一种非标参数的四角切圆超临界燃煤锅炉开发

本文介绍了一种新型的中小容量超临界燃煤锅炉布置型式,锅炉采用非标超临界参数,压力温度参数为25.4MPa.g/605℃/403℃,燃料为神木烟煤,采用四角切圆燃烧方式。

一种非标再热参数掺烧工艺废气煤粉锅炉开发

依托某化工园区热电联产项目配套1台150 MW背压式汽轮发电机组,为了处理化工园区生产过程中制造的工艺废气,自主开发设计了一种非标再热参数掺烧废气煤粉锅炉,锅炉为超高温亚临界参数,锅炉最大连续蒸发量为450 t/h,过热器压力温度参数为17.5 MPa/571℃,再热器蒸汽流量439.3 t/h,再热器压力温度参数为10.02 MPa/530℃,燃料为煤粉掺烧工艺废气,锅炉投运后,能够安全有效地掺烧工艺废气且各项指标运行正常,满足用户需求。

Non-parametric camera calibration method using single-axis rotational target

The ability to build an imaging process is crucial to vision measurement.The non-parametric imaging model describes an imaging process as a pixel cluster,in which each pixel is related to a spatial ray originated from an object point.However,a non-parametric model requires a sophisticated calculation process or high-cost devices to obtain a massive quantity of parameters.These disadvantages limit the application of camera models.Therefore,we propose a novel camera model calibration method based on a single-axis rotational target.The rotational vision target offers 3D control points with no need for detailed information of poses of the rotational target.Radial basis function(RBF)network is introduced to map 3D coordinates to 2D image coordinates.We subsequently derive the optimization formulization of imaging model parameters and compute the parameter from the given control points.The model is extended to adapt the stereo camera that is widely used in vision measurement.Experiments have been done to evaluate the performance of the proposed camera calibration method.The results show that the proposed method has superiority in accuracy and effectiveness in comparison with the traditional methods.

Flow of a Jeffery-Six Constant Fluid Between Coaxial Cylinders with Heat Transfer Analysis

In the present investigation we have discussed the flow of a Jeffrey-six constant incompressible fluid between two infinite coaxial cylinders in the presence of heat transfer analysis. The governing equations of Jeffrey-six constant fluid along with energy equation have been derived in cylindrical coordinates. The highly nonlinear equations are simplified with the help of non-dimensional parameters and then solved analytically with the help of homotopy analysis method （HAM） for two fundamental flows namely Couette and Generalized Couette flow. The effects of emerging parameters are discussed through graphs. The convergence of the HAM solution has been discussed by plotting h-curves.

Pattern characteristics in patients with primary liver cancer in different clinical stages

OBJECTIVE： To explore the characteristics of prima- ry liver cancer in terms of Traditional Chinese Medi-cine （TCM） by analyzing the variations of the patterns along with the clinical stages. METHODS： The patients who were hospitalized in the Changhai Hospital of Traditional Chinese Medi- cine dated from March 1999 to December 2008 were included in this retrospective study. The patients were grouped according to their cancer stag- es, and their patterns were judged and quantified according to the ＂Standard diagnosis and quantitative criteria of the common patterns in primary liv-er cancer＂ formulated by the Changhai Hospital of Traditional Chinese Medicine. Statistics methods included ANOVA and nonparametric test, among others.RESULTS： The data of the 398 newly diagnosed patients showed that Qi Stagnation, Blood Stasis, and Dampness patterns were more frequent than the other basic patterns with relatively high scores; patterns of Liver Qi Stagnation, Liver Blood Stasis, and Dampness Heat were more than the other complex patterns and scored relatively high. Scores of Dampness and Liver Qi Stagnation patterns varied among the groups at different stages and the differences were statistically significant （PDampeness= 0.002, PLiver Qi Stagnation ： 0.020）. The highest scores of Damp- ness pattern and Liver Qi Stagnation pattern corresponded with Stage Ⅲ b, and Stage Ⅲ a, respectively. Dampness pattern frequency was higher （P = 0.001） in the Stage Ⅲb group than in other groups.CONCLUSION： Pattern characteristics in patients with primary liver cancer of different clinical stages might manifest in the variations of the Dampness pattern along the process of the disease and the major pathogenic factor of primary liver cancer might be Dampness.

The seamless model for three-dimensional datum transformation

With extensive applications of space geodesy, three-dimensional datum transformation model has been necessarily used to transform the coordinates in the different coordinate systems.Its essence is to predict the coordinates of non-common points in the second coordinate system based on their coordinates in the first coordinate system and the coordinates of common points in two coordinate systems.Traditionally, the computation of seven transformation parameters and the transformation of noncommon points are individually implemented, in which the errors of coordinates are taken into account only in the second system although the coordinates in both two systems are inevitably contaminated by the random errors.Moreover, the coordinate errors of non-common points are disregarded when they are transformed using the solved transformation parameters.Here we propose the seamless (rigorous) datum transformation model to compute the transformation parameters and transform the non-common points integratively, considering the errors of all coordinates in both coordinate systems.As a result, a nonlinear coordinate transformation model is formulated.Based on the Gauss-Newton algorithm and the numerical characteristics of transformation parameters, two linear versions of the established nonlinear model are individually derived.Then the least-squares collocation (prediction) method is employed to trivially solve these linear models.Finally, the simulation experiment is carried out to demonstrate the performance and benefits of the presented method.The results show that the presented method can significantly improve the precision of the coordinate transformation, especially when the non-common points are strongly correlated with the common points used to compute the transformation parameters.