Structural Dynamics, Optimization, and Vibration Assessment in Turbomachinery
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Turbomachinery".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 194
Special Issue Editors
Interests: turbomachinery; structural dynamics; AI/ML in applied mechanics
Interests: mechanical systems diagnostic; rotating machines; wind engineering; wind energy; renewable energy technologies; wind turbines; wind; power systems analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Turbomachinery—including turbines, compressors, fans, and pumps—underpins energy, aerospace, and industrial systems that operate under demanding thermal, structural, and dynamic loads. These conditions make structural optimization and vibration behavior central to performance, safety, and reliability. Advances in computational mechanics, multi‑physics modeling, high‑fidelity sensing, and data‑centric engineering are enabling sharper predictions of dynamic response and more robust design choices.
Alongside established modeling and testing practices, artificial intelligence and machine learning now contribute to structural dynamics—accelerating optimization, improving vibration prediction, and enhancing early fault detection. Used with digital twins and physics‑consistent surrogate models, these tools open opportunities to design lighter, more efficient, and more resilient turbomachinery.
This Special Issue brings together recent progress in structural optimization, rotor dynamics, vibration assessment, and intelligent diagnostics for turbomachinery. We welcome original research articles, reviews, and research ideas that blend theory, simulation, and experiment, including studies that integrate AI/ML, advanced signal processing, and smart sensing with multi‑physics analysis and validation.
Topics of interest include, but are not limited to:
- Structural optimization of turbomachinery components;
- Rotor dynamics, flow‑induced vibration, and aeroelastic effects;
- AI/ML in structural dynamics and diagnostics
- Data‑driven vibration prediction and condition monitoring;
- Nonlinear dynamics, fatigue, and failure mechanisms;
- Multi‑physics modeling, FEM/CFD integration, and experimental validation;
- Digital twins and real‑time assessment; and
- Advanced/additive manufacturing for turbomachinery.
I look forward to receiving your contributions.
Dr. Amr Mohamed Metwally Ismaiel
Dr. Francesco Castellani
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. Machines 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
- structural optimization
- vibration analysis
- nonlinear dynamics
- fatigue and failure analysis
- physics‑informed learning
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