AI-Enabled Fluid Dynamics: The Future Revolution of Intelligent Fluid Technology
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".
Deadline for manuscript submissions: 25 March 2026 | Viewed by 11
Special Issue Editors
Interests: artificial intelligence; fluid dynamics; intelligent fluid control; fluid optimization
Interests: machine learning; deep learning; numerical simulation; complex fluid systems
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent electro-hydraulic control; energy recovery; hydraulic hybrid power; sealing and lubrication
Special Issue Information
Dear Colleagues,
With the rapid development of AI (Artificial Intelligence) technologies, the application of AI in fluid dynamics and hydraulic technology is becoming increasingly widespread, particularly in areas such as fluid simulation, control, optimization design, sealing, and lubrication. AI is leading a technological revolution in these fields. Traditional hydraulic system design and optimization often rely on empirical formulas and manual adjustments, whereas AI empowers hydraulic technology to make these processes more intelligent, precise, and automated, thereby improving system efficiency and reliability.
This Special Issue aims to explore the innovative applications of AI in hydraulic technology, combining advanced CFD and intelligent control algorithms to enhance the performance and intelligence of hydraulic systems. We are particularly interested in the applications of AI in hydraulic system optimization, sealing and lubrication design, and fluid dynamics simulations—including, but not limited to, machine learning-based hydraulic system performance prediction, AI-driven real-time fluid control and optimization, hydraulic system fault diagnosis, and predictive maintenance.
In addition, as the importance of sealing and lubrication technology in hydraulic systems continues to grow, the integration of AI and CFD provides new approaches for optimizing sealing designs and lubrication processes. This Special Issue welcomes innovative research in these directions, especially in areas such as simulation technologies that combine AI and fluid dynamics, intelligent diagnostics, and fluid dynamics modeling.
With the publication of this Special Issue, we will provide a platform the latest advancements in the integration of hydraulic technology and AI, promote the research and industrial applications of intelligent hydraulic systems, and further advance the optimization and innovation of hydraulic technology across various industries.
Prof. Dr. Ruichuan Li
Dr. Lintao Wang
Dr. Jikang Xu
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. Processes 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
- hydraulic technology
- adaptive control
- computational fluid dynamics
- fluid dynamics
- hydraulic system simulation
- fault diagnosis
- sealing and lubrication
- artificial intelligence
- machine learning
- deep learning
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