Centre of Applied Research Technology
Dr A. Apostolidis (Asteris)
Associate Professor of Maintenance, Repair and Overhaul (MRO) EngineeringDr Asteris Apostolidis has a broad technical expertise, having worked for academic institutes, airlines and aircraft manufacturers in four different countries. He has dealt with the technical and strategic problematics of the design, operation and maintenance of aircraft, having led numerous design projects and research initiatives in both the industry and academia. He is currently appointed as an Associate Professor at the Aviation Academy of the Faculty of Technology at Amsterdam University of Applied Sciences.
Asteris has previously worked in for Air France-KLM, in the areas of Technology Innovation Management and Strategy Development, as a member of the Group Strategy Technology Office. He acted as an internal consultant to numerous Group businesses at an Executive level. He has also worked for Air France Industries KLM Engineering & Maintenance in the areas of Predictive Maintenance, Data Analytics, Prognostics & Diagnostics, using both physics-based and data-driven methods for gas turbine engines and aircraft systems.
Asteris has also worked as an engineering consultant for a number of major European aerospace manufacturers, including Airbus, Safran, Rolls-Royce and Liebherr Aerospace in the areas of aerothermal modelling and performance of aircraft engines and systems, contributing to projects such as the certification campaign of the A320neo, the design of the LEAP engine and the post-certification support of the Trent 900 engine.
He currently sits in a number of Technical and Expert Committees and Working Groups in the fields of Artificial Intelligence in Aviation & Aerospace, Future Aircraft Architectures and Sustainable Transformation. He co-chair the Data Management & Validation subgroup of the SAE G-34 / EUROCAE WG-114 Joint International Committee for the certification of Artificial Intelligence-based Technologies in Aviation. Asteris is a Guest Columnist for Aerospace America, published by the American Institute of Aeronautics and Astronautics.
Dr Asteris Apostolidis holds a PhD in Computational Aerothermodynamics and an MSc in Aerospace Propulsion from Cranfield University (UK) and a Dipl.-Ing. in Mechanical Engineering, with a specialisation in Aeronautics from Aristotle University of Thessaloniki (Greece).
His areas of interest are (but not restricted to) the following:
- Aviation Maintenance, Repair & Overhaul
- Novel Aircraft & Propulsion Architectures
- Sustainability in Aviation & Aeronautics
- Artificial Intelligence in Aeronautics
- Simulation Methods & Digital Twins
- Power Production & Energy Policy
- Innovation Strategy Development
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AI-based exhaust gas temperature
Apostolidis, A., Bouriquet, N., & Stamoulis, K. (2022). AI-based exhaust gas temperature: prediction for trustworthy safety-critical applications. Aerospace, 9(11), [722]. https://doi.org/10.3390/aerospace9110722
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An End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines
Kefalas, M., van Stein, B., Baratchi, M., Apostolidis, A., & Bäck, T. (2022). An End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines. In P. Do, G. Michau, & C. Ezhilarasu (Eds.), Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022 (1 ed., Vol. 7, pp. 245-260). PHM Society. https://doi.org/10.36001/phme.2022.v7i1.3317
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Explainable artificial intelligence for exhaust gas temperature of turbofan engines
Kefalas, M., de Santiago Rojo Jr., J., Apostolidis, A., Van Den Herik, D., van Stein, B., & Back, T. (2022). Explainable artificial intelligence for exhaust gas temperature of turbofan engines. Journal of Aerospace Information Systems, 19(6), 447-454. https://doi.org/10.2514/1.I011058
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Explainable and Interpretable AI-Assisted Remaining Useful Life Estimation for Aeroengines
Protopapadakis, G., Apostolidis, A., & Kalfas, A. I. (2022). Explainable and Interpretable AI-Assisted Remaining Useful Life Estimation for Aeroengines. In Proceedings of ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition (GT2022 ed., Vol. 2, pp. 1-12). [GT2022-80777, V002T05A002] (Turbo Expo: Power for Land, Sea, and Air). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/GT2022-80777
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Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines
Kefalas, M., Baratchi, M., Apostolidis, A., van den Herik, D., & Bäck, T. (2021). Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines. In 2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021 (2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icphm51084.2021.9486549
