The aim of this work is to preliminary model a hybrid propulsion system that integrates fuel cells and a turbo-generator. The model is used to predict the performance of the propulsion system where parameters such as fuel consumption, and component efficiencies are monitored throughout a given eVTOL mission. Based on the analysed performance, the maximum payload the eVTOL can carry is estimated. With a fuel cell voltage efficiency of 43%, an eVTOL of MTOW of 3175 kg and a structural weight of 1905 can carry a maximum of 174 kg as a payload. The eVTOL propulsion system weights approximately 1041 kg, to which the fuel cell and its balance of plant components contributes the highest with 819 kg, where the turbogenerator contributes with 222 kg. According to the models prediction, the payload can be increased to approximately 200 kg , if a compressor efficiency of 85% was used.

In the scope of global decarbonisation, hydrogen has been identified as an essential energy carrier. Not only as an energy storage solution for further electrification in vehicles but also as a primary fuel source in processes that rely on chemical energy which cannot be easily electrified. However, at ambient conditions, hydrogen is also a very sparse gas with a low energy density. This requires it to be either liquified or compressed as a gas for most storage and transport solutions, both of which are rather energy-intensive processes. In the case of compressed hydrogen this leads to a significant amount of potential energy stored in the storage tanks. In current applications, the high pressure hydrogen is then expanded through a valve to reach the system’s desired working pressure. A process in which most of the potential energy stored in the compressed is lost to thermal effects.

The research in this thesis is focused on identifying a suitable application in which an expander could be applied to recover part of the potential energy stored in compressed hydrogen and to provide a conceptual design and a thermodynamical analysis of this expander to assess its efficiency and feasibility and has been caried out in collaboration with Ayed Engineering GmbH.
The literature study provided an overview of the current hydrogen landscape and the potential applications in which an expander could be applied. These have been evaluated and led to the selection of a hydrogen refuelling station as a suitable application for this purpose.

A system model has been developed to replicate a refuelling process in a hydrogen refuelling station and to serve as an operating environment for the to-be-designed expander. Based on the operating parameters of the hydrogen refuelling station, a number of expander designs have been developed, each optimized for the operating conditions in the station at a certain timestep. Empirical loss models have been applied to assess the performance of each of these designs across the entire operating range. From this, it was possible to find which potential design would provide the best overall performance. This selected design and the operating conditions at its optimal performance point have then been used in a 3D CFD validation case using Ansys CFX.

The results indicate that employing a radial inlet turbine for the recovery of potential energy stored in compressed hydrogen in a hydrogen refuelling station can indeed lead to notable operational cost savings but also that there are several practical challenges which remain to be solved. Specifically, the extremely high rotational speeds that are required in operation and the miniature design features of the turbine design. To address these, a sensitivity analysis of several system parameters and design assumptions has been included to provide guidance for future work.

As turbofan technology advances, periodic engine inspection and maintenance still remain a significant part of aircraft operational costs. Operators are thus looking to engine condition-based maintenance (ECBM), leveraging sensor data and numerical engine models for continuous diagnostics. KLM Engine Services developed a surrogate model based on the High Dimensional Model Representation approach, capable of processing a large volume of engine data at a lower computational cost to estimate engine component condition. This study proposes enhancements to expand the model's capabilities, incorporating additional engine parameters and extending the operational envelope. The augmented surrogate model was then combined with a Long Short Term Memory network capable of predicting component condition based on trends generated from the surrogate model. The framework proposed has demonstrated the potential advantages of combining the surrogate and prediction model for engine diagnostics and prognostics, serving as a valuable starting point for future ECBM projects at KLM ES.

Aero engines are critical components in aviation, relying on thermodynamics to convert fuel energy into thrust. Maintenance of these engines constitutes a significant portion of operational costs. Gas path analysis (GPA) is essential for engine health management, relying on performance modeling to simulate engine operations accurately. This project outlines a comprehensive study aimed at enhancing gas path modeling, conducted in collaboration with KLM Engine Services. The objective was to create a highly accurate model for simulating aero engines, utilizing a wealth of operational data for improved accuracy. The study focuses on the GEnx-1b turbofan model, addressing flaws in the existing approach, particularly related to Reynolds effects. Novel "Off design (OD) functions" are introduced to enhance model accuracy, and a systematic methodology for model enhancement is presented. The newly corrected model was validated against operational flight data, demonstrating high accuracy in simulating engine performance, with promising implications for maintenance efficiency.

