Data-Driven Modelling of Gas Turbine Engines

The book "Data-Driven Modelling of Gas Turbine Engines" offers a comprehensive examination of modern methods for modeling gas turbine engines (GTE). Today, GTEs are widely used in various fields, including jet engines, power plants, and petrochemical facilities. The efficiency, longevity, and reliability of these systems can be significantly enhanced through the use of modeling and simulation techniques. This specialized book highlights advancements in data-driven modeling of GTEs and compares innovative linear and nonlinear approaches. The linear models, such as Ridge, Lasso, and Multi-Task Elastic-Net, are based on linear regressions, while a nonlinear model is developed and validated using recurrent neural networks (RNN). The results demonstrate that the RNN model can reliably be used for engine performance prediction by tracking changes in system inputs.