Previous Issue
Volume 12, 01
 
 

Int. J. Thermofluid Sci. Technol., Volume 13, Issue 1 (September 2026) – 4 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Select all
Export citation of selected articles as:
21 pages, 25293 KB  
Article
Effect of Non-Uniform Nozzle Vane Tip Clearance on the Aerodynamic Performance of a Supersonic Variable Nozzle Turbine
by Qin Luo, Cong Xiang, Xinguo Lei, Zhen Liu and Zhichao Zhang
Int. J. Thermofluid Sci. Technol. 2026, 13(1), 4; https://doi.org/10.3390/ijtst13010004 - 28 Jul 2026
Abstract
The clearance of the nozzle vane significantly influences the aerodynamic performance of variable nozzle turbines (VNTs), often leading to increased flow losses and performance degradation. Although nozzle vane clearance often exhibits a non-uniform distribution due to corrosion, wear, machining tolerances, or assembly errors, [...] Read more.
The clearance of the nozzle vane significantly influences the aerodynamic performance of variable nozzle turbines (VNTs), often leading to increased flow losses and performance degradation. Although nozzle vane clearance often exhibits a non-uniform distribution due to corrosion, wear, machining tolerances, or assembly errors, the aerodynamic effects of such non-uniform clearance have rarely been investigated. This study aims to fill the research gap regarding the influence of non-uniform nozzle guide vane clearance on tip leakage flow and aerodynamic performance in a supersonic VNT. By systematically examining the flow field features under different clearance profiles via three-dimensional numerical simulations, this work seeks to identify a potential clearance configuration that can reduce flow loss and improve turbine efficiency. The flow losses, tip leakage vortex patterns, and the interaction between the leakage vortex and shock waves are analyzed in detail for different clearance profiles. The results indicate that for a rear-loaded vane profile, the shrinking clearance (SC) configuration yields a lower mass flow rate and higher aerodynamic efficiency compared to the expanding clearance (EC) and uniform clearance (UC) configurations. Specifically, the SC configuration effectively reduces leakage mass flow and vortex intensity. Consequently, the interaction between the leakage vortex and the shock wave is suppressed. This suppression significantly mitigates flow losses, which are primarily driven by the shock–vortex interaction rather than the interaction between the leakage flow and the main flow, thereby enhancing aerodynamic performance. These findings suggest that a rational design of non-uniform clearance profiles can substantially improve the aerodynamic performance of supersonic turbines. Full article
Show Figures

Figure 1

20 pages, 12259 KB  
Article
Turbulent Flow–Thermal Field Prediction Around a Pin-Fin Using Geometry-Aware Multiscale Graph Neural Network
by Riddhiman Raut, Evan M. Mihalko and Amrita Basak
Int. J. Thermofluid Sci. Technol. 2026, 13(1), 3; https://doi.org/10.3390/ijtst13010003 - 30 Jun 2026
Viewed by 256
Abstract
Pin-fins are widely used to enhance heat transfer in compact heat exchangers, turbine cooling passages, and electronic devices, but their complex geometries make accurate thermal–fluid prediction computationally expensive. This paper presents a geometry-aware multiscale (GAMS) graph neural network (GNN) for predicting steady turbulent [...] Read more.
Pin-fins are widely used to enhance heat transfer in compact heat exchangers, turbine cooling passages, and electronic devices, but their complex geometries make accurate thermal–fluid prediction computationally expensive. This paper presents a geometry-aware multiscale (GAMS) graph neural network (GNN) for predicting steady turbulent flow and heat transfer in a two-dimensional channel containing arbitrarily shaped pin-fin geometries. An automated framework integrating geometry generation, meshing, and ANSYS Fluent simulations was developed to construct the training dataset. Pin-fin geometries were parameterized using piecewise cubic splines, generating 1000 unique configurations through Latin Hypercube Sampling. Each simulation was converted into a graph representation, where nodes contained spatial coordinates, normalized streamwise position, one-hot boundary indicators, and signed distance to the nearest wall. These graph-based features were used to train the GNN to predict the temperature, velocity magnitude, and pressure fields directly from geometry. The network achieved excellent predictive accuracy, successfully capturing boundary layers, recirculation zones, and upstream stagnation regions while reducing computational wall time by 2–3 orders of magnitude compared to conventional CFD simulations. Overall, the proposed GNN provides a fast, reliable surrogate modeling framework for complex thermal–fluid flow configurations. Full article
Show Figures

Figure 1

2 pages, 159 KB  
Editorial
Editor-in-Chief’s Editorial: A New Chapter for the International Journal of Thermofluid Science and Technology
by Xiande Fang
Int. J. Thermofluid Sci. Technol. 2026, 13(1), 2; https://doi.org/10.3390/ijtst13010002 - 9 May 2026
Viewed by 375
Abstract
I would like to share news about tremendous progress in the International Journal of Thermofluid Science and Technology (IJTST) [...] Full article
1 pages, 168 KB  
Editorial
Publisher’s Note: International Journal of Thermofluid Science and Technology Joins the MDPI Portfolio
by Carla Aloè
Int. J. Thermofluid Sci. Technol. 2026, 13(1), 1; https://doi.org/10.3390/ijtst13010001 - 9 May 2026
Viewed by 248
Abstract
We are pleased to announce our partnership with the Nanjing University of Aeronautics and Astronautics (NUAA) for the publication of the International Journal of Thermofluid Science and Technology (IJTST) [...] Full article
Previous Issue
Back to TopTop