Our organizers have wide background: academia, industries and software companies.
Mehdi Vahdati, Xiao He (Imperial College London)
Shenren Xu, Dongming Cao,
(Northwestern Polytechnical University)
(Cadence Design Systems)
Guangfeng An, Xianjun Yu
(Technical University Darmstadt)
GPPS 3rd Turbomachinery CFD Workshop
The workshop will take place as part of the GPPS Hong Kong23 conference on October 16, 2023.The workshop will take place in a hybrid format (the conference will take place in person).
Built upon the experience from our previous workshops, we would like to launch our 3rd workshop. Along side workshop presentations we will be enjoying keynote talks from prominent speakers.
Three test cases are involved: the TUDa-GLR-OpenStage Transonic Axial Compressor, the BUAA Low Speed Large Scale Axial Compressor and the ETH Zurich 1.5-stage Turbine. The datasets of the two compressors are freely available to the public and can be downloaded from the workshop website. Accessing the datasets of the turbine requires attendance of the conference in 2021 or 2022 or registration of the upcoming conference or workshop.
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 inconsistency and deficiency of RANS results, which degrades users’trust in RANS turbomachinery solvers. This workshop aims to improve users’ trust in RANS turbomachinery solvers by conducting a validation and verification (V&V) study on three cases: the TUDA-GLR-OpenStage transonic axial compressor, the BUAA low speed large scale axial compressor and the ETH Zurich 1.5-stage turbine.
1. Troubleshooting and version control of numerical models in existing in-house and commercial turbomachinery RANS solvers.
2. Enhance understanding of RANS prediction capability for subsonic and transonic axial compressors
3. Develop a best practice guide of RANS solvers for subsonic and transonic axial compressors.
4. Promote education of the next generation turbomachinery CFD engineers.
How to Participate
Participants are welcome to analyze the three cases, submit their prediction results, attend the workshop to present their results, listen and learn from the discussions. In the workshop, the organizers will present a summary of all submitted data. After that, each participant submitting the prediction results will have a 10-min rapid talk to present their results. No paper submission is required.
1. The TUDa compressor and the BUAA compressor are freely available to the public. Workshop participants can download the data via our online request forms.
Click here to download the required data sets, official grids and data submission templates:
Note: There are two BUAA compressors: one is called Stage-A and the other Stage-B. In the workshop, we will look at Stage-B only. Please submit your Stage-B results. Nevertheless, you can present Stage-A results at the workshop.
2. To download the ETH Zurich turbine dataset, one must either have attended the GPPS conference in 2021 or 2022 or register for this year’s workshop and/or conference. If you are eligible, please go to the dataset request page Request Data (gpps.global) and fill the GPPS Data Set Request Form. Note you need to download, sign and upload the legal agreement for the ETH turbine.
3. Official grids and data submission templates are provided for each configuration. Participants are encouraged to perform analysis using the official grids. Nevertheless, they can also use their own grids.
4. The deadline of data submission is 30th September 2023. The data for the TUDa compressor should be submitted to Dongming Cao (email@example.com), and data for the BUAA compressor should be submitted to Jiazi Zhao (firstname.lastname@example.org) and data for the ETH Zurich turbine should be submitted to Xuanlong Da (email@example.com).
5. All workshop attendees have to register for the CFD workshop to attend and/or present their results and/or have their submitted data processed by the organizers.