Diagnostics and Optimization of Gas Turbine
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 30381
Special Issue Editors
Interests: energy & environment; process control; system analysis; design optimization; mechanical & gas turbine engineering; aerospace; defence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The future trends in the energy market, the increasing penetration of renewable sources, and the need for alternative fuels mean that gas turbines need to operate more flexibly and in advanced/hybrid configurations. At the same time, the variability of energy prices in the current diverse scenario increases the need to reduce operation and ownership costs. This poses new challenges in all phases of engine life: design, manufacturing, operations, monitoring, and maintenance.
Modern computing capabilities and the progress in artificial intelligence and machine learning allow for more and more complex tools. New methods are required for operations optimization, and for engine monitoring, diagnostics, and prognostics. Furthermore, methods for incorporating considerations in diagnostics during the design phase and for the integration of diagnostics, control, and optimization are of interest.
This Special Issue is meant to cover topics related to mathematical modeling, machine learning, optimization, and numerical methods aiming at improving reliability and flexibility, extending the lifetime, and reducing the operating costs of gas turbine systems. Envisaged applications include industrial gas turbines, micro gas turbines, aero-engines, and gas turbines in hybrid configurations.
Prof. Konstantinos Kyprianidis
Dr. Valentina Zaccaria
Guest Editors
Manuscript Submission Information
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Keywords
- diagnostics
- machine learning
- prognostics and lifetime estimation models
- maintenance optimization
- operations optimization and optimal control
- optimization under uncertainty
- sensors and data fusion
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