Over the years, aero engines have evolved into the efficient turbofans present on commercial airliners today. Although these engines are very reliable, they still experience degradations in performance and reductions in component health. The degradation affects the operation of the engine, which can be measured using the pressure, temperature, and rotational speed sensors. Using accurate engine models together with engine sensor measurements, the condition of individual components can be determined. Surrogate models of a non-linear Gas Path Analysis model have been developed for the GEnx-1B turbofan in the form of High Dimensional Model Representations. It is determined that the surrogate models developed are able to determine component conditions with a high accuracy and low computational complexity. The models are able to properly identify the effects of water-washes and turbine failures when applied to real-world on-wing data. Due to the low computational complexity of the models, they provide the possibility for fleet-wide continuous engine diagnostics.

Engine maintenance needs to be planned timely and strategically. This can only be done if the health of the engine can be predicted, which requires an accurate engine performance model. Developing such a model is becoming increasingly challenging, due to the reduced number of sensors in modern gas turbines. Besides this, a lot of data is proprietary and not available to the maintenance provider. This leads to the objective of the thesis: Improve Gas Path Analysis at KLM Engine Services, by developing a systematic approach for modelling modern turbofan engines using sensor measurements and general physical relations. For the on-design modelling general physical relations were used to bridge the gap caused by the reduced amount of data. The off-design modelling was done by scaling baseline maps from open literature using second order polynomials. The outcome was verified by developing two engine models in parallel and the validation was done using on-wing data.

Gas path analysis (GPA) plays a major role in turbofan condition monitoring. GPA uses thermodynamic engine models to identify deterioration of individual turbofan components. The accuracy of GPA results depends on the quality of the engine models. Turbofan performance is influenced by secondary performance parameters, which include variable geometry, active clearance control, bleeds flows and power off-take. Hence, if not accounted for in engine models, it decreases GPA accuracy.
A method is proposed to increase the accuracy of engine models by accounting for secondary performance parameters. Combining an evolutionary algorithm with on-wing engine data has resulted in a novel approach to determine relationships between secondary performance parameter settings and turbofan component performance deviation. Results show that accounting for these relationships in the engine models increases the model accuracy. Consequently, an accuracy increase of the GPA results is achieved. The method is verified with simulated data and validated with GEnx-1B on-wing engine data.

In this thesis, a hybrid Gas Path Analysis (GPA) tool is developed for next-generation turbofan engine condition monitoring purposes at KLM Engine Services. The main drawback of these new engines is that fewer gas path sensors are installed. However, this is compensated by a greater quantity in-flight data, including information on bleed valves, active clearance control systems and variable geometry positions. With the availability of this data optimal near steady-state operating points can be selected and a Multiple Operating Point Analysis (MOPA) can be implemented. Then, an Evolutionary Algorithm (EA) optimization approach is combined with the non-linear GPA program GSP in order to predict engine component health parameter deviations. Using this method it is possible to track fan, LPC, HPC, HPT and LPT deterioration. The tool has been verified with simulated data and validated using on-wing data from the General Electric GEnx-1B engine.

Engine performance diagnostics play a major role in keeping track of gas turbine condition and performance and identification of possible errors or faults. One of the key performance indicators used in engine performance diagnostics is the Hot Day Exhaust Gas Temperature Margin (EGTM). Ambient humidity affects the EGTM, however humidity is not measured on current civil aircraft and consequently introduces inaccuracy in the reported EGTM. Research has been performed investigating the effect of humidity on turbofan performance, to provide corrections for the reported EGTM for ambient humidity. Test-cell corrections, simulations and historical data have been reflected and were found to be very comparable. Corrections have been developed increasing reported EGTM accuracy. However not negligible, the overall effect of humidity on the inaccuracy of the EGTM is found to be small. It is therefore concluded that correcting the EGTM for humidity is possible but will increase EGTM accuracy only slightly.

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.

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.

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.

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.

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.