Abstract: A computer-implemented method controls input of at least a portion of a first training data set into a first machine learning algorithm. The first training data set includes data quantifying damage to a first compressor and data quantifying a first operating parameter of the first compressor. The first machine learning algorithm is executed, and data quantifying the first operating parameter is received as an output of the first machine learning algorithm. The first machine learning algorithm is trained using the received data output from the first machine learning algorithm and data quantifying the first operating parameter of the first compressor. The trained first machine learning algorithm is configured to enable determination of operability of a second compressor of a gas turbine engine.

Abstract: A computer-implemented method comprising: controlling input of data quantifying damage received by a compressor of a gas turbine engine into a first machine learning algorithm; receiving data quantifying a first operating parameter of the compressor as an output of the first machine learning algorithm; and determining operability of the compressor by comparing the received data quantifying the first operating parameter of the compressor with a threshold.

Abstract: A computer-implemented method comprising: controlling input of data quantifying damage received by a compressor of a gas turbine engine into a first machine learning algorithm; receiving data quantifying a first operating parameter of the compressor as an output of the first machine learning algorithm; and determining operability of the compressor by comparing the received data quantifying the first operating parameter of the compressor with a threshold.

Abstract: A computer-implemented method comprising: receiving an operability determination for a compressor of a gas turbine engine, the operability determination being determined using an output from a machine learning algorithm trained using data quantifying damage received by compressor blades of a compressor; determining one or more actions to be performed using the received operability determination; and generating control data using the determined one or more actions.

Abstract: A computer-implemented method comprising: controlling input of data quantifying damage received by a compressor of a gas turbine engine into a first machine learning algorithm; receiving data quantifying a first operating parameter of the compressor as an output of the first machine learning algorithm; and determining operability of the compressor by comparing the received data quantifying the first operating parameter of the compressor with a threshold.

Abstract: A computer-implemented method comprising: controlling input of at least a portion of a first training data set into a first machine learning algorithm, the first training data set including: data quantifying damage to a first compressor; and data quantifying a first operating parameter of the first compressor; executing the first machine learning algorithm; receiving data quantifying the first operating parameter as an output of the first machine learning algorithm; and training the first machine learning algorithm using: the received data output from the first machine learning algorithm; and data quantifying the first operating parameter of the first compressor, the trained first machine learning algorithm being configured to enable determination of operability of a second compressor of a gas turbine engine.

Abstract: This disclosure concerns aerofoils for an axial flow compressor. The compressor has an array of aerofoils angularly spaced about its axis of rotation. The aerofoils have leading and trailing edges extending in a direction spanning a flow region defined between a radially inner rotor component and a radially outer casing. Each aerofoil has opposing pressure and suction surfaces extending between the leading and trailing edges and terminating at a free end of the aerofoil. Each aerofoil leans towards the pressure surface by an angle of between 10° and 80° in the vicinity of the aerofoil tip. The aggressive negative lean towards the tip may help reduce over-tip leakage flow in use.