No description available.

No description available.

No description available.

No description available.

No description available.

Substantial economic and even safety related gains can be achieved if effective gas turbine performance analysis is attained. During the development phase, analysis can help understand the effect on the various components and on the overall engine performance of the modifications applied. During usage, analysis plays a major role in the assessment of the health status of the engine. Both condition monitoring of operating engines and pass off tests heavily rely on the analysis. In spite of its relevance, accurate performance analysis is still difficult to achieve. A major cause of this is measurement uncertainty: gas turbine measurements are affected by noise and biases. The simultaneous presence of engine and sensor faults makes it hard to establish the actual condition of the engine components. To date, most estimation techniques used to cope with measurement uncertainty are based on Kalman filtering. This classic estimation technique, though, is definitely not effective enough. Typical Kalman filter results can be strongly misleading so that even the application of performance analysis may become questionable. The main engine manufactures, in conjunction with research teams, have devised modified Kalman filter based techniques to overcome the most common drawbacks. Nonetheless, the proposed methods are not able to produce accurate and reliable performance analysis. In the present work a different approach has been pursued and a novel method developed, which is able to quantify the performance parameter variations expressing the component faults in presence of noise and a significant number of sensor faults. The statistical basis of the method is sound: the only accepted statistical assumption regards the well known measurement noise standard deviations. The technique is based on an optimisation procedure carried out by means of a problem specific, real coded Genetic Algorithm. The optimisation based method enables to concentrate the steady state analysis on the faulty engine component(s). A clear indication is given as to which component(s) is(are) responsible for the loss of performance. The optimisation automatically carries out multiple sensor failure detection, isolation and accommodation. The noise and biases affecting the parameters setting the operating point of the engine are coped with as well. The technique has been explicitly developed for development engine test bed analysis, where the instrumentation set is usually rather comprehensive. In other diagnostic cases (pass off tests, ground based analysis of on wing engines), though, just few sensors may be present. For these situations, the standard method has been modified to perform multiple operating point analysis, whereby the amount of information is maximised by simultaneous analysis of more than a single test point. Even in this case, the results are very accurate. In the quest for techniques able to cope with measurement uncertainty, Neural Networks have been considered as well. A novel Auto-Associative Neural Network has been devised, which is able to carry out accurate sensor failure detection and isolation. Advantages and disadvantages of Neural Network-based gas turbine diagnostics have been analysed.

No description available.

No description available.

No description available.

Gas turbine engine performance improvement has been requested continuously for both military and commercial applications due t various reasons. One of the issues is to save fuel and/or to increase the engine life to meet the multi-mission and operation cost economics requirements. I order to satisfy the customers' requirements, the engine manufacturers invested a lot of money and time if the gas turbine performance improvement. The most straight forward and simple approach is to trade the excess remained surge margins for performance. NASA has demonstrated the feasibility of this concept in their F-15 Highly Integrated Digital Electronic Control and Performance Seeking Control programs. It offers not only obvious benefits if the overall system performance improvement but also cost effective operations such a fuel saving and extended component life. Those were carried out with traditional control approaches which have to face the modelling difficulties. ' Due to successful control implementations of fuzzy logic if various environment of uncertainties, a proportional plus integral z logic controller if proposed. The fuzzy logic control system simulation results prove that the fuzzy logic controller is appropriate for gas turbine engine control. Basic fuzzy logic control concept is used with new approaches to simplifying the fuzzy logic controller. I order to enhance the engine performance, fuzzy logic control concept is used to optimize the engine performance parameters. A time function linear control scheme is proposed to the engine to a new operation location System simulation results prove the new methodology. It has to be understood that the engine model used if this research is not representative of a gas turbine, but it `is appropriate for the fuzzy logic control design analysis and simulation.