Craig R. Davison

Set Up and Operational Experience With a Micro-Turbine Engine for Research and Education

Set up and operation of a mid to large size gas turbine requires a significant investment in the engine and the test cell. The continuing cost of operation and maintenance is also substantial. Both the capital and operating costs are well outside the budget of many educational institutions, and small research centres. Micro-turbines, in particular engines used for model aircraft, are a viable alternative. Their capital costs are low and existing facilities can often be modified to support them. Micro-turbines are complete turbojets often utilizing a centrifugal compressor from a turbo-charger and an axial turbine. This work utilized a micro-jet engine commonly used to propel remote control aircraft. The engine has been set up for research into component degradation and as a laboratory for an upper year engineering course in turbomachinery. It is rated by the manufacturer to produce 150 N of thrust at 132,000 RPM. This paper examines some of the problems encountered in installing small total pressure and temperature probes in a micro engine. The entire test rig, including measurement of thrust and mass flow is presented. As well, the addition of a PC based control system is discussed. Operating data is presented and compared to larger engines to demonstrate the viability of this engine as a test bed. Some problems encountered in using an engine such as this beyond its normal operating envelope, along with some solutions, are presented.Copyright

Steady State and Transient Modeling of a Micro-Turbine With Comparison to Operating Engine

Steady state and transient computer models of a micro turbine were produced. The engine under study was a micro-jet engine that when tested at 126,000 RPM provided 95 N thrust. The aero-thermal model uses generic performance maps for the compressor and turbine which were modified, based on operating data, to represent the components in the engine under study. The model also includes the inlet ducting connected to the engine. It simulates engine operation from idle to full power over the expected operating range of ambient temperature, pressure and humidity. A comparison of steady state model results to actual engine operating data is presented over the full range of speeds. The effect of ambient humidity on the engine operating point is examined for a micro-engine, in particular at temperatures above 30° Celsius. The techniques for introducing component faults are given and their effect on the engine operation is presented. The degraded components are the turbine and inlet flow passages. The methods for modeling the transient behavior of the engine are also presented. Results are presented for both acceleration and deceleration of the engine between steady state operating point. These results are also compared to the operating engine.Copyright

Comparison of Transient Modeling Techniques for a Micro Turbine Engine

A large number of papers have been published on transient modeling of large industrial and military gas turbines. Few, however, have examined micro turbines. The decrease in size affects the relative rates of change of shaft speed, gas dynamics and heat soak. This paper compares the modeled transient effects of a micro turbojet engine comprised of a single stage of radial compression and a single stage of axial expansion, with a diameter of 12cm. The model was validated with experimental data. Several forms of the model were produced starting with the shaft and fuel transients. Conservation of mass, and then energy, was subsequently added for the compressor, combustor and turbine, and a large inlet plenum that was part of the experimental apparatus. Heat soak to the engine body was incorporated into both the shaft and energy models. Heat soak was considered in the compressor, combustor and turbine. Since the engine diameter appears in the differential equations to different powers, the relative rates of change vary with diameter. The rate of change of shaft speed is very strongly influenced. The responses of the different transient effects are compared. The relative solution times are also discussed, since the relative size of the required time steps changes when compared to a large engine.Copyright

Prediction of Time Until Failure for a Micro Turbine With Unspecified Faults

The potential extensive use of micro turbines in distributed power generation will produce a requirement for maintenance scheduling. The wide variety of operating environments and loads will demand that simple hour counters be overly conservative. Additionally, more advanced techniques, such as producing databases of the engine history or metallography, would have expenditures that far outweigh the benefit for such low cost engines. The technique presented in this paper predicts the remaining life of the engine based on operating data since the last overhaul. The technique is independent of the component degrading and uses specific fuel consumption as the determinant of failure, but does not require the measurement of fuel flow or power. The prime advantages of this technique are the requirement for few additional sensors beyond those needed for engine control, and the ability to predict the time until failure without knowledge of the fault occurring or input from the engine user. The system utilizes an algorithm to determine the form of the trend the path to failure is taking and applies exponential smoothing, which is then extrapolated forward to the failure conditions. Since the prediction is based on the operating data since the last overhaul, it initially performs very poorly, but as the engine approaches failure it improves. Depending on the form of the trend, good predictions begin between 30% and 70% through the life of the engine. The system was tested with a computer model of a micro turbojet engine with five different faults ranging from blockage of inlet filters to turbine degradation. Both single and combined faults were tested with similar success. The engine model, including healthy baseline and degraded operation, was validated with an experimental program.Copyright

