Machine learning based airflow sensing for aircraft

A computer usable program product comprising one or more computer-readable storage media, and program instructions stored on at least one of the one or more storage media, the stored program instructions when executed by a processor causing operations comprising:

• measuring, using a set of airflow sensors disposed on an airfoil of an aircraft, first airflow data comprising an amount of airflow experienced at each airflow sensor at a first time;
• analyzing, using a trained neural network model trained to classify airflow data from a first airflow sensor of the set of airflow sensors into either a first state or a second state, the first airflow data to determine an airflow state of the aircraft;
• adjusting, in response to determining that the aircraft is in an abnormal airflow state, at least one member from a set comprising (i) a control surface and (ii) a power unit of the aircraft;
• and
• returning, responsive to the adjusting, the aircraft to a normal airflow state.

An apparatus comprising:

• an airflow sensor disposed on an airfoil of an aircraft, the airflow sensor configured to measure airflow data comprising an amount of airflow experienced at the airflow sensor at a first time;
• a hardware computing element in the aircraft, the hardware computing element comprising a processor, a computer-readable memory, and a computer-readable storage device configured to execute a trained neural network model in the aircraft, wherein the trained neural network model is trained to determine, by classifying the airflow data into either a first state or a second state, an airflow state of the aircraft from the airflow data;
• and
• at least one member from a set comprising (i) an adjustable control surface of the aircraft coupled with the hardware computing element, and (ii) an adjustable power unit of the aircraft coupled with the hardware computing element.