Research on Optimization Algorithms for Fluid Machinery, Turbomachinery, and Rotating Equipment

Dear Colleagues,

Fluid machinery, including pumps, fans, compressors, turbines, blowers, and hydraulic systems, is widely used in energy, aerospace, chemical processing, water management, and many other industries. Their performance directly influences energy consumption, operational reliability, and overall system efficiency. In recent years, the growing demand for higher efficiency, reduced environmental impact, and enhanced durability has driven the need for sophisticated design and operational strategies. Optimization algorithms have emerged as powerful tools to tackle these complex engineering challenges, enabling researchers and engineers to explore large design spaces, improve geometries, optimize control strategies, and achieve multi-objective trade-offs. From classical gradient-based methods to modern metaheuristics, surrogate-based optimization, and machine learning-assisted approaches, the application of optimization techniques in fluid-flow machines continues to expand.

This Special Issue on “Research on Optimization Algorithms for Fluid Machinery, Turbomachinery, and Rotating Equipment” seeks high-quality works focusing on the development and application of optimization algorithms for fluid machinery, turbomachinery, and related rotating equipment, highlighting novel methodologies, practical case studies, and integrated frameworks that combine computational fluid dynamics (CFD), optimization, and data-driven techniques. Topics include, but are not limited to, the following:

• Development and application of optimization algorithms (e.g., genetic algorithms, particle swarm optimization, simulated annealing, gradient-based methods) for pumps, compressors, turbines, fans, and blowers;
• Multi-objective optimization considering performance, efficiency, noise, durability, and cost;
• Surrogate-assisted optimization and metamodeling for computationally expensive CFD simulations;
• Shape and topology optimization of blades, impellers, diffusers, casings, and other components;
• Optimization of control strategies for variable-speed or variable-geometry fluid machinery;
• Integration of machine learning and artificial intelligence with optimization for fluid system design and operation;
• Robust optimization under uncertainties in operating conditions, manufacturing tolerances, or material properties;
• Optimization of fluid machinery for renewable energy applications (e.g., wind turbines, hydro turbines, tidal turbines);
• Case studies demonstrating optimization in real-world industrial fluid-flow systems;
• Software tools, algorithms, and frameworks dedicated to fluid machinery optimization.

We look forward to receiving your contributions to help to advance this important field.

Dr. Yanyan Feng
Dr. Jianyang Yu
Dr. Dewei Fan
Prof. Dr. Huawei Lu
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. Processes 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 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.

• optimization algorithms
• fluid-flow machines
• computational fluid dynamics (CFD)
• metaheuristics (e.g., genetic algorithms, particle swarm optimization)
• surrogate-assisted optimization
• multi-objective design
• turbomachinery (pumps, turbines, compressors, fans, etc.)
• shape and topology optimization
• machine learning in fluid mechanics
• robust design under uncertainty