Effect of hole configurations on film cooling from cylindrical inclined holes for the application to gas turbine blades

Film cooling is one of the cooling systems investigated for the application to gas turbine blades. Gas turbines use film cooling in addition to turbulated internal cooling to protect the blades outer surface from hot gases. The present study concentrates on the experimental and numerical investigation of film cooling performance for a row of cylindrical holes in a modern turbine blade. The adiabatic film effectiveness and the heat transfer coefficient are determined experimentally on a flat plate downstream of a row of inclined different geometries hole exit by using a single test transient IR thermography technique. The focus of this investigation is to investigate advanced cooling hole geometries on film cooling heat transfer and cooling effectiveness over flat and turbine airfoil surfaces. Four test designs, crescent and converging slot, trench and cratered hole exits, are tested. Variations of these configurations are tested under two different test rigs. Results show that both the crescent and slot exits reduce the jet momentum at exit and also provide significantly higher film effectiveness with some increases in heat transfer coefficients. The trench where in the jets come in and spread evenly into a slot before exiting. An optimum trench depth exists at 0.75D as shallower and deeper trenches show worse performance. The cratered holes increase film effectiveness over the baseline case by about 50%. However, they do not provide significant lateral spreading as seen for trenched holes. Meanwhile, film cooling predictions are used to understand the mechanisms of the jets that exit these trenched holes and crater holes. The present work employs RSM (Reynolds stress transport model) for simulation of turbulent flows in film cooling and the simulation was run using FLUENT computer code. Comparisons are made with experimental data for the film effectiveness distributions. Results show that the film cooling jet exiting the trenched hole is more two-dimensional than the typical cylindrical holes and crater holes. Detailed flow structure visualization shows that the trench design counteracts the detrimental vorticity of the round hole flow, allowing it to remain attached to the surface

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle

Deep learning achieves state-of-the-art results in many tasks in computer vision and natural language processing. However, recent works have shown that deep networks can be vulnerable to adversarial perturbations, which raised a serious robustness issue of deep networks. Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks. A major drawback of existing adversarial training algorithms is the computational overhead of the generation of adversarial examples, typically far greater than that of the network training. This leads to the unbearable overall computational cost of adversarial training. In this paper, we show that adversarial training can be cast as a discrete time differential game. Through analyzing the Pontryagin's Maximal Principle (PMP) of the problem, we observe that the adversary update is only coupled with the parameters of the first layer of the network. This inspires us to restrict most of the forward and back propagation within the first layer of the network during adversary updates. This effectively reduces the total number of full forward and backward propagation to only one for each group of adversary updates. Therefore, we refer to this algorithm YOPO (You Only Propagate Once). Numerical experiments demonstrate that YOPO can achieve comparable defense accuracy with approximately 1/5 ~ 1/4 GPU time of the projected gradient descent (PGD) algorithm. Our codes are available at https://https://github.com/a1600012888/YOPO-You-Only-Propagate-Once.Comment: Accepted as a conference paper at NeurIPS 201