Volponi A , Tang L Principles of Gas Path Analysis 2025 | 36.81 MB
Title: Principles of Gas Path Analysis
Author: Allan J. Volponi and Liang Tang
Description:
Principles of Gas Path Analysis offers a self-contained reference of the concept and theory of gas path analysis (GPA), as both a diagnostic and prognostic methodology for gas turbine engines. It provides a chronological account of the methodology as it evolved over the past 50 years.
Before expanding into specific GPA concepts, the book begins by covering the basics that are generic to diagnostics and prognostics as well as a discussion of engine health management (EHM) and how GPA can contribute to this strategy. The text further introduces essential parameter corrections important for understanding the foundational principles of GPA. Additionally, advanced topics such as information fusion and ambiguity resolution are explored to highlight potential future advancements in the field. Examples are provided using simulated data generated from a fictional high bypass turbofan engine model. The book contains a comprehensive set of appendices with detailed treatment of the mathematical derivations and statistics behind GPA as well as specialized constructs for relevant methods such as neural networks and fuzzy logic.
The book is intended for professional engineers engaged in the gas turbine industry and EHM, including aircraft operators and maintainers. It will also benefit researchers studying gas turbine engine diagnostics and prognostics.
Principles of Gas Path Analysis offers a self-contained reference of the concept and theory of gas path analysis (GPA), as both a diagnostic and prognostic methodology for gas turbine engines. It provides a chronological account of the methodology as it evolved over the past 50 years.
Before expanding into specific GPA concepts, the book begins by covering the basics that are generic to diagnostics and prognostics as well as a discussion of engine health management (EHM) and how GPA can contribute to this strategy. The text further introduces essential parameter corrections important for understanding the foundational principles of GPA. Additionally, advanced topics such as information fusion and ambiguity resolution are explored to highlight potential future advancements in the field. Examples are provided using simulated data generated from a fictional high bypass turbofan engine model. The book contains a comprehensive set of appendices with detailed treatment of the mathematical derivations and statistics behind GPA as well as specialized constructs for relevant methods such as neural networks and fuzzy logic.
The book is intended for professional engineers engaged in the gas turbine industry and EHM, including aircraft operators and maintainers. It will also benefit researchers studying gas turbine engine diagnostics and prognostics.
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