This study concerns optimization of shapes, locations, and dimensions of internal cooling passages within a turbine vane under severe environments. The basic aim is to achieve a design that minimizes the average temperature and ensures the structural strength. Considering the prohibitive computational cost of 3D models, numerical optimization process is performed based on 2D cross-sectional models with available experimental temperature data as boundary conditions of thermomechanical analysis. To model the cooling channels, three kinds of shape configurations, i.e., circle, superellipse, and near-surface holes, are taken into account and compared. Optimization results of 2D models are obtained by using a globally convergent method of moving asymptotes (GCMMA). Furthermore, full conjugate heat transfer (CHT) analyses are made to obtain temperature distributions of 3D models extruded from 2D ones by means of shear stress transport (SST) k-ω turbulence model. It is shown that optimization of cooling passages effectively improves the thermomechanical performances of turbine vanes in comparison with those of initial C3X vane. The maximum temperature of optimized vane could be reduced up to 50 K without degrading mechanical strength.

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Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognition. In this paper, we propose a method of moving shadow detection based on edge information, which can effectively detect the cast shadow of a moving vehicle in a traffic scene. Having confirmed shadows existing in a figure, we execute the shadow removal algorithm proposed in this paper to segment the shadow from the foreground. The shadow eliminating algorithm removes the boundary of the cast shadow and preserves object edges firstly; secondly, it reconstructs coarse object shapes based on the edge information of objects; and finally, it extracts the cast shadow by subtracting the moving object from the change detection mask and performs further processing. The proposed method has been further tested on images taken under different shadow orientations, vehicle colors and vehicle sizes, and the results have revealed that shadows can be successfully eliminated and thus good video segmentation can be obtained.

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Despite a solid theoretical foundation and straightforward application to structural design problems, 3D topology optimization still suffers from a prohibitively high computational effort that hinders its widespread use in industrial design. One major contributor to this problem is the cost of solving the finite element equations during each iteration of the optimization loop. To alleviate this cost in large-scale topology optimization, the authors propose a projection-based reducedorder modeling approach using proper orthogonal decomposition for the construction of a reduced basis for the FE solution during the optimization, using a small number of previously obtained and stored solutions. This basis is then adaptively enriched and updated on-the-fly according to an error residual, until convergence of the main optimization loop. The method of moving asymptotes is used for the optimization. The techniques are validated using established 3D benchmark problems. The numerical results demonstrate the advantages and the improved performance of our proposed approach.