Chunyu Zhang

Building upon our previous aerodynamic characterizations of skewed jets, this study extends the investigation to systematically evaluate their thermal performance. Turbulent air jets are produced by unilaterally supplying coolant and forcing it through a series of concave perforated blockages having varying relative inner diameters (D_in/D_j = 3.0, 4.0 and 5.0) or relative thicknesses (t/D_j = 0.5, 2.0, 4.0, 6.0 and 8.0), with the jet diameter and Reynolds number fixed at D_j = 21 mm and Re_j = 20,000, respectively. The results demonstrate that the skewed jets exhibit pronounced asymmetric velocity profiles in both the x-z and y-z planes. Unlike the Gaussian distributions characteristic of conventional axisymmetric jets, these profiles manifest as distinctly skewed or saddle-shaped topologies. This topological distortion is exacerbated by reducing either D_in/D_j or t/D_j, albeit through fundamentally different mechanisms: the former only leads to jet deflection from the geometric axis, with the deflection angle increasing non-linearly from α = 4°, 5° to 12°; whilst the latter induces asymmetric internal flow development and exit momentum redistribution. The thermal performance of these jets on an iso-flux target flat plate, characterized by Nusselt number distributions at different jet-to-target spacings (H/D_j = 0 to 8.0), is shown to significantly differ from conventional axisymmetric jets.

This work presents an approach for sensitivity analysis of turbine cooling blade with surface thickness uncertainties, combining mesh deformation method, neural network model and multidisciplinary analysis. Normally, for even tiny shape changes, conventional geometry-based method failed easily during the auto-processing analysis. Therefore, mesh deformation method was utilized to capture the tiny size changes in the multidisciplinary analysis for both the fluid and the structure meshes. The neural network model is constructed by design of experiments to reduce the computational cost. Sensitivity analysis of the multidisciplinary system of blade is performed by numerical difference algorithm with the neural network model. Results showed that the proposed method was effective and practical in engineering.

This work presents an approach for sensitivity analysis of turbine cooling blade with surface thickness uncertainties, combining mesh deformation method, neural network model and multidisciplinary analysis. Normally, for even tiny shape changes, conventional geometry-based method failed easily during the auto-processing analysis. Therefore, mesh deformation method was utilized to capture the tiny size changes in the multidisciplinary analysis for both the fluid and the structure meshes. The neural network model is constructed by design of experiments to reduce the computational cost. Sensitivity analysis of the multidisciplinary system of blade is performed by numerical difference algorithm with the neural network model. Results showed that the proposed method was effective and practical in engineering.

This study presents impingement cooling from a flat plate by multiple asymmetric jets. Such jets are discharged through blunt-edge inline orifice holes with a thickness-to-diameter ratio of t/Dj = 0.5 and a jet-to-jet spacing of T/Dj = 4.0, at the Reynolds number of 20,000. First, fluidic features are established both in free exit and with impingement, at varying short target spacing (e.g., H/Dj ≤ 4.0). Second, thermal characteristics of the jet impingement are elucidated. Results demonstrate that, due to a skewed incidence of the coolant stream upstream of concave orifice holes, the resulting multiple orifice jets are asymmetric and skewed relative to the orifice axis. These results mimic multiple fluidically inclined jets. However, asymmetric entrainment that takes place causes faster mixing with the surrounding fluid at rest as well as faster decay of momentum. This shows more effective cooling from a flat plate for the relatively short H/Dj range than conventional symmetric orifice and nozzle jets.