Igor Loboda

Instituto Politecnico Nacional

Igor Loboda received the M.S. and Ph.D. degrees in aircraft engine engineering from the Kharkov Aviation Institute (Ukraine) in 1979 and 1994, respectively. He was an investigator, lecture and assistant professor at the Kharkov Aviation Institute during the period of 1992 to 2001. Since 2001 he works as an assistant professor and investigator at the National Polytechnic Institute of Mexico. His research interests are in the areas of simulation and condition monitoring of gas turbines and common theory of pattern recognition. Particular issues of interest are gas turbine thermodynamic models (static and dynamic), model identification, analysis of real data (gas path variables), fault identification techniques, and neural networks application to the gas path analysis.

1books edited

3chapters authored

Latest work with IntechOpen by Igor Loboda

This book presents new studies in the area of turbomachine mathematical modeling with a focus on models applied to developing engine control and diagnostic systems. The book contains one introductory and four main chapters. The introductory chapter describes the area of modeling of gas and wind turbines and shows the demand for further improvement of the models. The first three main chapters offer particular improvements in gas turbine modeling. First, a novel methodology for the modeling of engine starting is presented. Second, a thorough theoretical comparative analysis is performed for the models of engine internal gas capacities, and practical recommendations are given on model applications, in particular for engine control purposes. Third, multiple algorithms for calculating important unmeasured parameters for engine diagnostics are proposed and compared. It is proven that the best algorithms allow accurate prognosis of engine remaining lifetime.The field of wind turbine modeling is presented in the last main chapter. It introduces a general-purpose model that describes both aerodynamic and electric parts of a wind power plant. Such a detailed physics-based model will help with the development of more accurate control and diagnostic systems.In this way, this book includes four new studies in the area of gas and wind turbine modeling. These studies will be interesting and useful for specialists in turbine engine control and diagnostics.

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Modeling of Turbomachines for Control and Diagnostic Applications

Edited by Igor Loboda

Chapters authored

Neural Networks for Gas Turbine Diagnosis

By Igor Loboda

The present chapter addresses the problems of gas turbine gas path diagnostics solved using artificial neural networks. As a very complex and expensive mechanical system, a gas turbine should be effectively monitored and diagnosed. Being universal and powerful approximation and classification techniques, neural networks have become widespread in gas turbine health monitoring over the past few years. Applications of such networks as a multilayer perceptron, radial basis network, probabilistic neural network, and support vector network were reported. However, there is a lack of manuals that summarize neural network applications for gas turbine diagnosis.

Part of the book: Artificial Neural Networks

Advanced Nonlinear Modeling of Gas Turbine Dynamics

By Roman L. Zelenskyi, Sergiy V. Yepifanov and Igor Loboda

The process of gas turbine development requires different mathematical models. In particular, physics-based nonlinear dynamic models are widely used in the development of control and diagnostic systems. The present chapter firstly reviews known works on nonlinear dynamic engine modeling centering on model applications and developments. As an important development, modeling of heating up engine components is considered. This phenomenon consists in a radial clearance change during transients that influences engine static and dynamic performances. This clearance change is usually computed by a finite element method that is critical to computer resources. The chapter secondly presents a new and more rapid simulation methodology to integrate two dynamic processes, a general engine transient and a clearance change. This allows creating a more accurate and relatively fast engine dynamic model that is easy to use in the design of control and diagnostic systems. Finally, the chapter introduces further methodology enhancement consisting in the consideration of the influence of varying metal temperature on the strains induced by mechanical loads. To validate methodology, it is applied to a particular turbofan engine, and the simulated and real engine dynamic performances are compared.

Part of the book: Aerospace Engineering

Introductory Chapter: Gas and Wind Turbines and Their Models

By Igor Loboda

Part of the book: Modeling of Turbomachines for Control and Diagnostic Applications

Igor Loboda

Latest work with IntechOpen by Igor Loboda

Modeling of Turbomachines for Control and Diagnostic Applications

Chapters authored

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