Flow Control Across Varying Length Scales: Nanofluidics, Microfluidics and Millifluidics

A special issue of Fluids (ISSN 2311-5521).

Deadline for manuscript submissions: 20 December 2025 | Viewed by 455

Special Issue Editors


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Guest Editor
Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Interests: flow control; heat transfer; hydraulics; computational fluid dynamics; microchannel
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Interests: valve; fluid components; multiphase flow; computational fluid dynamics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
State Key Laboratory of Fluid Power Components and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: flow control; sealing; microfluidics; fluid components

E-Mail Website
Guest Editor Assistant
Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Interests: computational fluid dynamics; valve; hydraulics; heat and mass transfer

Special Issue Information

Dear Colleagues,

Flow control across varying length scales —nanofluidics, microfluidics, and millifluidics—has unlocked transformative opportunities in science and engineering. Each scale presents unique challenges and capabilities, offering tailored solutions to enhance heat and mass transfer in the fine chemical industry and biomedical diagnostics. Nanofluidics focuses on controlling flows at a nanoscale, leveraging unique surface effects and molecular interactions. Microfluidics excels in precision control and integrates with technologies like MEMS, driving breakthroughs in lab-on-a-chip systems and personalized medicine. Finally, millifluidics extends the above principles to larger volumes, bridging the gap between microscopic precision and macroscopic practicality. This Special Issue explores innovations in flow control technologies across these scales, including advanced sensors, actuators, fluid control units, and detection mechanisms. By uniting insights across disciplines and length scales, this Special Issue aims to catalyze innovation, foster cross-disciplinary collaboration, and accelerate the development of scalable solutions for complex fluidic challenges.

Prof. Dr. Jinyuan Qian
Prof. Dr. Zhijiang Jin
Guest Editors

Dr. Wenqing Li
Dr. Zhenhao Lin
Guest Editor Asisstants

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Keywords

  • flow control
  • nanofluidics
  • microfluidics
  • millifluidics

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Published Papers (1 paper)

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Research

25 pages, 3790 KiB  
Article
Studying Inverse Problem of Microscale Droplets Squeeze Flow Using Convolutional Neural Network
by Aryan Mehboudi, Shrawan Singhal and S.V. Sreenivasan
Fluids 2025, 10(8), 190; https://doi.org/10.3390/fluids10080190 - 24 Jul 2025
Viewed by 286
Abstract
We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. [...] Read more.
We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption. Full article
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