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Advances in Gas Turbines

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J2: Thermodynamics".

Deadline for manuscript submissions: closed (10 March 2023) | Viewed by 8731

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Special Issue Editors

Prof. Dr. Marian Piwowarski

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Guest Editor

Faculty of Mechanical Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
Interests: highly efficient thermal power plants based on cycles using organic fluids; energy storage systems and intelligent power systems; gas cycles (air, CO2, helium, and others) and combined gas–steam circuits with high efficiency operating on the basis of hard coal, lignite, and other fuels, including various biofuels or hydrogen; development of a completely innovative power plant based on a combination of coal combustion technology and combined gas and steam systems with efficiency above 60%; analyses of high-efficiency cycles for supercritical parameters; theoretical and experimental studies of gas and steam micro-turbo sets

Prof. Dr. Krzysztof Kosowski

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Guest Editor

Faculty of Mechanical Engineering, Gdansk University of Technology, Gabriela Narutowicza Street 11/12, 80-233 Gdansk, Poland
Interests: optimization of main design parameters of turbines, including CFD and artificial intelligence methods; optimization of turbine power plant cycles; turbine dynamics, self-excited vibrations of rotor systems; active control of flow and mechanical vibrations; renewable sources of energy, distributed energy; design and investigations of isothermal micro turbines; design and experimental research of microturbines for ORC power plants; design of micro gas turbines; high-efficiency turbine cycles; energy storage

Special Issue Information

Dear Colleagues,

I am pleased to invite you to contribute to the upcoming Special Issue entitled “Advances in Gas Turbines”, which will be published in the Energies journal. This Special Issue is open to researchers and authors who wish to submit their research and review articles on issues related to modern gas turbine power plants (gas, combined and nuclear power plants) and detailed analyses related to the design of modern gas turbines, compressors, and fans, both axial and radial. Detailed analyses include issues such as aero-acoustics, noise generation and reduction, vibration, flutter, aero-elasticity, heat transfer and blade cooling, secondary, tip clearance and leakage flows, etc. The aim of this Special Issue is to present the latest research in the field of design, modeling, numerical analysis, and measurements in the field of gas turbines, compressors, and fans.

Prof. Dr. Marian Piwowarski
Prof. Dr. Krzysztof Kosowski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

numerical calculations of turbines, compressors, and fans
modern power plants with gas turbines (gas, combined and nuclear power plants)
optimization of turbomachines
computational fluid dynamics (CFD)
vibration, flutter, aero-elasticity
aero-acoustics, noise generation, and reduction
heat transfer and blade cooling
secondary, tip clearance, and leakage flows
experimental measurement

Published Papers (3 papers)

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Research

15 pages, 2327 KiB

Open AccessFeature PaperArticle

Monitoring Metal Wear Particles of Friction Pairs in the Oil Systems of Gas Turbine Power Plants

by Valentin Belopukhov, Andrey Blinov, Sergey Borovik, Mariya Luchsheva, Farit Muhutdinov, Petr Podlipnov, Aleksey Sazhenkov and Yuriy Sekisov

Energies 2022, 15(13), 4896; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134896 - 04 Jul 2022

Cited by 6 | Viewed by 1461

Abstract

In the example of the aviation gas turbine engine the problem of monitoring metal wear particles of friction pairs in the oil systems of gas turbine power plants is considered. The solution based on using the multi-channel cluster single-coil eddy current sensor (CSCECS) with sensitive elements in the form of single circuits is proposed. The CSCECS provides the detection of ferromagnetic and non-ferromagnetic particles and their ranking by several size groups. The sensor is invariant to the size (inner diameter) of the monitored oil pipeline and has high throughput and identical sensitivity across all channels. Two variants of the hardware structure of the debris continuous monitoring system (DCMS) prototype implementing the proposed approach are suggested. The first variant is intended for engine bench tests and contains the CSCECS with integrated preamplifiers and forced air cooling of the electronic modules. The second variant of the DCMS prototype involves the use of the uncooled sensors without built-in electronics and it focuses on operation in autonomous mode not only in bench tests but also during the engine normal operation. A brief description of the DCMS operational algorithm is given. The algorithm is the same for both hardware versions but differs at the software implementation level. The correctness of the algorithm for the detection and size identification of the wear metal particles was verified during the laboratory experiments with a total duration of 5 h and 30 min. The DCMS prototype was also examined during the full-scale engine bench tests. The experiments indicated that the number, size, and magnetic properties of the particles detected by DCMS generally corresponded to the number, size, and magnetic properties of the particles fixed by the MetalSCAN oil debris monitoring system which was used for verification of the DCMS functional capability. The results were also confirmed through laboratory analysis of the wipe samples on the debris filters. However, unlike the existing approaches, the design of the CSCECS additionally made it possible to evaluate the oil flow features in the pipeline of the engine lubrication system. Full article

(This article belongs to the Special Issue Advances in Gas Turbines)

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19 pages, 26410 KiB

Open AccessFeature PaperArticle

Towards Designing an Innovative Industrial Fan: Developing Regression and Neural Models Based on Remote Mass Measurements

by Jacek Czyżewicz, Piotr Jaskólski, Paweł Ziemiański, Marian Piwowarski, Mateusz Bortkiewicz, Krzysztof Laszuk, Ireneusz Galara, Marta Pawłowska and Karol Cybulski

Energies 2022, 15(7), 2425; https://0-doi-org.brum.beds.ac.uk/10.3390/en15072425 - 25 Mar 2022

Cited by 2 | Viewed by 1679

Abstract

This article presents the process of the construction and testing a remote, fully autonomous system for measuring the operational parameters of fans. The measurement results obtained made it possible to create and verify mathematical models using linear regression and neural networks. The process was implemented as part of the first stage of an innovative project. The article presents detailed steps of constructing a system to collect and process measurement data from fans installed in actual operating conditions and the results of analysis of this data. In particular, a measurement infrastructure was developed, defined, and implemented. Measuring equipment was mounted on selected ventilation systems with relevant fans. Systems were implemented that allowed continuous measurement of ventilation system parameters and remote transmission of data to a server where it was regularly analysed and selected for use in the process of modelling and diagnostics. Pearson’s correlation analysis for p < 0.05 indicated that all seven parameters (suction temperature, discharge temperature, suction pressure, current consumption, rotational speed, humidity, and flow) were significantly correlated with efficiency (p < 0.001). A satisfactory level of correlation between the selected parameters measured in actual conditions and the characteristics of the fan and the ventilation system was experimentally verified. This was determined by finding 4 statistically significant parameters at a confidence level of 95%. This allowed the creation of two mathematical models of the fan system and the ventilation system using linear regression and neural networks. The linear regression model showed that the suction temperature, discharge temperature, and air humidity did not affect the fan efficiency (they are statistically insignificant, p > 0.05). The neural model, which considered all measured parameters, achieved the same accuracy as the model based on four significant parameters: suction pressure, current consumption, rotational speed, and flow. Full article

(This article belongs to the Special Issue Advances in Gas Turbines)

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27 pages, 1234 KiB

Open AccessArticle

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

by Martí de Castro-Cros, Manel Velasco and Cecilio Angulo

Energies 2021, 14(24), 8468; https://0-doi-org.brum.beds.ac.uk/10.3390/en14248468 - 15 Dec 2021

Cited by 16 | Viewed by 4875

Abstract

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment. Full article

(This article belongs to the Special Issue Advances in Gas Turbines)

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