| Objective: | Develop a novel methodology for the real-time management of aero gas turbine engine life and usage data.
| Description: | The monitoring of gas turbine component life and usage is becoming increasingly complex and is generating greater orders of magnitude of data that require processing, storage, and almost certainly retrieval and re-processing.
The issue can be illustrated through the example of the low cycle fatigue life of critical components. For older engines, this is specified simply in terms of a number of cycles. These may be defined for the whole component, or for specific high-stress features of that component. The consumption, or usage, of these cycles must then be calculated, monitored, and managed for a fleet of engines as the engines are flown and run. The early method of doing this was to estimate the mean number of cycles that would be consumed each flying hour, and then allocate a flying hour life. Subsequent methods included installing devices on aircraft to count and sum major and minor cycles. The most recent thinking has been for the installation of much more precise (but complex) on-board algorithms to calculate cycle consumption in real time as a function of spool speeds, temperatures, and their rates of change; these provide either for the download of processed data or raw data (for off-board processing). All of these methods have disadvantages. The early models are oversimplified and therefore, conservative and wasteful of potential component usage. The later ones are complex, processor intensive, and lead to significant data management problems (particularly when data must be maintained against individual components, through several overhauls, throughout the component's life). For example, one advanced engine has on-board algorithms for real-time usage calculation for over 120 critical areas that are then managed off-board.
This topic is searching for a novel methodology for managing gas turbine life and usage data in real time that is data efficient, manageable, effective, and safe. It is not looking for an Information Technology (IT) system architecture, but the methodology to be used on one. The methodology should be flexible enough to allow for re-assessment of consumption where subsequent knowledge provides a revised understanding of the way the damage is accrued. The solution might involve the recognition of different regimes of damage accrual (including transients) that could be summed, or feature extraction, but should reflect the physics of the damage being imparted to the component and not be just a data reduction or compression method. An analogy might be the algorithms developed for sound, pictures, and video developed by the JPEG and MPEG groups.
| | PHASE I: Develop a novel conceptual methodology for the management of gas turbine life and usage data that is data efficient, manageable, effective, safe, and flexible.
| | | PHASE II: Develop algorithms that are compatible with industry standards to represent the on-board and off-board elements of a representative gas turbine life and usage data management system and to demonstrate its functionality.
| | DUAL USE COMMERCIALIZATION: Commercial applications include aircraft and power generation gas turbines. Military applications include aircraft, ship, and ground vehicle gas turbines.
| References: | 1. E. L. Suarez, J. Hansen, M. Duffy, and P. Gatlin, AIAA 97-2900, New Approach to Tracking Engine Life, 33rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, July 1997, Seattle, Washington.
2. E. L. Suarez, M. J. Duffy, D. Seto, and S. M. Cote, AIAA 2003-4985, Advanced Life Prediction Systems for Gas Turbine Engines, 39th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, July 2003, Huntsville, Alabama.
| | Keywords: | usage, damage, life, consumption, monitoring, data, management, Low Cycle Fatigue (LCF) |