Advances in Thermal–Hydraulic and Multiphase Flow Research in Nuclear Engineering

Dear Colleagues,

This Special Issue highlights how data-driven methods can improve fluid mechanics research and applications in nuclear engineering. We welcome work that uses machine learning, modern data analytics, and hybrid physics–data approaches to better understand, predict, and control complex thermal–hydraulic and multiphase flows in nuclear systems, with the goal of supporting safer and more efficient design and operation. Topics of interest include (but are not limited to): Data-driven turbulence modeling and closure strategies for nuclear thermal–hydraulics; Surrogate models and fast prediction tools for CFD and system codes; Hybrid physics–ML models for flow, heat transfer, and mass transfer; multiphase flow: boiling, condensation, two-phase instabilities, and critical heat flux (CHF); Flow-induced vibration, thermal mixing, stratification, and flow distribution; Reduced-order modeling, operator learning, and emulation of high-fidelity simulations; Sensor data + ML for flow monitoring, anomaly detection, and state estimation; Data assimilation and uncertainty quantification for thermal–hydraulic predictions; Experimental + simulation data fusion, benchmark datasets and validation practices; Interpretability, robustness, and V&V of data-driven tools for safety-relevant use. Types of submissions we expect:

We invite original research articles, applied case studies (experimental, numerical, or combined), review papers, technical notes, and dataset/benchmark papers. Contributions with clear validation, uncertainty reporting, and reproducible workflows (data/code when possible) are especially encouraged.

Dr. Xiuzhong Shen
Prof. Dr. Naibin Jiang
Guest Editors

Dr. Guangyun Min
Guest Editor Assistant

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. Fluids 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 1800 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.

• machine learning
• reduced-order modeling
• thermal–hydraulics
• multiphase flow
• CFD simulation
• fluid–structure interaction