Optimization of Flow Matching Schemes for a Heavy Gas Turbine Burning Syngas

A heavy-duty gas turbine,designed for natural gas,was used to bum the syngas with two different calorific values.This study was mainly to optimize the flow matching scheme for the gas turbine.Two models of gas turbine burning syngas with different calorific values were established and the calculation models of different flow matching schemes were provided.The optimum scheme was obtained by evaluating thermal efficiency and work output under different operating conditions.The results showed that the highest unit efficiency was achieved by,without significant drop in work output,increasing the throat area of the turbine nozzle and reducing the initial temperature of the gas.On the premise of ensuring the safety of the gas turbine unit,increasing the pressure ratio of the compressor could further improve the unit efficiency and the work output.Simply adjusting the angle of the inlet guide vane fails to match the flow of compressor and turbine.The measures such as reducing inlet temperature of turbine or air bleed still need to be adopted,but the thermal efficiency dropped significantly in this process.

Fully automatic DOM generation method based on optical flow field dense image matching

Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification;this can consume much time and produce building facade contained results.To address this problem,a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper.Firstly,3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images,respectively.Then,the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points.Subsequently,the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline(LR-B)interpolation method with triangular mesh constraint for the point clouds void area,and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice.Finally,a seamline network is automatically searched using a disparity map optimization algorithm,and DOM is smartly mosaicked.The qualitative and quantitative experimental results on three datasets were produced and evaluated,which confirmed the feasibility of the proposed method,and the DOM accuracy can reach 1 Ground Sample Distance(GSD)level.The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.