Low-speed model testing has advantages such as great accuracy and low cost and risk, so it is widely used in the design procedure of the high pressure compressor (HPC) exit stage. The low-speed model testing project is conducted in Nanjing University of Aeronautics and Astronautics (NUAA) to represent aerodynamic load and flow field structure of the seventh stage of a high-performance ten-stage high-pressure compressor. This paper outlines the design work of the low speed four-stage axial compressor, the third stage of which is the testing stage. The first two stages and the last stage provide the compressor with entrance and exit conditions, respectively. The high-to-low speed transformation process involves both geometric and aerodynamic considerations. Accurate similarities demand the same Mach number and Reynolds number, which will not be maintained due to motor power/size and its low-speed feature. Chinese sentences and greetings. Compromises of constraints are obvious. Modeling principles are presented in high-to-low speed transformation. Design work was carried out based on these principles. Four main procedures were conducted successively in the general design, including establishment of low-speed modeling target, global parameter design of modeling stage, throughflow aerodynamic design, and blading design. In global parameter design procedure, rotational speed, shroud diameter, hub-tip ratio, midspan chord, and axial spacing between stages were determined by geometrical modeling principles. Attached is a geomturbo file for a stator blade. The blade sections (untwisted) need to be stacked along the vector radius through the center of the throats, i.e center of channel in numeca autoblade. During the throughflow design process, radial distributions of aerodynamic parameters such as D-factor, pressure-rise coefficient, loss coefficients, stage reaction, and other parameters were obtained by determined aerodynamic modeling principles. Finally, rotor and stator blade profiles of the low speed research compressor (LSRC) at seven span locations were adjusted to make sure that blade surface pressure coefficients agree well with that of the HPC. Three-dimensional flow calculations were performed on the low-speed four-stage axial compressor, and the resultant flow field structures agree well with that of the HPC. It is worth noting that a large separation zone appears in both suction surfaces of LSRC and HPC. How to diminish it through 3D blading design in the LSRC test rig is our further work. Copyright in the material you requested is held by the American Society of Mechanical Engineers (unless otherwise noted). This email ability is provided as a courtesy, and by using it you agree that you are requesting the material solely for personal, non-commercial use, and that it is subject to the American Society of Mechanical Engineers' Terms of Use. The information provided in order to email this topic will not be used to send unsolicited email, nor will it be furnished to third parties. Please refer to the American Society of Mechanical Engineers' Privacy Policy for further information. MULTIDISCPLINARY DESIGN OPTIMIZATION Plug-in all NUMECA and external solvers and benefit from its state-of-the-art technology for multi-disciplinary optimization. PARAMETRIC MODELING Define a large design space thanks to a range of Parametric Modeling capabilities such as Autoblade™ or for turbomachinery applications, our brand new OMNIS™ Morphing, built-in Rhino/Grashopper plug-in and external modelers. GENERIC GEOMETRY PARAMETRIC MODELER Thanks to our partnership with, get access to the and boast a number of cutting edge features in terms of Design of Experiments (DoE), model reduction, optimization algorithms and post processing. UNCERTAINTY QUANTIFICATION Take into account and quantify the influence of variability on the simulation prediction to ensure your optimization is in real life conditions. ROBUST DESIGN OPTIMIZATION Combining FINE™/Design3D with Uncertainty Quantification further allows you to perform. It maximizes the performance and minimizes the performance variability to ensure your optimum really is optimum. Key features • Multidisciplinary optimization platform • Plug-in all NUMECA and external solvers and benefit from its state-of-the-art technology for multi-disciplinary optimization. • Addressing various sectors: Turbo, Marine, Aero, Combustion. • New optimization kernel Minamo • Advanced Optimization Algorithms • Comprehensive data-mining and analysis tools, such as Self-Organizing Maps, ANOVA, and others (see list on the website) • Large choice of parametric modelers such as Autoblade™,, Rhino, OMNIS™/Morphing, external by python scripts. • Unique Uncertainty Quantification for operational, geometrical and manufacturing variability • Unique Robust Design Optimization.
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