Obtaining fluid flow pattern for turbine stage with neural model

In the paper possibility of applying neural model to obtaining patterns of proper operation for fluid flow in turbine stagefor fluid-flow diagnostics is discussed. Main differences between Computational Fluid Dynamics (CFD) solvers and neural model is given, also limitations and advantages of both are considered. Time of calculations of both methods was given, also possibilities of shortening that time with preserving the accuracy of the calculations are discussed. Gathering training data set and neural networks architecture is presented in detail. Range of work of neural model was given. Required input data for neural model and reason why it is different than in computational fluid dynamics solvers isexplained. Results obtained with neural model in 21 tests are discussed. Arithmetic mean and median of relative errors of recreating distribution of pressure and temperature are shown. Achieved results are analysed.