In-Hole Characteristic Interface and Film Cooling Interface Model

Zhang, Zhen; Li, Hui; Chen, Ziyu; Yuan, Xin

doi:10.1115/1.4050757

Cited by 3 publications

(1 citation statement)

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“…In the experiment, the cooling jet undergoes intense turbulent mixing with the mainstream, and the cooling effectiveness reduces quickly in the flow direction. For narrow expansion holes such as the 30–7–7 hole [ 18 ], comparison by the authors [ 21 ] shows that the realizable k - ε model predicts well the laterally averaged cooling effectiveness. Table 3 shows the area averaged cooling effectiveness of the results shown in Figure 5 .…”

Section: Optimization Processmentioning

confidence: 99%

Optimization of the Double-Expansion Film-Cooling Hole Using CFD

Zhang

Chen

et al. 2023

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Film cooling is a major cooling technique used in modern gas turbines and air engines. The geometry of film-cooling holes is the fundamental aspect affecting the cooling performance. In this paper, a new cooling configuration called the double-expansion film-cooling hole has been put forward, which yields better performance than the widely used shaped holes and is easy to manufacture. The double-expansion holes at inclination angles of α=30∘, 45∘, and 60∘ are optimized using the genetic algorithm and the Kriging surrogate model, which is trained by CFD data randomly sampled using the Latin hypercube method. The numerically optimized double-expansion holes at different inclination angles were experimentally evaluated and compared with the optimized single-expansion laid-back fan-shaped holes, and the optimized double-expansion hole at α=30∘ was manually modified based on experiment results. Compared with the optimal single-expansion holes, the area-averaged cooling effectiveness of the double-expansion holes was increased by 34.5% at α=30∘, by 27.8% at α=45∘, and basically the same at α=60∘, showing the benefit of the double-expansion concept. The loss mechanism of film cooling was also analyzed in the perspective of the entropy generation rate, showing the optimal double-expansion holes have 21% less loss compared to a baseline narrow single-expansion hole. It was also found that CFD sometimes predicts a different trend from the experiment in optimization, and the experimental validation is necessary.

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Section: Optimization Processmentioning

confidence: 99%

Optimization of the Double-Expansion Film-Cooling Hole Using CFD

Zhang

Chen

et al. 2023

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Inversion learning of turbulent thermal diffusion for film cooling

et al. 2022

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Film cooling is a typical three-dimensional fluid phenomenon, where the coolant with lower temperature is ejected from discrete holes to protect metal walls from being burnt by the hot mainstream. It is a great challenge for Reynolds-averaged Navier–Stokes (RANS) methods to accurately predict the coolant coverage on the wall because the turbulent thermal diffusion tends to be under-predicted due to inherent assumptions behind RANS models. In this paper, a framework of integrated field inversion and machine learning is built to enhance RANS prediction of turbulent thermal diffusion. A neural network (NN) is trained in this framework to predict the spatially varying turbulent Prandtl number ([Formula: see text]) and to improve the prediction of RANS models. The temperature distribution obtained from the large eddy simulation is used as the learning target, and the discrete adjoint method is used as the inverse model that helps calculate derivatives of mean square error of the temperature distribution to NN parameters. The training process of NN shows good convergence properties. The results show that the obtained NN effectively increases the insufficient turbulent thermal diffusion by predicting much lower [Formula: see text] than the commonly used value of 0.9. The NN-enhanced RANS provides significant improvements on predicting experimental temperature distributions compared with general RANS models not only on the training data but also on the unseen testing data. In addition, the obtained NN can be implemented into general-purpose software with minimal effort and no numerical stability problem.

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