Session: 25-01 Turbine Component Lifing

Submission Number: 178796

Methodology to Predict the Impact of Customer-Specific Operating Profiles on Industrial Gas Turbine Component Life 

The industrial gas turbine (IGT) market is gradually expanding in scope, with broader applications and increasing requirements for operational flexibility. A key aspect of operational flexibility is the ability to temporarily overfire to produce more power. There are several reasons to do this: 1) it may be required to comply with Grid Code to help stabilize the grid; 2) to provide extra power on a hot day; or 3) to help manage spinning reserve requirements for a bank of gas turbines. The diverse operational profiles of IGTs presents significant challenges for original equipment manufacturers (OEMs), as variations in customer usage can strongly influence component degradation and, consequently, the lifespan of critical parts. To address this, OEMs have developed robust methodologies capable of assessing the impact of differing operational requirements (including the effect of overfiring) on engine health and durability. 

This work presents a novel methodology that combines a physics-based digital asset approach with synthetic engine data tailored to a customer’s location and operational requirements. The digital asset utilizes a reduced order model that infers stresses and temperatures at critical locations from engine performance data. These outputs are then combined with damage models representing mechanisms such as creep, fatigue, oxidation, and corrosion. To recognise uncertainty in actual usage patterns, the methodology incorporates a Monte Carlo simulation to generate ensembles of potential operational scenarios. This probabilistic approach yields a distribution of damage outcomes, providing insight into the most likely component degradation rather than a single deterministic value.

This lightweight solution provides rapid quantitative assessment of expected damage accumulation over extended periods under specific customer usage scenarios (e.g. extra power, spinning reserve) prior to engine deployment.  When coupled with Equipment Health Monitoring, this allows an OEM sanctioned temporary overfire that is under the control of the operator. The proposed framework offers a powerful tool for both OEMs and customers to evaluate the lifecycle implications of tailored operating strategies, supporting informed decision-making for improved performance and durability.

Presenting Author: Andrew Moffat Solar Turbines Inc.

Presenting Author Biography: Andrew Moffat is a Group Manager of Digital Methods and Technology in the Condition Based Engineering Department at Solar Turbines. His background is in materials engineering with a PhD from the University of Southampton. He is now responsible for the automation of lifing approaches for the purposes of lifing high integrity components in Solar Turbines fleet of digital twins.

Authors:

Adam Thompson Element Digital Engineering
Mike Zhang Solar Turbines Inc.
Kris Kapitzke Solar Turbines Inc.
Andrew Moffat Solar Turbines Inc.