... is approached utilizing the current advances in generative artificial intelligence. We train an invertible neural network (INN) on an expandable database of geometrically parameterized combustor designs with simulated performance labels. Utilizing the INN in its inverse direction, multiple design proposals...

... learning via neural network (NN) Training is employed to establish correlations between turbulent flow features and optimal GEKO parameters, enabling the trained model's generalization. This approach allows computationally faster simulations of swirling flow using an optimized GEKO model matching...

... multivariable curve fitting problems [ 18 ]. It is a three-layer, feedforward neural network commonly applied to pattern recognition, signal processing, nonlinear system modeling, and control problems that has simple topological structure, universal approximation ability, and favorable convergence...

... References [1] Jelali , M. , and Kroll , A. , 2004 , Hydraulic Servo-Systems: Modeling, Identification, and Control , Springer-Verlag , London. [2] Lazzaretto , A. , and Toffolo , A. , 2001 , “ Analytical and Neural Network Models for Gas Turbine Design and Off-Design Simulation...

... November 26, 2016; published online February 23, 2017. Editor: David Wisler. 23 09 2016 26 11 2016 Aeronautical propulsion Aerospace applications Computational Design optimization Engines Gas turbine technology Modeling Neural networks Reliability Thermodynamics Aerospace...

... learning approach is used for the misfiring detection. The features used as inputs for the classifier are extracted from measurements incorporating physical knowledge about the given setup. To this end, a neural network is trained based on labeled data which is then used for classification purposes, i.e...

..., with the maximum exergy efficiency and the lowest cost per power (k$/kW) as its objectives. Artificial neural network (ANN) is chosen to accelerate the parameters query process. It is shown that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch...

...Krzysztof Dominiczak; Romuald Rządkowski; Wojciech Radulski; Ryszard Szczepanik Considered here are nonlinear autoregressive neural networks (NETs) with exogenous inputs (NARX) as a mathematical model of a steam turbine rotor used for the online prediction of turbine temperature and stress...

... = gas generator speed NMF = number of membership functions NN = neural networks NPT = power turbine speed OP = output power p kj = scalar coefficients in ANFIS function PC = percentage of compliance QAPRBS = quasi-amplitude modulated pseudo random binary...

...” data. These models of different details are used in a specific diagnostic process employing model-based diagnostic methods, namely the probabilistic neural network (PNN) method and the deterioration tracking method. The results demonstrate the level of diagnostic information that can be obtained...