After maintenance, turbofan engines are subjected to a performance acceptance test in an indoor test-cell to demonstrate that corrected performance thresholds are met. The same indicators are also monitored after on-wing installation. Despite corrections for operation conditions, differences are observed between test-cell and subsequent on-wing performance. A comprehensive list of potential root causes for those differences was investigated using data-driven analyzes, theory and simulations. The main root causes are thermal effects, resulting from the lack of thermal stabilization during on-wing operation, and seal run-in, resulting from the initial decrease of effectiveness of replaced seals. Aircraft sensor bias and test-cell correction factors are expected to also contribute considerably. Engine bleed air and power extraction effects are negligible. The impact of inaccurate or missing throttle, temperature and humidity corrections was eliminated by application of proposed engine-specific customized corrections, which served as a successful proof of concept for improved on-wing monitoring accuracy.

Transitioning to cleaner sources of energy and improving the efficiency of power generators are crucial in combating global warming. Organic Rankine Cycles (ORC) when coupled to heat emitting power generators allows for waste heat recovery; thus increasing the engine overall efficiency and reducing atmospheric pollution. Additionally, implementing engine life cycle management is another way to improve the overall efficiency of power systems. The condition monitoring tool that has been developed in this research comprises a true model and a surrogate model. The true and surrogate models describe the real and ideal state of the ORC respectively and a comparison of both models reveals the health status of the ORC. The research has succeeded in creating a tool that is capable of performing accurate diagnostics in the shortest possible time and also in the development of a simple but unique method to derive thermodynamic relations for two phase fluids.

In the near future the Royal Netherlands Air Force will replace their fleet of F-16’s with the F-35. In the past the NLR has aided the Air Force with life cycle and deterioration analysis work on the F100-PW-220 engine, which powers the F-16. Understanding the physical system of the engine allows for on-condition maintenance. The same is preferred for the F135-PW-100 engine powering the F-35. Therefore, a preliminary lifing analysis tool has been developed for the F135-PW-100 engine rotor blades, based on open source literature.

Modern gas turbines are complex and expensive machines, requiring specialised maintenance to keep them in working order. Maintenance takes places at specialised shops such as KLM Engine Services (KLM ES). Part of KLM Engineering & Maintenance (KLM E&M), KLM ES provides maintenance ser- vices to aircraft engines belonging both to KLM and to external customers. Gas Path Analysis (GPA) is a technique which can aid KLM ES in its maintenance process. As a gas turbine deteriorates over time, its performance will change. These changes in performance can be measured in the gas path of the engine. Using GPA the degraded component can be identified, after which corrective steps can be taken. This analysis gives more insight into the condition of a gas turbine than the traditional method in which only the Exhaust Gas Temperature (EGT) margin is taken into account. At KLM ES GPA is performed using Gas turbine Simulation Program (GSP), currently with the capability to analyse the General Electric (GE) CF6-80 and CFMI CFM56-7B. Recently KLM ES has started maintenance on the GE GEnx-1B, powering the Boeing 787. The GEnx-1B is a state of the art aircraft engine, capable of collecting large amounts of performance data while in operation. KLM ES is not yet able to apply GPA to the GEnx-1B. The objective of this research is to extend the use of GPA at KLM ES to the GEnx-1B, by creating a model in GSP capable of providing accurate GPA results for the GEnx-1B using both test cell and on-wing measurements. A GSP model has been created based on test cell measurements, taken at different power settings, of an average GEnx-1B. The range of power settings over which the model is capable of simulating the engine has been extended using take-off snapshots taken on-wing. Off-design performance simulation in GSP is based on component maps, describing the performance of the compressors and turbines of a gas turbine. Component maps describing the behaviour of the GEnx-1B components are proprietary to GE and unavailable in the public domain. Therefore maps representing a CF6-80, which are based on publicly available maps and are accessible at KLM ES, have been tuned to match the measured GEnx- 1B performance. The tuned model is capable of simulating the reference engine in take-off conditions well. Some modelling errors remain, however these do not hamper the usability of the model of GPA. Although the GEnx-1B is a much more modern engine than a CF6-80, it has fewer sensors installed in its gas path. Most importantly no pressure measurement is taken in the fan bypass, after the booster and after the High Pressure Turbine (HPT). During modelling this increased the uncertainty involved in tuning the component maps and required additional assumptions to be made to fix the model in the design point. Furthermore it has its effect on the application of GPA using GSP. The GPA method implemented in GSP is Adaptive Modelling (AM). AM is directly dependent on the amount of parameters measured in an engine, it requires an equal amount of available measurements as engine parameters it can adapt to compute an engine its condition. As previous work focused on engines with more measurements available, more research was required to investigate the possibility of applying AM to the GEnx-1B. It is found that the lack of a pressure sensor after the HPT affects GPA the most. With this sensor missing, deterioration on the Low Pressure Turbine (LPT) is contributed to an HPT efficiency loss by the AM component. Having no pressure measurement after the booster does not severely impact AM results. Lack of a thrust measurement, or equivalent pressure measurement, results in having no information available on the fan when on-wing snapshots are analysed. Overall it was concluded that the model would still be usable to analyse the condition of GEnx-1Bs in operation, or in the test cell after maintenance. The model has been tested using snapshots of test cell measurements available from multiple engines and using on-wing take-off snapshots of the whole KLM fleet. Results based on test cell snapshots correlate well with the work done during the shopvisits and with the EGT margin as reported by the test cell software. Analysis of on-wing snapshots also shows a good relation with the EGT margin as computed by the engine. Furthermore deterioration is attributed to the components on which it would be expected. On-wing results are affected by scatter, which also influences the choice of reference dataset. A suggestion is made for a reference dataset, enabling the analysis of all KLM GEnx-1Bs considered. Overall it is proved that it is possible to analyse a modern engine such as the GEnx-1B with GSP, both in the test cell and in operation.

Current unmanned aerial vehicles (UAVs) are limited in operating altitude and endurance by the maximum attainable performance of small gas turbine engines. While improvements in the cycle thermal efficiency can be obtained through the introduction of heat exchangers, the consequent increase in engine weight partially offsets the benefits gained in specific fuel consumption (SFC). Conceptual studies on semi-closed cycles, where part of the engine mass flow rate is recirculated through the engine core, have shown that a significant reduction in engine weight can be obtained in comparison with equivalent open cycle solutions. This thesis presents a preliminary design and optimization study of semi-closed cycle engines for high altitude UAV applications. A detailed thermodynamic and mechanical model has been created to correlate component performance and weight variation as function of thermodynamic design parameters. The model has been coupled with a multi-objective optimization algorithm to optimize various engine cycles and to assess the trade-off between minimum SFC and minimum engine weight. Results have shown that a considerable degree of compactness can be obtained with this novel configuration, leading to an overall engine weight approximately two time lower than conventional open cycles.