E+T Editorial Team Tue 18 Feb 2025
Collected at: https://eandt.theiet.org/2025/02/18/ai-system-could-help-aircraft-recover-mid-air-stalls-and-altitude-drops
Aircraft could be equipped with an onboard AI system to help with midair stalls and sharp altitude drops, researchers have said.
A team from KTH Royal Institute of Technology in Sweden has conducted tests on an AI system designed to enhance the effectiveness of experimental technologies for manipulating airflow on wing surfaces.
An AI technique known as deep reinforcement learning (DRL) allows the programme to use previous experiences to guide the way it controls the airflow technologies in real time.
The AI control system zeroes in on one particularly dangerous aerodynamic phenomenon known as flow detachment, or turbulent separation bubbles. To stay aloft, aircraft need slow-moving air underneath the wing, and fast-moving air above it. The air moving over the wing surface needs to follow the wing shape, or ‘attach’, to the surface.
According to Ricardo Vinuesa, a fluid dynamics expert at KTH, when the air moving over the wing’s surface no longer follows the wing shape and instead breaks away, it creates a dangerous ‘bubble’ of swirling recirculating air or stalled airflow.
“This usually occurs when the wing is at high angle of attack, or when the air slows down due to increasing pressure,” he said. “When this happens, lift decreases, and drag increases, which can lead to a stall and make the aircraft harder to control.”
But using the DRL system, new researchers found they could reduce the area of these bubbles by 9%.
The team tested how effectively AI could control experimental devices that pulse air in and out of a small opening in the wing surface, known as synthetic jets. While such innovations are still in the experimental stage, aerospace engineers look at them to complement physical features such as vortex generators that planes rely on to maintain the right balance of airflow above and below the wings.
Aircraft could be equipped with an onboard AI system to help with midair stalls and sharp altitude drops, researchers have said.
A team from KTH Royal Institute of Technology in Sweden has conducted tests on an AI system designed to enhance the effectiveness of experimental technologies for manipulating airflow on wing surfaces.
An AI technique known as deep reinforcement learning (DRL) allows the programme to use previous experiences to guide the way it controls the airflow technologies in real time.
The AI control system zeroes in on one particularly dangerous aerodynamic phenomenon known as flow detachment, or turbulent separation bubbles. To stay aloft, aircraft need slow-moving air underneath the wing, and fast-moving air above it. The air moving over the wing surface needs to follow the wing shape, or ‘attach’, to the surface.
According to Ricardo Vinuesa, a fluid dynamics expert at KTH, when the air moving over the wing’s surface no longer follows the wing shape and instead breaks away, it creates a dangerous ‘bubble’ of swirling recirculating air or stalled airflow.
“This usually occurs when the wing is at high angle of attack, or when the air slows down due to increasing pressure,” he said. “When this happens, lift decreases, and drag increases, which can lead to a stall and make the aircraft harder to control.”
But using the DRL system, new researchers found they could reduce the area of these bubbles by 9%.
The team tested how effectively AI could control experimental devices that pulse air in and out of a small opening in the wing surface, known as synthetic jets. While such innovations are still in the experimental stage, aerospace engineers look at them to complement physical features such as vortex generators that planes rely on to maintain the right balance of airflow above and below the wings.
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