Prediction of Ventilation Effectiveness for LM9000 Package With Machine Learning

Gas turbines usually are installed inside an enclosure, which is used as protection from the external environment and to provide an acoustic insulation. A ventilation system is required to control the temperature inside the enclosed volume and to dilute any potential gas leakage that may come from faulty pipes or flanges. The system has to be properly designed to avoid any unexpected explosion which would generate an overpressure not contained by enclosure walls. The most common approach to predict the effectiveness of the ventilation system requires to perform CFD analyses, which are very expensive in computational terms. A new approach has been proposed by authors, using machine learning and artificial neural networks (ANN) to identify the poorly ventilated zones. This methodology has been further developed, optimized and applied to a real gas turbine packages of new generation. In the present paper the authors will show the application of this procedure to the LM9000 package and the comparison with the results predicted using conventional CFD techniques. The tangible improvement introduced by this methodology is that the computational time is reduced from about three weeks with the common CFD approach to few minutes. The artificial neural network is developed in a Python environment that is applied during the CFX post-process phase of a steady state CFD simulation, providing results equivalent to unsteady CFD simulation. Besides the immediate benefits of this particular application, the suggested approach looks to be a great candidate to substitute the conventional and time-consuming CFD simulations with a fast post-processing algorithm that is able to learn and self-optimize as long as it is used.