Abstract: A method of using computer implemented neural network for simulation of aerodynamic performance of technical object having geometry, method includes: training neural network using plurality of sets of pre-computed computational fluid dynamics encodings, CFD, outputs, wherein training is generated using inputs including: geometry of at least one training technical object; spatial locations of neural network input nodes as node attributes; relationship between geometry of at least one training technical object and neural network input node locations; associated boundary conditions; operating conditions; and computed outputs including flow fields and aerodynamic performance parameters; training using loss function evaluating error between neural network and pre-computed CFD outputs to produce trained neural network; using trained neural network with new inputs to generate as output predicted aerodynamic performance of technical object, wherein relationship between geometry of at least one training technical obje

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 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 neural network is trained for, and may be used in, the simulation of the fluid flow through a domain around an object geometry. A first training process for the neural network includes training (902) the network on a first set of encodings of pre-computed computational fluid dynamics (CFD) simulations for object geometries and associated boundary conditions. The first training process uses a first loss function that evaluates an error between the network output and the pre-computed CFD simulations. A second training process is then carried out which includes training (905) the network on a second set of encodings of object geometries and associated boundary conditions. The second training process uses a second loss function that evaluates an error between the network output and a set of fluid dynamics conditions.

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: 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 method of assembling a rotor stack comprising a plurality of component parts, the method comprising: determining swash measurements for interfacing surfaces of the plurality of parts; calculating a runout estimate for a plurality of relative orientations of the parts; applying an optimization algorithm to identify an optimal orientation from the plurality of relative orientations based on the runout estimates; and assembling the parts in the optimal orientation.

Abstract: A method of assembling a rotor stack comprising a plurality of component parts, the method comprising: determining swash measurements for interfacing surfaces of the plurality of parts; calculating a runout estimate for a plurality of relative orientations of the parts; applying an optimisation algorithm to identify an optimal orientation from the plurality of relative orientations based on the runout estimates; and assembling the parts in the optimal orientation.

Abstract: A combustor for a gas turbine engine which includes a flame tube made of a ceramic material and a metal casing which surrounds the flame tube. The combustor further includes a thermal insulation layer between the flame tube and the casing which protects the casing from the high temperatures produced by combustion in the combustor and a loading structure which holds the flame tube in a state of axial and radial compression at all engine operating conditions.