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Decarbonizing by 2050
Apostolidis, A. (Author). (2021). Decarbonizing by 2050: optimists, pessimists and realists. Web publication/site, Aerospace America. https://aerospaceamerica.aiaa.org/departments/decarbonizing-by-2050-optimists-pessimists-and-realists/
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A health monitoring modelling case study
Apostolidis, A., & Stamoulis, K. P. (2021). A health monitoring modelling case study: humidity effects on engine deterioration prediction. In MATEC web of conferences (Vol. 349). [03011] https://doi.org/10.1051/matecconf/202134903011
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An AI-based Digital Twin Case Study in the MRO Sector
Apostolidis, A., & Stamoulis, K. P. (2021). An AI-based Digital Twin Case Study in the MRO Sector. Transportation Research Procedia, 56, 55-62. https://doi.org/10.1016/j.trpro.2021.09.007
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Don’t sideline environmental sustainability
Apostolidis, A. (Author). (2020). Don’t sideline environmental sustainability. Web publication/site, Aerospace America. https://aerospaceamerica.aiaa.org/departments/dont-sideline-environmental-sustainability/
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Aviation Data Analytics in MRO Operations: Prospects and Pitfalls
Apostolidis, A., Pelt, M., & Stamoulis, K. P. (2020). Aviation Data Analytics in MRO Operations: Prospects and Pitfalls. In T. Myklebust, T. Stålhane, G. D. Jenssen, & I. Wærø (Eds.), Annual Symposium on Reliability and Maintainability (RAMS) [9153694] (Proceedings - Annual Reliability and Maintainability Symposium; Vol. 2020-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAMS48030.2020.9153694
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A tabu search-based memetic algorithm for the multi-objective flexible job shop scheduling problem
Kefalas, M., Limmer, S., Apostolidis, A., Olhofer, M., Emmerich, M. T. M., & Bäck, T. H. W. (2019). A tabu search-based memetic algorithm for the multi-objective flexible job shop scheduling problem. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 1254-1262). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3326817
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Data mining in MRO
Pelt, M., Apostolidis, A., de Boer, R. J., Borst, M., Broodbakker, J., Jansen, R., Helwani, L., Patron, R. F., & Stamoulis, K. (2019). Data mining in MRO. Hogeschool van Amsterdam, Faculteit Techniek. https://www.amsterdamuas.com/binaries/content/assets/subsites/aviation/data-mining-in-mro/data-mining-in-mro---publication-auas-2019.pdf?1559025759404
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A review: Prognostics and health management in automotive and aerospace
Nguyen, V. D., Kefalas, M., Yang, K., Apostolidis, A., Olhofer, M., Limmer, S., & Bäck, T. (2019). A review: Prognostics and health management in automotive and aerospace. International Journal of Prognostics and Health Management, 10(2), [023]. https://doi.org/10.36001/ijphm.2019.v10i2.2730
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Data analytics case studies in the maintenance, repair and overhaul (MRO) industry
Pelt, M., Stamoulis, K., & Apostolidis, A. (2019). Data analytics case studies in the maintenance, repair and overhaul (MRO) industry. In S. Pantelakis , & C. Charitidis (Eds.), MATEC Web of Conferences (Vol. 304). [04005] https://doi.org/10.1051/matecconf/201930404005
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Turbine cooling and heat transfer modelling for gas turbine performance simulation
Apostolidis, A. (2015). Turbine cooling and heat transfer modelling for gas turbine performance simulation. [Research external, graduation external, Cranfield University]. Cranfield University. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.650163
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A High Resolution Turbine Cooling Prediction Method for Performance and Mechanical Integrity Calculations
Apostolidis, A., Laskaridis, P., & Pilidis, P. (2013). A High Resolution Turbine Cooling Prediction Method for Performance and Mechanical Integrity Calculations. In XXI International Symposium on Air Breathing Engines (ISABE 2013): Challenges in Technology Innovation: Global Collaboration (Vol. 1, pp. 1-10). American Institute for Aeronautics and Astronautics (AIAA).
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WebEngine: A Web-Based Gas Turbine Performance Simulation Tool
Apostolidis, A., Sampath, S., Laskaridis, P., & Singh, R. (2013). WebEngine: A Web-Based Gas Turbine Performance Simulation Tool. https://doi.org/10.1115/GT2013-95296