| 1. |
- Fentaye, Amare Desalegn, et al.
(författare)
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Hybrid model-based and data-driven diagnostic algorithm for gas turbine engines
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
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Konferensbidrag (refereegranskat)abstract
- Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.
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| 2. |
- Ferrari, M. L., et al.
(författare)
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Pressurized SOFC system fuelled by biogas : Control approaches and degradation impact
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
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Konferensbidrag (refereegranskat)abstract
- This paper shows control approaches for managing a pressurized Solid Oxide Fuel Cell (SOFC) system fuelled by biogas. This is an advanced solution to integrate the high efficiency benefits of a pressurized SOFC with a renewable source. The operative conditions of these analyses are based on the matching with an emulator rig including a T100 machine for tests in cyber-physical mode (a real-time model including components emulated in the rig, operating in parallel with the experimental facility and used to manage some properties in the plant, such as the turbine outlet temperature set-point and the air flow injected in the anodic circuit). The T100 machine is a microturbine able to produce a nominal electric power output of 100 kW. So, the paper presents a real-time model including the fuel cell, the off-gas burner, and the recirculation lines. Although the microturbine components are planned to be evaluated with the hardware devices, the model includes also the T100 expander for machine control reasons, as detailed presented in the devoted section. The simulations shown in this paper regard the assessment of an innovative control tool based on the Model Predictive Control (MPC) technology. This controller and an additional tool based on the coupling of MPC and PID approaches were assessed against the application of Proportional Integral Derivative (PID) controllers. The control targets consider both steady-state (e.g. high efficiency solutions) and dynamic aspects (stress smoothing in the cell). Moreover, different control solutions are presented to operate the system during fuel cell degradation. The results include the system response to load variations, and SOFC voltage decrease. Special attention is devoted to the fuel cell system constraints, such as temperature and time-dependent thermal gradient. Considering the simulations including SOFC degradation, the MPC was able to decrease the thermal stress, but it was not able to compensate the degradation. On the other hand, the tool based on the coupling of the MPC and the PID approaches produced the best results in terms of set-point matching, and SOFC thermal stress containment.
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| 3. |
- Gkoutzamanis, V. G., et al.
(författare)
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Conceptual design and energy storage positioning aspects for a hybrid-electric light aircraft
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
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Konferensbidrag (refereegranskat)abstract
- This work focuses on the feasibility of a 19-passenger hybrid-electric aircraft, to serve the short-haul segment within the 200-600 nautical miles. Its ambition is to answer some dominating research questions, during the evaluation and design of aircraft based on electric propulsion architectures. The potential entry into service of such aircraft is foreseen in 2030. A literature review is performed, to identify similar concepts that are under research and development. After the requirements definition, the first level of conceptual design is employed. Based on a set of assumptions, a methodology for the sizing of the hybrid-electric aircraft is described to explore the basis of the design space. Additionally, a methodology for the energy storage positioning is provided, to highlight the multidisciplinary aspects between the sizing of an aircraft, the selected architecture (series/parallel partial hybrid) and the energy storage operational characteristics. The design choices are driven by the aim to reduce CO2 emissions and accommodate boundary layer ingestion engines, with aircraft electrification. The results show that it is not possible to fulfill the initial design requirements (600 nmi) with a fully-electric aircraft configuration, due to the farfetched battery necessities. It is also highlighted that compliance with airworthiness certifications is favored by switching to hybrid-electric aircraft configurations and relaxing the design requirements (targeted range, payload, battery technology). Finally, the lower degree of hybridization (40%) is observed to have a higher energy efficiency (12% lower energy consumption and larger CO2 reduction), compared to the higher degree of hybridization (50%), with respect to the conventional configuration.
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| 4. |
- Mantelli, L., et al.
(författare)
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A degradation diagnosis method for gas turbine - fuel cell hybrid systems using Bayesian networks
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
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Konferensbidrag (refereegranskat)abstract
- During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system. This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell - gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.
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| 5. |
- Rendon, M. A., et al.
(författare)
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Energy management of a hybrid-electric aeronautical propulsion system to be used in a stationary test bench
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
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Konferensbidrag (refereegranskat)abstract
- Environmental requirements have led the air transportation industry to work towards reducing greenhouse gas emissions and mechanical noise levels. Nowadays, this sector contributes with 2% of the total greenhouse gas emissions, and there is a demand from global aviation regulators for further reducing this percentage. In the last years, the development of Hybrid-Electric Propulsion Systems (HEPSs) has grown. The HEPS combines an Internal Combustion Engine (ICE), for example, Gas Turbine (GT) or reciprocating engine, with an Electric Motor (EM), combining the inherent advantages of both. HEPSs present increased efficiency and operating safety in comparison with conventional ICE-powered systems. Furthermore, they can supply the electrical devices with power. This area of study is multidisciplinary in nature and, therefore, poses research challenges on ICEs, EMs, electronic converters, propeller design, monitoring and control systems, management and supervision systems, energy efficiency and optimization, aerodynamics and aircraft mechanical design. A research project aimed at the characterisation of hybrid-electric aircraft propulsion systems, and the construction of a HEPS prototype, is underway in Brazil. The system is essentially composed of a GT, an EM, three electronic converters, a battery bank and a propeller. It can operate with three different topologies: series, full-electric and turbo-electric. A test bench with all the necessary peripheral and analysis infrastructure is under construction. Present work aims to: (i) develop simplified models for all the test bench components, (ii) given a mission profile, show the results of an initial energy management computing code that determines the optimal hybridization strategy, and (iii) simulate various operating alternatives for the chosen mission profile. The results (i) highlight the impact of critical characteristics of the batteries on the HEPS performance, and (ii) demonstrate the application of the management code on optimizing the aircraft energy consumption for a given mission profile.
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