have a failure rate of one per 375,000 flight hours. Following an engine shutdown, a precautionary landing is usually performed with airport fire and rescue equipment positioned near the runway.

Over the years, there have nevertheless been a number of fatalities, such as a a McDonnell Douglas DC-10 flying from Miami to San Francisco in 1973, in which an engine failure resulted in one death; two Polish LOT Airlines flights that suffered catastrophic engine failures in the 1980s and caused crashes that killed over 200 people on board;

In 2009, Chelsey Sullenberger landed his Airbus 320 with 155 passengers and crew on the Hudson river, after both engines were disrupted by a flock of birds. On August 1st 2019, a generation aviation (GA) aircraft experienced a fuel-supply-induced engine cutoff – landing on a Washington highway.

Prof. Yosef Ben-Asher of the Faculty of Aerospace Engineering at then Technion-Israel Institute of Technology at Haifa, has had the issue of engine failures on his research radar for about two decades. Disturbing rates of fatal GA accidents, mostly due to loss of thrust, have persisted. This has inspired a parallel Technion research group led by electrical engineering faculty dean Prof. Nahum Shimkin and Dr. Aharon Bar-Gill, to find an online optimal solution to this problem.

The on-line algorithm calculates (and periodically re-checks) the globally-optimal trajectory in terms of minimal altitude loss, accounting for descent-generated terrain obstacles and on-board estimated intense in-plane and crosswinds.

“Since we are aiming to aid a pilot under immense stress, it is imperative to validate the algorithm in actual flight,” said the engineers. “We’ve chosen to flight-test our optimal algorithm on a Cessna 172 – to demonstrate both the optimal airstrip choice, and trajectory generation, as well as the following of this trajectory by the Pilot-in-Distress.

“A major challenge to the onboard implementation was the requirement for absolutely no interface of the airborne test equipment with the aircraft systems, neither mechanical nor electrical. Our students have successfully designed a self-contained experimental setup. This endeavor called for ingenuity, for example, coming up with real-time estimation of wind intensity and direction,” they continued.

“Also, the students have developed a dedicated simulation for testing the implementation of the algorithm. It involves flight modeling, the generation of the optimal trajectory towards the preferable airstrip and cues on a screen for the pilot to track this trajectory. This simulative environment has contributed immensely to our on-board S/W debugging and thus to the success of our airborne experiment,” they said.

Assuming engine failure west of Mount Tabor in the Lower Galilee and potential landing strips east of it, the algorithm was able to select the best landing strip available to the east of Mount Tabor and compute the optimal trajectory for the test pilot to track. The pilot circumvented Mount Tabor appropriately, reporting that the cue-tracking dynamics was satisfactory.“We have thus validated our concept in-flight, as a real-time algorithm for the tracking of the globally-optimal trajectory by the pilot. This real-time globally-optimal algorithm, developed by the Technion research team, can be readily adopted in GA aircraft cockpits as well as for unmanned aerial vehicles.

 

 

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