1. “Centrifugal Compressor Polytropic Performance—Improved Rapid Calculation Results—Cubic Polynomial Methods”
Matt Taher et al.
Int. J. Turbomach. Propuls. Power 2021, 6(2), 15; 10.3390/ijtpp6020015
Available online: https://doi.org/10.3390/ijtpp6020015

2. “Axial Compressor Mean-Line Analysis: Choking Modelling and Fully-Coupled Integration in Engine Performance Simulations”
Ioannis Kolias et al.
Int. J. Turbomach. Propuls. Power 2021, 6(1), 4; 10.3390/ijtpp6010004
Available online:  https://doi.org/10.3390/ijtpp6010004

3. “Near-Wall Flow in Turbomachinery Cascades—Results of a German Collaborative Project”
David Engelmann et al.
Int. J. Turbomach. Propuls. Power 2021, 6(2), 9; 10.3390/ijtpp6020009
Available online: https://doi.org/10.3390/ijtpp6020009

4. “Differential Throttling and Fluidic Thrust Vectoring in a Linear Aerospike”
Michele Ferlauto A. Dar et al.
Int. J. Turbomach. Propuls. Power 2021, 6(2), 8; 10.3390/ijtpp6020008
Available online: https://doi.org/10.3390/ijtpp6020008

5. “A Consistent and Implicit Rhie–Chow Interpolation for Drag Forces in Coupled Multiphase Solvers”
Lucian Hanimann et al.
Int. J. Turbomach. Propuls. Power 2021, 6(2), 7; 10.3390/ijtpp6020007
Available online: https://doi.org/10.3390/ijtpp6020007

6. “Modelling the Condensation Phenomena within the Radial Turbine of a Fuel Cell Turbocharger”
Tim Wittmann al.
Int. J. Turbomach. Propuls. Power 2021, 6(3), 23; 10.3390/ijtpp6030023
Available online: https://doi.org/10.3390/ijtpp6030023

7. “Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines”
Seyed Jalal Mohammadi Doulabi Fard et al.
Int. J. Turbomach. Propuls. Power 2021, 6(1), 2; 10.3390/ijtpp6010002
Available online: https://doi.org/10.3390/ijtpp6010002

8. “Influence of the Rotor-Driven Perturbation on the Stator-Exit Flow within a High-Pressure Gas Turbine Stage”
Paolo Gaetani et al.
Int. J. Turbomach. Propuls. Power 2021, 6(3), 28; 10.3390/ijtpp6030028
Available online: https://doi.org/10.3390/ijtpp6030028

9. “Influence of Spanwise and Streamwise Film Hole Spacing on Adiabatic Film Effectiveness for Effusion-Cooled Gas Turbine Blades”
Matthew Courtis et al.
Int. J. Turbomach. Propuls. Power 2021, 6(3), 37; 10.3390/ijtpp6030037
Available online: https://doi.org/10.3390/ijtpp6030037

10. “A Machine Learning Approach to Improve Turbulence Modelling from DNS Data Using Neural Networks”
Yuri Frey Marioni et al.
Int. J. Turbomach. Propuls. Power 2021, 6(2), 17; 10.3390/ijtpp6020017
Available online: https://doi.org/10.3390/ijtpp6020017