The blockage is one of the important factors affecting the icing of airfoils in wind tunnel tests. In this paper, numerical simulations are conducted to study the effect of blockage on the icing of different airfoils. By reducing the height of testing wind tunnels, the blockage is increased, and the changes in the height and angle of the ice horn are numerically investigated. The simulation results indicate that as the blockage increases, the flow velocity above the stagnation point of the airfoil increases, leading to larger pressure coefficients distribution and stronger heat transfer capacity. As a result, the position of icing moves forward, and the angle of the upper ice horn becomes smaller. In addition, the increased flow velocity facilitates the collection of water droplets in the area, which improves the icing and increases the height of the upper ice horn. It is also found that the blockage increases the angle of attack of the airfoil, moving the stagnation point backward and decreasing the angle of the upper ice horn. When the blockage is above 15%, the joint influence of the opening angle and height of the upper ice horn significantly reduces the projection height of the upper ice horn in the direction of the incoming flow, leading to unacceptable criticality of the ice shape.
In this paper, an adaptive mesh refinement technique is presented for simulation of compressible flows, which can effectively refine the mesh in the regions with shock waves and vortices. The present approach uses the total energy per unit volume as an indicator to capture the shock waves and vortical structures. In the approach, an h-refinement strategy is adopted. To save the computational effort, the flow variables on the new mesh are obtained from the previous step by interpolation, which ensures that the problem is always solved on the refined mesh. Both inviscid and viscous compressible flows are considered in this work. Their governing equations are, respectively, Euler equations and Navier–Stokes equations associated with the implementation of the Spalart–Allmaras turbulence model. The cell-centered finite volume method and Jameson scheme are chosen to carry out spatial discretization, and the five-stage Runge–Kutta scheme is applied to discretize the temporal derivative. The present approach is applied to simulate three test problems for its validation. Numerical results show that it can effectively capture the shock waves and vortices with improvement in solution accuracy.
Icing scaling is of great importance for ice wind tunnel experiment and airworthiness certification. In this study, MAEFN (Multi-Autoencoder Fusion Network) method is developed to achieve fast icing image prediction. The novelty of MAEFN lies in its multi-module network structure, which enables the control of the training process. Such a structure can solve the problem that icing image generation networks are difficult to train directly, and effectively adjust the accuracy of icing images. MAEFN is divided into four modules, i.e. feature extraction, mapping relationship construction, image generation and image post-processing. Four neural networks are used to complete different steps and all models are finally fused into MAEFN model. MAEFN generates a 120×120 icing image through a 7-dimensional icing condition vector, and achieves 98.85% average pixel accuracy on supercritical wing. The standard deviation on the validation set is 0.0117, and the pixel accuracy of 680 out of 777 validation images exceeds 98%$. It takes 2000s to train 7000 icing images on V100 GPU. Furthermore, the efficiency of the fast icing algorithm is improved significantly by 25000 times compared with the traditional icing algorithm, which only cost 2×10-4s for a single case. Based on MAEFN, the exhaustive icing scaling method is realized, and similar icing results of four special ice shapes are found out from 1 million icing images in a few minutes. The proposed image generation method MAEFN can be further developed for highly complex icing images such as long-time icing results or other anti-icing tasks such as critical icing analysis.
In order to compare and analyze the similarities and differences between normal droplet icing shapes and supercooled large droplet icing shapes, SADRI carried out normal droplet and supercooled large droplet icing wind tunnel tests in the NRC−AIWT icing wind tunnel. Taking the typical glaze ice in normal droplet icing conditions as the reference, the freezing drizzle and freezing rain icing tests under the supercooled large droplet conditions were carried out. The test results show that compared with normal droplets, the ice horn height of supercooled large droplets decreases with the increase in droplet particle size, and even the ice horn characteristics are not obvious when the icing condition is freezing rain. At the same time, the range and height of rough element ice shape after the main ice horn of supercooled large droplets are significantly larger and higher than those of the normal droplets, while the difference in the rough element in different supercooled large droplet icing conditions is small.
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