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- Aslanidou, Ioanna, et al.
(författare)
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Teaching gas turbine technology to undergraduate students in Sweden
- 2018
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791851128
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the teaching of gas turbine technology in a third-year undergraduate course in Sweden and the challenges encountered. The improvements noted in the reaction of the students and the achievement of the learning outcomes is discussed. The course, aimed at students with a broad academic education on energy, is focused on gas turbines, covering topics from cycle studies and performance calculations to detailed design of turbomachinery components. It also includes economic aspects during the operation of heat and power generation systems and addresses combined cycles as well as hybrid energy systems with fuel cells. The course structure comprises lectures from academics and industrial experts, study visits, and a comprehensive assignment. With the inclusion of all of these aspects in the course, the students find it rewarding despite the significant challenges encountered. An important contribution to the education of the students is giving them the chance, stimulation, and support to complete an assignment on gas turbine design. Particular attention is given on striking a balance between helping them find the solution to the design problem and encouraging them to think on their own. Feedback received from the students highlighted some of the challenges and has given directions for improvements in the structure of the course, particularly with regards to the course assignment. This year, an application developed for a mobile phone in the Aristotle University of Thessaloniki for the calculation of engine performance will be introduced in the course. The app will have a supporting role during discussions and presentations in the classroom and help the students better understand gas turbine operation. This is also expected to reduce the workload of the students for the assignment and spike their interest.
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| 2. |
- Pontika, E. C., et al.
(författare)
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Aeroengines : Multi-platform application for aero engine simulation and compressor map operating point prediction
- 2019
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791858677
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Konferensbidrag (refereegranskat)abstract
- This paper presents the development of AeroEngineS (Aircraft Engine Simulation), a multi-platform app with graphical user interface for aero engine simulation and compressor map operating point prediction. Gas turbine performance simulation is a crucial part of the design process. It provides information about the required operating conditions of all the components and the overall performance of the engine so that engineers can determine whether the current engine configuration meets the performance requirements. Some gas turbine simulation programs have been developed in the last decades, however, there was a lack of an open-source, lightweight, user-friendly, but still very accurate, application which would be easily accessible from all platforms. AeroEngineS can be used as a user-friendly preliminary design tool, since, during this design phase, details about the geometry are not known yet. The main aim is to calculate simply and quickly the basic parameters of the thermodynamic cycle and the performance, in order to determine which design is able to meet the required specifications. AeroEngineS constitutes a free and simple app which can primarily serve educational purposes as it is easily accessible by students from any platform to assist them in aero engine technology courses. Secondarily, it has the potential to be used even by engineers as a quick tool accessible from all devices. The app consists of two basic stand-alone functions. The first function is aero engine simulation at Design Point which solves thermodynamic calculations. The second function is compressor map operating point prediction using a novel method of combining scaling techniques and Artificial Neural Networks.
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