Date of Award
6-14-2023
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Mechanical, Automotive, and Materials Engineering
Keywords
Fan stall;incompressible;RANS;steady;Turbomachinery
Supervisor
Jeff Defoe
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
This thesis applies a modified Spalart-Allmaras (SA) turbulence model, the helicity-corrected Spalart-Allmaras (HCSA) model, in incompressible steady Reynolds-averaged Navier-Stokes (RANS) computations to reduce the numerical simulation run time for fan stall inception prediction. Computations are carried out using the OpenFOAM package. This manuscript-style thesis contains two main chapters, each stemming from conference papers. First, the HCSA turbulence model is applied to a low-speed axial fan, known as the boundary layer ingestion (BLI) fan, for fan stall behavior simulation. Both RANS and unsteady RANS (URANS) with the HCSA model and RANS with the original SA model are performed, with all results compared to experimental data from the literature. The HCSA model is able to predict the stalling flow coefficient to within 0.006 of the experimentally measured value in RANS (stall margin error at 0.012), compared with a stall margin error of 0.002 in URANS. The original SA model RANS predicts stall inception very early (flow coefficient error of -0.05). The HCSA steady RANS computation is thus shown to be a good option to find the stall point at reduced computational cost. The manner in which the HCSA model steady computations identify the correct stall inception mechanism is also revealed by comparing to the URANS stall inception process. The second part of the work presents results from the original SA, HCSA and Menter shear stress transport (SST) turbulence model for a linear cascade with NACA 65-1810 blading, focusing on how and why the HCSA model is able to predict corner separations accurately. The key takeaway is that the addition to the turbulent viscosity production associated with helicity keeps the flow attached in a more realistic manner than the other turbulence models are able to achieve.
Recommended Citation
YU, ZHIFAN, "The Importance of Accurately Predicting Corner Separations in Fan Stall Point Identification with Steady RANS: Computations with The Helicity-Corrected Spalart-Allmaras Turbulence Model" (2023). Electronic Theses and Dissertations. 9317.
https://scholar.uwindsor.ca/etd/9317