Improved Body Force Model for Estimating Off-Design Axial Compressor Performance

Abstract

Based on the COMSOL software, body forces substituted into the Reynolds-averaged Navier–Stokes (RANS) equations as the source terms instead of the actual blade rows were improved to better predict the compressor performance. Improvements in parallel body force modeling were implemented, central to which were the local flow quantities. This ensured accurate and reliable off-design performance prediction. The parallel force magnitude mainly depended on the meridional entropy gradient extracted from three-dimensional (3D) steady single-passage RANS solutions. The COMSOL software could easily and accurately translate the pitchwise-averaged entropy into the grid points of the body force domain. A NASA Rotor 37 was used to quantify the improved body force model to represent the compressor. Compared with the previous model, the improved body force model was more efficient for the numerical calculations, and it agreed well with the experimental data and computational fluid dynamics (CFD) results. The results indicate that the improved body force model could quickly and efficiently capture the flow field through a turbomachinery blade row.

A Three-Dimensional Body Force Modeling of Fans in Windmilling Condition and Its Application

“…Due to the circumferential uniformity of the grid used to solve the flow in the fan duct, the blockage effect of the presence of blades on the flow cannot be considered. Research [30][31][32] has shown that disregarding the blockage effect of blades can have a certain impact on the accuracy of calculation results, especially for near-chocking conditions. Therefore, this article introduces the blockage coefficient b was introduced for considering the circumferential thickness of the blade during calculation, which can be calculated as follows: The relationship between the rotational speed of the fan spool and resistance torque under different operating conditions was obtained using numerical simulation results, as shown in Figure 8.…”

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

“…Several studies have focused on the characteristics of various axial compressors and the elements influencing their performance (Huang et al, 2022;Babin et al, 2023). For instance, Blinov and Zubkov (2020) investigated the effect of blade row defects on the performance of a compressor.…”