Thermal Design, Thermodynamic Analysis, and Optimization of Aero-Engines and Gas Turbines
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: Thermal Management".
Deadline for manuscript submissions: 22 October 2024 | Viewed by 1567
Special Issue Editors
Interests: advanced cooling technology for turbine blades; optimization design of cooling structure; machine learning in cooling design; equipment thermal design
Interests: artificial neural networks; inverse methods; computational fluid dynamics; numerical optimization; engineering, applied and computational mathematics
Special Issues, Collections and Topics in MDPI journals
Interests: cooling technology for hot-temperature components; cooling structure optimization
Special Issue Information
Dear Colleagues,
With the increasing operating temperatures of aero-engines and gas turbines, the cooling of high-temperature components has become a significant challenge. The development of reliable and efficient cooling structures is therefore essential to ensure the reliable operation of these systems. Traditional cooling methods may not be suitable for the new generation of aero-engines and gas turbines, as they may not provide the required cooling efficiency under extreme operating conditions. As a result, there is a need for novel cooling structures with enhanced cooling capabilities as well as cooling design methods with high efficiency and accuracy.
The aim of this Special Issue is to bring together original research and review articles discussing recent advances in thermal design, thermodynamic analysis, and optimization of aero-engines and gas turbines.
The topics of interest for publication include, but are not limited to, the following:
- Overall thermal design and analysis of hot-temperature components such as blades and combustors;
- Thermal design and analysis of unit cooling structures;
- High-efficiency and high-precision thermal design methods;
- Enhancement of cooling data;
- Machine learning modeling of cooling performance;
- Proposal and optimization of new cooling structures.
Dr. Lei Xi
Dr. Denglong Ma
Dr. Zhen Zhao
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
- high-temperature components
- thermal design
- thermodynamic analysis
- novel cooling structure
- high-performance heat- transfer structure
- machine learning
- data enhancement
- performance prediction
- structural optimization
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A review of machine learning methods in turbine cooling
Authors: Xu Liang, Jin Shenglong, Xi Lei, Li Yunlong, Gao Jianmin
Affiliation: School of Mechanical Engineering, Xi'an Jiaotong University
Abstract: In the current design work, turbine performance requirements are getting higher and higher, and turbine blade design needs multiple rounds of iterative optimization. 3D turbine optimization involves multiple parameters, and 3D simulation takes a long time. Machine learning method can make full use of historical accumulated data to train high-precision data models, which can greatly reduce turbine blade performance evaluation time and improve optimization efficiency. Based on the data model, the advanced intelligent combinatorial optimization technology can effectively reduce the number of iterations, find the better model faster, and improve the optimization calculation efficiency.Based on different cooling parts of turbine blades and the development of machine learning, this paper discusses the feasibility of applying different machine learning models in the field of turbine cooling design.
