GPPS Data Sets Release Schedule
The first data set by ETH Zurich, TU Darmstadt, and Seoul National University were made available free of charge during or after the GPPS Chania20 to the registered attendees who requested it. It will become public domain end of 2022 / beginning of 2023
The second data set by ETH Zurich, TU Darmstadt, and Seoul National University will be made available free of charge after the GPPS Xi’an21 taking place on April 11-13, 2022 in China to the registered attendees who request it. The registered attendees will also receive access to the first set of data if required. The second set of data will become public domain soon, we will send notification to our mailing list once this happens.
The two data sets provided by Beihang University will be made available to the registered Xi’an21 attendees who request them. It will become public domain soon, we will send notification to our mailing list once this happens.
Before the data sets become public domain, only the GPPS conference attendees will receive access to it. ETH Zurich, TU Darmstadt, Seoul National University, and Beihang University as data providers may also distribute the data sets at their discretion before it becomes public domain. Data distribution to third-parties (not related to the GPPS events) will be handled through the data providers.
The data provided by ETH Zurich, TU Darmstadt, Seoul National University, and Beihang University remain the property of the named institutions. The data is intended to be used by the registered attendee (individual use) and must not be further distributed to a third party without the data providers’ written permission. Until the data is released publically, the data, including any analysis of the data, can only be included in a publication where the named registrant, who received the data, is an author.
Registered Chania20 attendees can request the data via an online form. Besides filling in an online form, a contract governing the data release must be digitally signed and uploaded.
ETH Zurich: D-Turbine – 1.5 stage high work axial turbine
1st Data Set (during GPPS Chania20)Geometry Data
ETH D-Case-Study 011. Turbine geometry: Stator 1, Rotor, Stator 2 and OGV 3D CAD models
2. Cavities configuration: Rotor upstream and downstream cavities geometry
3. Stator 1 Clocking information relative to Stator 2 (4 different positions)
Experimental Data
1. Operating parameters of the turbine (inlet, outlet, mass-flow, rotational speed etc.)
2. Derived flow parameters using 2-Sensor FRAP technology at four (4) measurement planes (Stator 1 inlet, Stator 1 outlet, Rotor outlet, Stator 2 outlet). Measured quantities cover an area of one (1) stator pitch and from 5.70% to 99.30% blade span. For each measurement plane the following are provided:
• Steady circumferentially mass-averaged profiles (1-D) of flow quantities
• Steady circumferentially area-averaged profiles(1-D) of flow quantities
• Unsteady Phase-Lock-Averaged flow quantities for 6 blade passing events derived at stationary frame of reference for entire measurement plane (2-D)
• Unsteady Phase-Lock-Averaged flow quantities for 6 blade passing events derived at rotor relative frame of reference for entire measurement plane (2-D) (only at rotor outlet)
3. Derived flow parameters using FRAP technology at Rotor outlet at three (3) additional Stator 1 clocking positions. Measured quantities cover an area of one stator pitch and from 5.70% to 99.30% blade span.
Additional Material
1. Explanatory material regarding measurement and reference planes, angle convention and more.
2. Publications (Journals and PhD Thesis) related to the measurements provided.
2nd Data Set (after GPPS Technical Conference 2021)
Geometry DataETH D-Case-Study 02
1. Turbine geometry: Stator 1, Rotor, Stator 2 and OGV 3D CAD models
2. Cavities configuration: Rotor upstream and downstream cavities geometry modifications due to cooling injection
3. Position of trip-wires at Stator 1 to increase turbulence levels downstream
Experimental Data
1. Operating parameters of the turbine (inlet, outlet, mass-flow, injection rate, rotational speed etc.)
2. Derived flow parameters using 4-sensor FRAP (FRAP-4S) technology at three (3) measurement planes (Stator 1 outlet, Rotor outlet, Stator 2 outlet). Measured quantities cover an area of one (1) stator pitch and from 5.70% to 99.30% blade span. For each measurement plane the following are provided:
• Steady circumferentially mass-averaged profiles (1-D) of flow quantities
• Steady circumferentially area-averaged profiles(1-D) of flow quantities
• Unsteady Phase-Lock-Averaged flow quantities for 6 blade passing events derived at stationary frame of reference for entire measurement plane (2-D)
• Unsteady Phase-Lock-Averaged flow quantities for 6 blade passing events derived at rotor relative frame of reference for entire measurement plane (2-D) (only at rotor outlet)
• Full unsteady flow quantities for 85 rotor revolutions (4590 blade passing events) derived at stationary frame of reference for entire measurement plane (2-D)
4. The data described in point 2 will be provided for FRAP-4S with 3 different diameters, namely 3 mm, 4 mm and 5 mm FRAP tip diameter.
TU Darmstadt: TUDa-GLR-OpenStage
1st Data Set (after GPPS Chania20)
Transonic compressor stage geometry/ information• TU Darmstadt Rotor 1 with StatorOPT, OGV, radial diffusor
• Hub & shroud contour
• Operating conditions:
• Design speed:
• 20000rpm, N100 (transonic)
• running tip clearance 0.85% chord
• Part speed
• N65 (subsonic)
Measurement data, exemplary
• Steady state
• inlet conditions (i.e. pt, tt, ps, …)
• Performance data
• 1D & 2D pressure/ temperature traverses at stage exit
2nd Data Set (after GPPS Technical Conference 2021)
Measurement data, exemplary• Steady State:
– 5-hole probe at rotor exit (i.e. pt, ps, alpha, Ma)
• Dynamic:
– unsteady wall pressure at blade tip (steady state & transient operating conditions, e.g. stall inception)
– unsteady pressure probe at rotor exit
Seoul National University: Flat Plate Transitional Boundary Layer Data
1st Data Set
For smooth and rough flat plates under zero pressure gradient• Surface statistics (e.g., Ra, etc.)
• Unsteady hotwire velocity (in streamwise and normal directions) data at various streamwise locations
Data facilitates the determination of integral parameters, intermittency, transition onset/end loci, time-resolved velocity, Reynolds stresses, and more to understand flow physics and help validate various analytical/numerical models