Development of Fault Diagnosis and Failure Prediction Techniques for Small Gas Turbine Engines

A computer model of a gas turbine auxiliary power unit was produced to develop techniques for fault diagnosis and prediction of remaining life in small gas turbine engines. Due to the relatively low capital cost of small engines it is important that the techniques have both low capital and operating costs.Failing engine components were identified with fault maps, and an algorithm was developed for predicting the time to failure, based on the engine’s past operation. Simulating daily engine operation over a maintenance cycle tested the techniques for identification and prediction. The simulation included daily variations in ambient conditions, operating time, load, engine speed and operating environment, to determine the amount of degradation per day. The algorithm successfully adapted to the daily changes and corrected the operating point back to standard conditions to predict the time to failure.Copyright

Steady State Performance Simulation of Auxiliary Power Unit With Faults for Component Diagnosis

A computer model of an auxiliary power unit has been developed. The model determines the steady state operating condition of the engine under user specified ambient conditions and load. Once the healthy baseline condition is determined faults, such as turbine degradation or flow passage blockage, were introduced and the effect on the engine’s operating parameters noted.Comparing the results to healthy baseline data generates fault signatures and fault maps, that can be used in engine diagnosis. By observing the changes in parameters that can be economically monitored the viability of this simple diagnosis method can be determined.Copyright

Automated Fault Diagnosis for Small Gas Turbine Engines

In one possible model of distributed power generation a large number of users will operate individual, gas turbine powered, cogeneration systems. These systems will be small, relatively inexpensive, and installed in locations without ready access to gas turbine maintenance experts. Consequently an automated method to monitor the engine and diagnose its health is required. To remain compatible with the low cost of the power system the diagnostics must also be relatively inexpensive to install and operate. Accordingly a minimum number of extra sensors should be used and the analysis performed by a common personal computer system. The current work automates the diagnosis of component faults by comparing the engine’s operating trends to the trends for known faults. This allows the relative percentage chance of each fault occurring to be determined. The likelihood of each fault is then compared, to determine which component is degrading. The technique can be adapted to compare the engines historic operating trend or a single operating point. In this initial work a computer model was used as a test bed and 5 faults were introduced individually. The technique successfully diagnosed the faulty component using either the operating trend or a single operating point.Copyright

Automated Fault Diagnosis of a Micro Turbine With Comparison to a Neural Network Technique

In the predicted future of distributed power generation, a large number of users will operate gas turbine powered cogeneration systems. These systems will be small, relatively inexpensive, and installed in locations without ready access to experts in gas turbine maintenance. Consequently, an automated system to monitor the engine and diagnose the health of the system is required. To remain compatible with the low cost of the overall system, the diagnostic system must also be relatively inexpensive to install and operate. Therefore, a minimum number of extra sensors and computing power should be used. A statistical technique is presented that compares the engine operation over time to the expected trends for particular faults. The technique ranks the probability that each fault is occurring on the engine. The technique can be used online, with daily data from the engine forming a trend for comparison, or, with less accuracy, based on a single operating point. The use of transient operating data with this technique is also examined. This technique has the advantage of providing an automated numerical result of the probability of a particular mode of degradation occurring, but can also produce visual plots of the engine operation. This allows maintenance staff to remain involved in the process, if they wish, rather than the system operating purely as a black box, and provides an easy to understand aid for discussions with operators. The technique is compared to an off the shelf neural network to determine its usefulness in comparison to other diagnostic methods. The test bed was a micro turbojet engine. The data to test the system was obtained from both experiment and computer modeling of the test engine.Copyright