Large eddy simulation for improvement of performance estimation and turbulent flow analysis in a hydrodynamic torque converter. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Liu, Chunbao; Li, Jing; Bu, Weiyang; Ma, Wenxing; Shen, Guang; Yuan, Zhe
• Is Part Of: Engineering applications of computational fluid mechanics. Volume 12:Issue 1(2018)
• Page Start: 635
• Page End: 651
• Publication Date: 2018-01-01
• Subjects: hydrodynamic torque converter -- transient simulation -- LES -- SGS models; Computational fluid dynamics -- Periodicals; 620.10640285
• DOI: 10.1080/19942060.2018.1489896
• Languages: English
• ISSNs: 1994-2060

Abstract

ABSTRACT: Computational fluid dynamics (CFD) has been widely applied as an effective tool for optimizing products and reducing production cycles in many industrial fields; consequently, engineers are constantly pursuing higher accuracy in the performance predictions of CFD methods. In this paper, an analysis for the flow field of a hydrodynamic torque converter (TC) is conducted to evaluate CFD applications in detail. In the past, Reynolds-averaged Navier–Stokes (RANS) simulations have always played a dominant role in the numerical modeling of TCs because of their efficient calculation speed. However, most RANS models are unable to capture the complicated transient flows whose performance estimation errors are generally greater than 10%. Therefore, large eddy simulation (LES) with various sub-grid scale (SGS) models are applied in order to explore feasible methods for improving numerical accuracy and capturing the detailed transient flow phenomena. The effectiveness of the LES method is verified by comparing the numerical results with experimental data. Although the grid resolution is not fine enough due to the limitations of the high-performance computer (HPC) used, LES with dynamic kinetic energy transport (KET) models were still able to obtain an excellent description of both the near-wall flow and the main-stream flow via quantitative and qualitative analyses. The maximum error in the capacity factor ( CF ) is remarkably reduced to 4.4%. It is therefore beyond doubt that applying LES methods using coarser grid resolutions can still guarantee higher prediction accuracy through the reasonable selection of SGS models, which can effectively reduce the computing capacity requirements and contribute to the design process of TCs.