Advanced Modeling of Aero-Engine Complex Systems
A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".
Deadline for manuscript submissions: 30 September 2026
Special Issue Editors
2. College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
Interests: aero-engine system safety and airworthiness; thermal protection of aero-engine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Aero-engines are complex thermomechanical systems characterized by deep multidisciplinary coupling and multisystem coordination. High-precision modeling is critical for their design, analysis, optimization, and health management. Core objectives for advanced aero-engine modeling include the following: multidisciplinary coupling (bidirectional simulation coupling across physical fields including aerothermodynamics, structural mechanics, combustion chemistry, etc.), multiscale integration (connecting microscopic combustion chemical reactions to mesoscopic component performance and overall engine performance), and full operating condition coverage (from start-up and idle to maximum and transient states, including non-design points and extreme conditions).
Current modeling techniques face critical challenges, including balancing computational cost and accuracy, overcoming inherent uncertainties in physical models, ensuring the stability and efficiency of multiscale and multidisciplinary coupling algorithms, and managing software integration and data handling.
This Special Issue of Aerospace will focus on cutting-edge research in advanced modeling for aero-engines, encompassing traditional gas turbine engines (turbofans, turboshafts, turboprop engines, etc.), ramjet engines, pulse detonation engines, and emerging new energy propulsion systems (such as fuel cells). We particularly welcome submissions exploring the application of advanced modeling techniques in aero-engine research, including cross-scale and cross-system multi-disciplinary coupling, the integration of artificial intelligence/machine learning with physical models, and digital twin technologies. The editors of this Special Issue invite submissions addressing the current challenges around modeling various aero-engine types and exploring novel methodologies for modeling complex aero-engine systems.
Prof. Dr. Shuiting Ding
Dr. Peng Liu
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 250 words) can be sent to the Editorial Office for assessment.
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. Aerospace is an international peer-reviewed open access monthly 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 2400 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
- aero-engine modeling
- multidisciplinary modeling
- multiscale integration modeling
- algorithm stability
- managing software integration
- artificial intelligence
- machine learning
- digital twin technologies
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