Date of Award

Fall 2024

Document Type

Open Access Dissertation

Department

Mechanical Engineering

First Advisor

Jamil Khan

Abstract

Gas Turbine Engines (GTEs) serve as primary propulsion systems in aviation and are key for power generation units in various industrial applications. The conventional Gas Turbine Engine Development and Monitoring Lifecycle (EDML) typically encompasses six stages: preliminary design, numerical analysis, prototyping and testing, manufacturing, systems integration, and subsequent systematic monitoring processes. This dissertation redefines the gas turbine engine design and development process by synergistically integrating design capabilities, real-time operational data, and predictive maintenance through the implementation of digital twins. The primary objective is to establish a comprehensive framework for gas turbine engine design by utilizing thermodynamic and aerodynamic modeling, supported by advanced numerical simulations, and incorporating machine learning techniques to develop a digital twin. This framework aims to optimize engine performance, reduce design cycles, enhance predictive maintenance, and enable real-time dynamic monitoring and analysis. Through the digital twin, the system will integrate sensor data, simulations, and optimization algorithms to continuously update and refine the engine’s design and operational parameters, ensuring enhanced efficiency and performance across various operating conditions.

In this study, the SR-30 gas turbine engine is used as a case study to develop the framework for gas turbine engine design and analysis. In the first stage, experimental data is gathered using the SR-30 engine at the Midlands Tech Northeast Campus, with multiple fuels tested, including Jet-A, Jet-B, and kerosene. These experiments capture operational data across transient, design, and off-design conditions to assess both component-level and system-level efficiency. A process digital twin is developed based on operational data through physics-based machine learning models. In the second stage of the framework, numerical simulations are conducted for the SR-30’s centrifugal compressor and axial turbine using ANSYS tools such as BladeGen, coupled with ANSYS-CFX, for multi-parameter turbomachinery design optimization. The aerodynamic analysis assesses factors such as pressure distributions and velocity profiles based on the various blade geometries, while the structural analysis evaluates stress concentrations and deformation due to operational loads. Input parameters, including blade geometry, rotational speeds, and pressure ratios, are adjusted according to specific design requirements to ensure accurate modeling for varying operating conditions. Based on numerical simulations, inverse aerodynamic modeling with machine learning techniques is applied to iteratively generate and refine new design profiles for turbine and compressor blade geometries, which are subsequently used to study performance characteristics. In the final stage, a modular, plug-and-play software platform for gas turbine turbomachinery components is developed, facilitating the integration of thermodynamic design, performance optimization through advanced numerical analysis, process monitoring, emissions forecasting, and comprehensive lifecycle management. This approach aims to significantly enhance the efficiency, performance, and reliability of gas turbine engines by leveraging digital twin technology across the entire EDML. The novel Gas Turbine Engine Design and Monitoring Tool (GTE-DMT), a state-of-the-art digital twin software, is designed to navigate the Gas Turbine Engine Development and Monitoring Lifecycle (EDML) by integrating capabilities for preliminary design, gas path analysis, efficiency optimization, detailed turbomachinery component design, health monitoring, and emissions and fault diagnostics. In future work, this modular digital twin framework can be scaled to accommodate various engine geometries and operating conditions, enabling its use across different gas turbine platforms, including larger commercial and industrial gas turbines.

Rights

© 2025, Sowmya Raghu

Download

Share

COinS