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Blade tip leakage is responsible for reducing the aerodynamic performance and increasing the thermal load. In order to obtain better cooling efficiency, the thermodynamic performance of different film hole arrangements and blowing ratios have been compared and analyzed in a cavity tip structure. Reynolds-averaged Navier–Stokes equation simulations at five blowing ratios from 0.25 to 2.0 have been carried out for three stream-wise and four pitch-wise film hole arrangements. Based on the reference flow field of the cavity without any coolant, the mechanism of the cooling air influence is analyzed by investigating the flow structure and the vortex development in the tip region. It is found that in the stream-wise arrangements and pitch-wise arrangements, the scheme SW1, in which the film holes are arranged along the suction surface, and the scheme PW2, in which the air film holes are arranged at 0.2 times the axial position, provide the maximum average film cooling efficiency of 0.235 and 0.282, respectively. However, the effective cooling area of SW1 is greater than that of PW2, which are 39.2% and 41.3%, respectively. At the lower blowing ratios, the cooling efficiency increases with the increase in the blowing ratio; however, at the higher blowing ratios, the cooling effect is affected by the clearance size and cavity depth when the coolant impinges on the casing wall. [ABSTRACT FROM AUTHOR]

Tomographic reconstruction of three-dimensional (3D) tracer particle distributions through light field particle image velocimetry (LF-PIV) faces challenges in low reconstruction resolution owing to the elongation effect and extensive computational cost incurred by the iterative process. To resolve these challenges, this study proposes a deep neural network-based volumetric reconstruction approach to alleviate the reconstruction elongation and enhance the reconstruction efficiency. A tailored deep learning model (namely, LF-DNN) incorporating residual neural network architecture and a novel hybrid loss function is established to reconstruct the particle distributions through LF images. The parallax information of the flow field decoded from the raw LF data is leveraged as the input features of the network model. Comparative studies between the proposed method and the traditional tomographic reconstruction algorithms (multiplicative algebraic reconstruction technique, MART and pre-recognition MART, PR-MART) are performed through synthetic datasets. Experiments on a cylinder wake flow are further conducted to validate the performance of the proposed LF-DNN. The results indicate that the LF-DNN outperforms MART and PR-MART in terms of the reconstruction quality, mitigation of elongation effect, and noise resilience. The LF-DNN also improves the reconstruction efficiency which is 9.6 and 7.1 times higher than the MART and PR-MART, respectively. The relative error of the cylinder wake flow achieved by the LF-DNN is 2% lower than the MART. It suggests that the LF-DNN can facilitate accurate volumetric particle reconstruction and hence the three-dimensional flow measurement by single camera-based LF-PIV. [ABSTRACT FROM AUTHOR]

The increase in power density of hydrodynamic torque converters (HTCs) leads to a sharp rise in temperature within flow channels, affecting the reliability. In order to accurately predict the thermal effect and temperature distribution characteristics of the HTC internal viscosity oil, a multi-physics computational fluid dynamics (CFD) model is proposed. A specialized test bench was established, and the macro and internal flow temperature data were obtained. HTCs with different working conditions and wheel sets were studied. The results indicate that CFD model considering energy equation can accurately predict the overall hydrodynamic performance and the flow field temperature characteristics under different rotating conditions. The prediction error of the overall temperature rise is within 4.92%, and the flow field temperature prediction error of the stator is under 14.3%. The hydraulic characteristics is improved by 6.02%. The analysis of internal flow and energy exchange characteristics indicates the thermal effects and temperature distribution mechanisms caused by energy loss in the flow field within the HTC. The study provides an effective computational model for the prediction and control of the heat generation of the HTC and enhances the depth of research on the flow mechanism of inhomogeneous flow fields caused by thermal effects. [ABSTRACT FROM AUTHOR]