Identification of poorly ventilated zones in gas-turbine enclosures with machine learning

Ventilation systems are used in gas turbine packages to control the air temperature, to protect electrical instrumentation and auxiliary items installed inside the enclosure and to ensure a proper dilution of potentially dangerous gas leakages. These objectives are reached only if the ventilation flow is uniformly distributed in the whole volume of the package, providing a good air flow quality as prescribed by international codes such as ISO 21789. To evaluate the effectiveness of the ventilation design, numerical computations are performed for several purposes, one of which is the identification of poorly ventilated portions of the enclosure. In fact, it is essential to accurately detect the regions which are less ventilated, since they could be prone to the accumulation of an accidental fuel gas leak. There are different approaches to identify these portions, such as decay regression or inlet source analysis, that require unsteady simulations of the flow field inside the package. The present work discusses the implementation of a new methodology using machine learning and artificial neural networks (ANN) to detect the poorly ventilated regions where a gas cloud can accumulate. The concentration of fuel gas is estimated starting from a steady-state computation without running a more expensive unsteady computation. The entire process is built around an accurate training of the ANN using a proper set of simpler test-cases that have been identified to match the characteristics of the gas turbine enclosure. During the training phase accuracy and overfitting of the ANN were monitored to ensure robustness of the method. The procedure is then applied to a real case scenario and the results are presented in this paper highlighting the main advantages of this approach respect to a conventional use of CFD analysis. Computations of the flow fields are carried out using OpenFOAM with RANS and U-RANS approaches, while the ANN is developed and trained in Python.