Abstract

ABSTRACTComputational 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.

Highlights

  • An important component of automatic transmissions, the hydrodynamic torque converter (TC) is comprised of three elements: (1) the pump, which directly connects to the engine and transfers the power of the input shaft to the automatic transmission fluid (ATF); (2) the turbine, which rotates under the impact action of the high-speed ATF and transfers the mechanical energy to the output shaft; and (3) the stator, which is stationary and redirects the ATF into the pump to generate the circulating flow in the blade cascade (Figure 1)

  • These results demonstrate that the use of the large eddy simulation (LES) method produces a significant improvement in performance estimation

  • The comparison shows that the Dynamic Smagorinsky–Lilly (DSL), Wall-adapting local eddy viscosity (WALE), and kinetic energy transport (KET) models are most effective at capturing the vortex structures, whereas the description generated by the SL model is quite rudimentary

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Summary

Introduction

An important component of automatic transmissions, the hydrodynamic torque converter (TC) is comprised of three elements: (1) the pump, which directly connects to the engine and transfers the power of the input shaft to the automatic transmission fluid (ATF); (2) the turbine, which rotates under the impact action of the high-speed ATF and transfers the mechanical energy to the output shaft; and (3) the stator, which is stationary and redirects the ATF into the pump to generate the circulating flow in the blade cascade (Figure 1). Lei, Wang, Liu, and Li (2012) analyzed the flow rate, pressure, and flow loss by establishing a full three-dimensional passage model, providing a direction for TC design optimization Most of these Reynolds-averaged Navier–Stokes (RANS) studies suffer from a lack of clarity in the description of the flow structures. Tasaka, Oshima, Fujimoto, and Kishi (2017) evaluated the feasibility of LES unsteady calculation using coincident results in the oil flow pattern at the correct position. These two studies only employed Smagorinsky–Lilly (SL) models of the LES method, and failed to show significant differences compared to the SGS models. The feasibility of using different CFD methods as industrial diagnostic tools is evaluated according to the presence of obvious improvements in performance predictions

Governing equations
SGS models
Test setup
Three-dimensional computational domain and mesh layout
Computational settings
Evaluation of temporal variations
Prediction estimations of the LES method and comparison with the RANS method
Flow field description
Flow mechanism analysis
Near-wall treatment
Conclusion
Full Text
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