Assessing the Effects of Computational Model Parameters on Aerodynamic Noise Characteristics of a Heavy-Duty Diesel Engine Turbocharger Compressor at Full Operating Conditions

Abstract

<div class="section abstract"><div class="htmlview paragraph">In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in turbocharger compressors of heavy-duty diesel engines has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence models (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2° and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under various conditions were investigated. The results showed that for the compressor performance, the turbulence models and time-step parameters selection were within 3% error of the simulated and experimental values for pressure ratio and efficiency. Under high-efficiency conditions, in a fixed time step, the use of SST could achieve high prediction accuracy in pressure ratio and efficiency. For aerodynamic noise prediction, at both the blade passing frequency and its first order harmonic frequency could obtain the significant peak values of power spectrum density (PSD) for four model parameters. In addition, the turbulence models with 4° time step showed lower PSDs at high frequency (more than 15000 Hz) as compared with the PSDs of 2° time step in volute region under near-surge condition. Therefore, based on the trade-off relationship between computational accuracy and time cost, the SST model combined with the 4° time step was the best choice for the calculation of compressor performance and aerodynamic noise prediction at various conditions.</div></div>