Bringing programming and aviation know-how together to create safer flight systems

January 25, 2012

Troy, N.Y. - Rensselaer Polytechnic Institute computer scientist Carlos Varela has received seed funding from the U.S. Air Force to help make flight data as updated, active, and accurate as possible. Varela, part of the Data Science Research Center at Rensselaer, will use the more than $100,000 grant to develop sophisticated computer logic programming to help create safer and more efficient flight technology. The grant is part of the Air Force Office of Scientific Research (AFOSR) Dynamic Data-Driven Application Systems (DDDAS) program.

Flying an airplane takes skill and Varela knows this all too well. The associate professor of computer science is also a licensed pilot. But, what Varela also knows is that flying an airplane also takes data--and lots of it. Pilots are constantly receiving and inputting data from air traffic controllers, weather reports, and the multiple sensors throughout the plane to help ensure a safe flight. But what happens when that data isn't up-to-date, or worse, incorrect?

"Data should drive the flight systems in an aircraft to create active and constantly updated flight data for the pilot," Varela said. "The idea is that rather than pulling data from sources such as weather forecasting services or air traffic control, which is more static in nature, the system would constantly be comparing and updating source data so that the flight plan is always up to date."

According to Varela, data must understand the connections with other data. For example, we can't be on time for a meeting if we didn't get the text message that the meeting was moved to an hour earlier. The data points weren't connected and we are late for the meeting. In the same way, a pilot or autopilot system cannot take the right action when the data they are receiving is out of date or plain wrong. This may have been the problem with the tragic crash of Air France flight 447, which crashed into the Atlantic in June of 2009 killing, all on board, according to Varela.

The evidence from the crash has suggested that the pilots on board raised the nose of the plane when it shouldn't have been brought up, he said. A plane with a nose too high will lose speed until eventually it stalls completely. Since the crash, many experts have purported that an equipment failure may have provided inaccurate information to the autopilot, causing it to disengage, said Varela. The pilots may have then incorrectly reacted to the emergency by raising the nose of the plane when in fact it needed to go down to break the stall, according to Varela.

Varela himself experienced this exact type of equipment failure during one of his own flights. Fortunately, Varela was able to determine the failure by comparing bad airspeed data to the ground speed data provided by his own GPS device. It turned out that ice had developed on the airspeed sensor, causing it to fail. Varela did not lower the nose of the plane as he normally would in response to such readings and instead deiced the sensor and safely finished his flight. His simple data comparison revealed a misreading and likely saved his life.

These experiences inspired Varela to develop new flight system programming that greatly reduces the possibility of accidents by making connections between the different data streams available to a pilot. This would create redundancies in the data that allow the different data streams to essentially fact check each other.

An active and redundant flight system may help prevent crashes caused by sensor or other data errors. For example, by comparing the airspeed data to the ground speed data, a flight system would be able to fact check a bad airspeed reading, assuming reasonable constraints on the wind speed. If the pilot is only operating by air speed data alone, they would have no way of knowing that there is an error in the system and they would respond to the incorrect data, upsetting the balance of the plane. The ground speed data would instead provide a fact checking mechanism because if air speed were swiftly changing, ground speed would be doing the same. If airspeed is changing, but ground speed remains unchanged, Varela's more active flight system would be able to notify the pilot of the discrepancy, allowing for more informed decision making.

The same would be true of flight plans. Flight plans are typically developed well ahead of the actual flight based on weather and air traffic forecasts. At the time of the actual flight, only limited or extreme data is considered, said Varela.

"The data being used for the actual flight is a little bit old and certainly not ideal," said Varela. "Pilots then end up flying a suboptimal and even unsafe plan."

The new system Varela seeks to develop would allow for the easier and faster inclusion of new data. This means that when a sensor gets new data such as a change in air pressure or temperature, it would trigger dependences with other data such as the data stream from the sensors that detect potentially lethal icing on the plane's wings. The flight plan could then be updated as needed. In this manner, pilots would always be flying an updated plan.

"The programming will model the data mathematically, so a pilot can tell that the data being received is wrong with some probability and have more information to make a flight decision."

The new system will build off what is known in computer science as logic programming by extending a logic programming language to associate probabilities to knowledge. The new system will also give first-class support to redundancy and connections between various spatio-temporal data streams, said Varela.

"Weather forecasts are by their very nature, not a sure thing," Varela said. "But, other data points are.

Varela hopes to create a new system that more easily deals with data streams and quickly admits new data. Such a system could be expanded to include unmanned flight systems and even beyond aviation. The example given by Varela was the citation of scientific findings, where data needs to be well connected to ensure it is accurate and constantly updated as new findings are created.
-end-


Rensselaer Polytechnic Institute

Related Data Articles from Brightsurf:

Keep the data coming
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Astronomers are bulging with data
For the first time, over 250 million stars in our galaxy's bulge have been surveyed in near-ultraviolet, optical, and near-infrared light, opening the door for astronomers to reexamine key questions about the Milky Way's formation and history.

Novel method for measuring spatial dependencies turns less data into more data
Researcher makes 'little data' act big through, the application of mathematical techniques normally used for time-series, to spatial processes.

Ups and downs in COVID-19 data may be caused by data reporting practices
As data accumulates on COVID-19 cases and deaths, researchers have observed patterns of peaks and valleys that repeat on a near-weekly basis.

Data centers use less energy than you think
Using the most detailed model to date of global data center energy use, researchers found that massive efficiency gains by data centers have kept energy use roughly flat over the past decade.

Storing data in music
Researchers at ETH Zurich have developed a technique for embedding data in music and transmitting it to a smartphone.

Life data economics: calling for new models to assess the value of human data
After the collapse of the blockchain bubble a number of research organisations are developing platforms to enable individual ownership of life data and establish the data valuation and pricing models.

Geoscience data group urges all scientific disciplines to make data open and accessible
Institutions, science funders, data repositories, publishers, researchers and scientific societies from all scientific disciplines must work together to ensure all scientific data are easy to find, access and use, according to a new commentary in Nature by members of the Enabling FAIR Data Steering Committee.

Democratizing data science
MIT researchers are hoping to advance the democratization of data science with a new tool for nonstatisticians that automatically generates models for analyzing raw data.

Getting the most out of atmospheric data analysis
An international team including researchers from Kanazawa University used a new approach to analyze an atmospheric data set spanning 18 years for the investigation of new-particle formation.

Read More: Data News and Data Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.