Workshop Organizers
Our organizers have wide background: academia, industries and software companies.
Dr. Xiao He
(Shanghai Jiaotong University, China)
Dr. Dajan Mimic
(Leibniz Universität Hannover, Germany)
Prof. Mehdi Vahdati
(TU Hamburg, Germany)
Prof. Dingxi Wang
Prof. Ruiyu Li
Prof. Liming Gao
(Northwestern Polytechnical University, China)
GPPS 5th Turbomachinery CFD Workshop
Building on the achievements of our previous workshops, we would like to announce the CFD Workshop in Shanghai 2025. The workshop will center on two key cases: the Northwestern Polytechnical University cascade cases, and the Institute of Turbomachinery and Fluid Dynamics (University of Hannover) test case. The NWPU open test case data for these studies is available for download on the ScienceDB platform, while the Institute of Turbomachinery and Fluid Dynamics test case can be accessed via the University of Hannover github platform (download links can be found below). Data submission templates can be downloaded below.
We invite participants to join us for in-depth discussions and collaborative efforts, contributing to the continued progress of computational fluid dynamics.
How to Participate
Participants are encouraged to attend the workshop to present their findings in a 10-minute oral presentation, focusing on methodology, challenges, and insights. No formal paper submission is required; a concise summary of results suffices. The workshop will feature a collective review of all contributions, followed by structured discussions to dissect trends, anomalies, and best practices.
Who Can Participate
Attendees of the GPPS Shanghai25 Technical Conference are eligible to participate in the GPPS CFD Workshop25, which will be held concurrently with the main conference.
The CFD Workshop is complimentary for all Shanghai25 registrants, including both day-pass and full-conference ticket holders. To view conference fees for the technical conference, please click here.
Motivation
Validating the reliability of numerical methods using experimental data is crucial for performance evaluation in turbomachinery design. Nevertheless, discrepancies between experimental and numerical results frequently occur due to uncertainties inherent in both domains, thereby reducing researchers’ confidence in numerical predictions. These discrepancies often stem from inherent differences between the experimental and numerical environments, not solely from turbulence models or numerical schemes.
To ensure that the numerical models used for validation closely match the experimental conditions, this study will release a complete set of standard compressor blade row models, along with a full set of typical high-load, large-angle bend compressor blade row test data, based on standard compressor blade row experiments.
NPU-MGS1 and NPU-28 Compressor Cascade Models
To address these validation challenges and ensure numerical models closely reflect experimental conditions, this study provides a comprehensive dataset based on standard compressor blade row experiments. Specifically, two standard high-load compressor cascade models, NPU-MGS1 and NPU-28, with diffusion factors of 0.41 and 0.5 respectively, are presented. The dataset encompasses a range of inlet Mach numbers from 0.4 to 0.79, detailed inlet boundary conditions, flow quality parameters—including inflow uniformity, periodicity, and two-dimensionality—and extensive aerodynamic test data. This includes surface isentropic Mach numbers, aerodynamic losses measured in wake regions, and outlet flow angles, thereby providing a robust foundation for accurate numerical method validation.
How to participate:
Researchers and practitioners are welcome to engage with the DLR and NPU-28 test cases by conducting numerical analyses and submitting predictions of key parameters (e.g., surface isentropic Mach numbers, wake losses, outlet flow angles). Submissions must adhere to the guidelines outlined on the ScienceDB platform, which includes detailed geometry specifications, boundary conditions, and validation metrics. Data access and submission guidelines are available on the ScienceDB platform (NPU-MGS1 and NPU-28).
Objectives：
1.Enhance the understanding of RANS capabilities in predicting the inner flow field with high-adverse pressure gradient.
2.Discuss the reasons for discrepancies between experimental and numerical results in compressor blade rows with high-adverse gradient flow, by comparing the numerical results of 2D, 3D, and axial velocity density ratio -considered simulations.
3.Promote education of the next generation turbomachinery CFD engineers.
4.Discuss the applicability of different turbulence models in high-subsonic strong reverse flow fields.
TFD-4AC open test case: 4½-stage high-speed axial compressor
Motivation
RANS simulation is the industrial workhorse for predicting turbomachinery flows. However, due to the errors and uncertainties involved in the experiments and CFD models, users can occasionally find inconsistencies and deficiencies in RANS results, which degrades the trust of users in RANS turbomachinery solvers. This workshop aims to improve trust of users in RANS turbomachinery solvers by conducting a validation and verification (V&V) study on a highly challenging test case: the TFD-4AC 4½-stage high-speed axial compressor open test case.
Objectives
- Benchmarking of existing in-house and commercial turbomachinery RANS solvers.
- Improved understanding of RANS prediction capabilities and shortcomings for high-speed multi-stage compressors.
- Develop a roadmap/best-practice guide for future RANS developments in realistic turbomachinery applications.
- Promote the education of the next generation of turbomachinery CFD engineers
Submitting Your Data
Submission Deadline: Please submit your results by 28 August 2025. Data submission templates can be downloaded above.
Note: Submissions received after the deadline but before the workshop may not be included in the workshop summary presentation. However, they will be considered for inclusion in the final report.
Please send files under 15MB via email, and larger files (over 15MB) using WeTransfer or a suitable local alternative, to the contacts listed below:
NWPU Data Sets: Prof. Ruiyu Li (Northwestern Polytechnical University)
TFD Data Sets: Dr. Dajan Mimic (Leibniz Universität Hannover)
